Mock wpApiSettings for development and render SettingsForm component

.env.example

@@ -1,6 +0,0 @@
-OPENAI_MODEL_NAME="gpt-4o"
-OPENAI_API_KEY="Enter your API key"
-OPENAI_API_BASE="https://api.openai.com/v1"
-CHAINLIT_USERNAME=admin
-CHAINLIT_USERNAME=admin
-CHAINLIT_AUTH_SECRET="chainlit create-secret to create"

.github/workflows/python-package.yml

@@ -1,40 +0,0 @@
-# This workflow will install Python dependencies, run tests and lint with a variety of Python versions
-# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python
-
-name: Python package
-
-on:
-  push:
-    branches: [ "develop" ]
-  pull_request:
-    branches: [ "develop" ]
-
-jobs:
-  build:
-
-    runs-on: ubuntu-latest
-    strategy:
-      fail-fast: false
-      matrix:
-        python-version: ["3.10", "3.11"]
-
-    steps:
-    - uses: actions/checkout@v3
-    - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v3
-      with:
-        python-version: ${{ matrix.python-version }}
-    - name: Install dependencies
-      run: |
-        python -m pip install --upgrade pip
-        python -m pip install flake8 pytest
-        if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
-    # - name: Lint with flake8
-    #   run: |
-    #     # stop the build if there are Python syntax errors or undefined names
-    #     flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
-    #     # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
-    #     flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
-    # - name: Test with pytest
-    #   run: |
-    #     pytest

.github/workflows/python-publish.yml

@@ -1,39 +0,0 @@
-# This workflow will upload a Python Package using Twine when a release is created
-# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python#publishing-to-package-registries
-
-# This workflow uses actions that are not certified by GitHub.
-# They are provided by a third-party and are governed by
-# separate terms of service, privacy policy, and support
-# documentation.
-
-name: Upload Python Package
-
-on:
-  release:
-    types: [published]
-
-permissions:
-  contents: read
-
-jobs:
-  deploy:
-
-    runs-on: ubuntu-latest
-
-    steps:
-    - uses: actions/checkout@v3
-    - name: Set up Python
-      uses: actions/setup-python@v3
-      with:
-        python-version: '3.x'
-    - name: Install dependencies
-      run: |
-        python -m pip install --upgrade pip
-        pip install build
-    - name: Build package
-      run: python -m build
-    - name: Publish package
-      uses: pypa/gh-action-pypi-publish@27b31702a0e7fc50959f5ad993c78deac1bdfc29
-      with:
-        user: __token__
-        password: ${{ secrets.PYPI_API_TOKEN }}

.gitignore

@@ -1,39 +1,23 @@
-.vscode
-crewai
-.cache
-__pycache__
-chroma.sqlite3
-test/
-dist/
-.env
-assets/*
-.idea
+# See https://help.github.com/articles/ignoring-files/ for more about ignoring files.
+
+# dependencies
+/node_modules
+/.pnp
+.pnp.js
+
+# testing
+/coverage
+
+# production
+/build
+
+# misc
 .DS_Store
-.pytest_cache
-praisonAI.egg-info
-flagged
-test.yaml
-db
-praisonai_prompt.txt
-watch.sh
-docs.sh
-other
+.env.local
+.env.development.local
+.env.test.local
+.env.production.local
 
-.chainlit
-.files
-
-site
-# Ignore Sphinx build directory
-docs/_build/
-docs/source/
-docs/build/
-
-# Ignore Sphinx cache
-docs/_build/doctrees/
-docs/_build/doctrees/*
-docs/_build/doctrees/.doctrees
-
-# Ignore Sphinx logs and local build files
-docs/_build/.buildinfo
-docs/_build/.buildinfo/*
-docs/_build/.buildinfo/.buildinfo
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*

.readthedocs.yml

@@ -1,23 +0,0 @@
-# .readthedocs.yaml
-# Read the Docs configuration file
-# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
-
-# Required
-version: 2
-
-# Set the OS, Python version and other tools you might need
-build:
-  os: ubuntu-22.04
-  tools:
-    python: "3.12"
-  jobs:
-    post_install:
-    - bash docs/generate_docs.sh
-
-mkdocs:
-  configuration: mkdocs.yml
-  fail_on_warning: true
-
-python:
-  install:
-    - requirements: docs/requirements.txt

Dockerfile

@@ -1,6 +0,0 @@
-FROM python:3.11-slim
-WORKDIR /app
-COPY . .
-RUN pip install flask praisonai==0.0.36 gunicorn markdown
-EXPOSE 8080
-CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]

LICENSE

@@ -1,20 +0,0 @@
-MIT License
-
-Permission is hereby granted, free of charge, to any person obtaining
-a copy of this software and associated documentation files (the
-"Software"), to deal in the Software without restriction, including
-without limitation the rights to use, copy, modify, merge, publish,
-distribute, sublicense, and/or sell copies of the Software, and to
-permit persons to whom the Software is furnished to do so, subject to
-the following conditions:
-
-The above copyright notice and this permission notice shall be
-included in all copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
-EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
-MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
-NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
-LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
-OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
-WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

README.md

@@ -1,307 +1,70 @@
-# Praison AI
+# Getting Started with Create React App
 
-Praison AI, leveraging both AutoGen and CrewAI or any other agent framework, represents a low-code, centralised framework designed to simplify the creation and orchestration of multi-agent systems for various LLM applications, emphasizing ease of use, customization, and human-agent interaction.
+This project was bootstrapped with [Create React App](https://github.com/facebook/create-react-app).
 
-## TL;DR
-```bash
-pip install praisonai
-export OPENAI_API_KEY="Enter your API key"
-praisonai --init create a movie script about dog in moon
-praisonai
-```
+## Available Scripts
 
-## Installation
+In the project directory, you can run:
 
-```bash
-pip install praisonai
-```
+### `npm start`
 
-## Initialise
+Runs the app in the development mode.\
+Open [http://localhost:3000](http://localhost:3000) to view it in your browser.
 
-```bash
-export OPENAI_API_KEY="Enter your API key"
-```
+The page will reload when you make changes.\
+You may also see any lint errors in the console.
 
-Generate your OPENAI API KEY from here: https://platform.openai.com/api-keys
+### `npm test`
 
-Note: You can use other providers such as Ollama, Mistral ... etc. Details are provided at the bottom.
-    
-```bash
-praisonai --init create a movie script about dog in moon
-```
-This will automatically create agents.yaml file in the current directory.
+Launches the test runner in the interactive watch mode.\
+See the section about [running tests](https://facebook.github.io/create-react-app/docs/running-tests) for more information.
 
-### To initialse with a specific agent framework (Optional):
+### `npm run build`
 
-```bash
-praisonai --framework autogen --init create movie script about cat in mars
-```
+Builds the app for production to the `build` folder.\
+It correctly bundles React in production mode and optimizes the build for the best performance.
 
-## Run
+The build is minified and the filenames include the hashes.\
+Your app is ready to be deployed!
 
-```bash
-praisonai
-```
+See the section about [deployment](https://facebook.github.io/create-react-app/docs/deployment) for more information.
 
-or 
-    
-```bash
-python -m praisonai
-```
+### `npm run eject`
 
-### Specify the agent framework (Optional):
+**Note: this is a one-way operation. Once you `eject`, you can't go back!**
 
-```bash
-praisonai --framework autogen
-```
+If you aren't satisfied with the build tool and configuration choices, you can `eject` at any time. This command will remove the single build dependency from your project.
 
-### Full Automatic Mode
+Instead, it will copy all the configuration files and the transitive dependencies (webpack, Babel, ESLint, etc) right into your project so you have full control over them. All of the commands except `eject` will still work, but they will point to the copied scripts so you can tweak them. At this point you're on your own.
 
-```bash
-praisonai --auto create a movie script about Dog in Moon
-```
+You don't have to ever use `eject`. The curated feature set is suitable for small and middle deployments, and you shouldn't feel obligated to use this feature. However we understand that this tool wouldn't be useful if you couldn't customize it when you are ready for it.
 
-## User Interface
+## Learn More
 
-```bash
-pip install -U "praisonai[ui]"
-export OPENAI_API_KEY="Enter your API key"
-chainlit create-secret
-export CHAINLIT_AUTH_SECRET=xxxxxxxx
-praisonai ui
-```
+You can learn more in the [Create React App documentation](https://facebook.github.io/create-react-app/docs/getting-started).
 
-or 
+To learn React, check out the [React documentation](https://reactjs.org/).
 
-```
-python -m praisonai ui
-```
+### Code Splitting
 
-## Create Custom Tools
+This section has moved here: [https://facebook.github.io/create-react-app/docs/code-splitting](https://facebook.github.io/create-react-app/docs/code-splitting)
 
-### TL;DR to Create a Custom Tool
+### Analyzing the Bundle Size
 
-```bash
-pip install praisonai duckduckgo-search
-export OPENAI_API_KEY="Enter your API key"
-praisonai --init research about the latest AI News and prepare a detailed report
-```
+This section has moved here: [https://facebook.github.io/create-react-app/docs/analyzing-the-bundle-size](https://facebook.github.io/create-react-app/docs/analyzing-the-bundle-size)
 
-- Add `- InternetSearchTool` in the agents.yaml file in the tools section. 
-- Create a file called tools.py and add this code [tools.py](./tools.py)
+### Making a Progressive Web App
 
-```bash
-praisonai
-```
+This section has moved here: [https://facebook.github.io/create-react-app/docs/making-a-progressive-web-app](https://facebook.github.io/create-react-app/docs/making-a-progressive-web-app)
 
-### Pre-requisite to Create a Custom Tool
-`agents.yaml` file should be present in the current directory. 
+### Advanced Configuration
 
-If it doesn't exist, create it by running the command `praisonai --init research about the latest AI News and prepare a detailed report`.
+This section has moved here: [https://facebook.github.io/create-react-app/docs/advanced-configuration](https://facebook.github.io/create-react-app/docs/advanced-configuration)
 
-### Step 1 to Create a Custom Tool
+### Deployment
 
-Create a file called tools.py in the same directory as the agents.yaml file.
+This section has moved here: [https://facebook.github.io/create-react-app/docs/deployment](https://facebook.github.io/create-react-app/docs/deployment)
 
-```python
-# example tools.py
-from duckduckgo_search import DDGS
-from praisonai_tools import BaseTool
-
-class InternetSearchTool(BaseTool):
-    name: str = "InternetSearchTool"
-    description: str = "Search Internet for relevant information based on a query or latest news"
-
-    def _run(self, query: str):
-        ddgs = DDGS()
-        results = ddgs.text(keywords=query, region='wt-wt', safesearch='moderate', max_results=5)
-        return results
-```
-
-### Step 2 to Create a Custom Tool
-
-Add the tool to the agents.yaml file as show below under the tools section `- InternetSearchTool`.
-
-```yaml
-framework: crewai
-topic: research about the latest AI News and prepare a detailed report
-roles:
-  research_analyst:
-    backstory: Experienced in gathering and analyzing data related to AI news trends.
-    goal: Analyze AI News trends
-    role: Research Analyst
-    tasks:
-      gather_data:
-        description: Conduct in-depth research on the latest AI News trends from reputable
-          sources.
-        expected_output: Comprehensive report on current AI News trends.
-    tools:
-    - InternetSearchTool
-```
-
-## Test
-    
-```bash
-python -m unittest tests.test 
-```
-
-## Agents Playbook 
-
-### Simple Playbook Example
-
-```yaml
-framework: crewai
-topic: Artificial Intelligence
-roles:
-  screenwriter:
-    backstory: 'Skilled in crafting scripts with engaging dialogue about {topic}.'
-    goal: Create scripts from concepts.
-    role: Screenwriter
-    tasks:
-      scriptwriting_task:
-        description: 'Develop scripts with compelling characters and dialogue about {topic}.'
-        expected_output: 'Complete script ready for production.'
-```
-
-### Detailed Playbook Example
-
-```yaml
-framework: crewai
-topic: Artificial Intelligence
-roles:
-  movie_concept_creator:
-    backstory: 'Creative thinker with a deep understanding of cinematic storytelling,
-      capable of using AI-generated storylines to create unique and compelling movie
-      ideas.'
-    goal: Generate engaging movie concepts using AI storylines
-    role: Movie Concept Creator
-    tasks:
-      movie_concept_development:
-        description: 'Develop movie concepts from AI-generated storylines, ensuring
-          they are engaging and have strong narrative arcs.'
-        expected_output: 'Well-structured movie concept document with character
-          bios, settings, and plot outlines.'
-  screenwriter:
-    backstory: 'Expert in writing engaging dialogue and script structure, able to
-      turn movie concepts into production-ready scripts.'
-    goal: Write compelling scripts based on movie concepts
-    role: Screenwriter
-    tasks:
-      scriptwriting_task:
-        description: 'Turn movie concepts into polished scripts with well-developed
-          characters, strong dialogue, and effective scene transitions.'
-        expected_output: 'Production-ready script with a beginning, middle, and
-          end, along with character development and engaging dialogues.'
-  editor:
-    backstory: 'Adept at identifying inconsistencies, improving language usage,
-      and maintaining the overall flow of the script.'
-    goal: Refine the scripts and ensure continuity of the movie storyline
-    role: Editor
-    tasks:
-      editing_task:
-        description: 'Review, edit, and refine the scripts to ensure they are cohesive
-          and follow a well-structured narrative.'
-        expected_output: 'A polished final draft of the script with no inconsistencies,
-          strong character development, and effective dialogue.'
-dependencies: []
-```
-
-## Include praisonai package in your project
-
-```python
-from praisonai import PraisonAI
-
-def basic(): # Basic Mode
-    praison_ai = PraisonAI(agent_file="agents.yaml")
-    praison_ai.main()
-    
-def advanced(): # Advanced Mode with options
-    praison_ai = PraisonAI(
-        agent_file="agents.yaml",
-        framework="autogen",
-    )
-    praison_ai.main()
-    
-def auto(): # Full Automatic Mode
-    praison_ai = PraisonAI(
-        auto="Create a movie script about car in mars",
-        framework="autogen"
-    )
-    print(praison_ai.framework)
-    praison_ai.main()
-
-if __name__ == "__main__":
-    basic()
-    advanced()
-    auto()
-```
-
-## Include CrewAI Tools
-
-```
-pip install "praisonai[crewai-tools]"
-```
-
-## Deploy 
-    
-```bash
-gcloud init
-gcloud services enable run.googleapis.com
-gcloud services enable containerregistry.googleapis.com
-gcloud services enable cloudbuild.googleapis.com
-
-export OPENAI_MODEL_NAME="gpt-4o"
-export OPENAI_API_KEY="Enter your API key"
-export OPENAI_API_BASE="https://api.openai.com/v1"
-
-yes | gcloud auth configure-docker us-central1-docker.pkg.dev 
-gcloud artifacts repositories create praisonai-repository --repository-format=docker --location=us-central1
-
-PROJECT_ID=$(gcloud config get-value project)
-TAG="latest"
-docker build --platform linux/amd64 -t gcr.io/${PROJECT_ID}/praisonai-app:${TAG} .
-docker tag gcr.io/${PROJECT_ID}/praisonai-app:${TAG} us-central1-docker.pkg.dev/${PROJECT_ID}/praisonai-repository/praisonai-app:${TAG}
-docker push us-central1-docker.pkg.dev/${PROJECT_ID}/praisonai-repository/praisonai-app:${TAG}
-
-gcloud run deploy praisonai-service \
-    --image us-central1-docker.pkg.dev/${PROJECT_ID}/praisonai-repository/praisonai-app:${TAG} \
-    --platform managed \
-    --region us-central1 \
-    --allow-unauthenticated \
-    --set-env-vars OPENAI_MODEL_NAME=${OPENAI_MODEL_NAME},OPENAI_API_KEY=${OPENAI_API_KEY},OPENAI_API_BASE=${OPENAI_API_BASE}
-```
-
-## Other Models
-
-```bash
-# Ollama
-OPENAI_API_BASE='http://localhost:11434/v1'
-OPENAI_MODEL_NAME='mistral'
-OPENAI_API_KEY='NA'
-
-# FastChat
-OPENAI_API_BASE="http://localhost:8001/v1"
-OPENAI_MODEL_NAME='oh-2.5m7b-q51'
-OPENAI_API_KEY=NA
-
-# LM Studio
-OPENAI_API_BASE="http://localhost:1234/v1"
-OPENAI_MODEL_NAME=NA
-OPENAI_API_KEY=NA
-
-# Mistral API
-OPENAI_API_BASE=https://api.mistral.ai/v1
-OPENAI_MODEL_NAME="mistral-small"
-OPENAI_API_KEY=your-mistral-api-key
-```
-
-## Contributing
-
-- Fork on GitHub: Use the "Fork" button on the repository page.
-- Clone your fork: `git clone https://github.com/yourusername/praisonAI.git`
-- Create a branch: `git checkout -b new-feature`
-- Make changes and commit: `git commit -am "Add some feature"`
-- Push to your fork: `git push origin new-feature`
-- Submit a pull request via GitHub's web interface.
-- Await feedback from project maintainers.
+### `npm run build` fails to minify
 
+This section has moved here: [https://facebook.github.io/create-react-app/docs/troubleshooting#npm-run-build-fails-to-minify](https://facebook.github.io/create-react-app/docs/troubleshooting#npm-run-build-fails-to-minify)

agents-scrapewebitetool.yaml

@@ -1,42 +0,0 @@
-framework: crewai
-topic: create a movie about cat in mars based on https://blog.celtx.com/how-to-write-a-good-story/
-roles:
-  story_developer:
-    backstory: Experienced in crafting compelling narratives for a diverse audience.
-    goal: Develop an engaging storyline for a movie about cats in Mars
-    role: Story Developer
-    tasks:
-      storyline_creation:
-        description: Create a captivating storyline for a movie about cats in Mars
-          inspired by the blog post at https://blog.celtx.com/how-to-write-a-good-story/
-        expected_output: Detailed storyline with key plot points, character motivations,
-          and setting descriptions.
-    tools:
-    - 'ScrapeWebsiteTool'
-  screenwriter:
-    backstory: Proficient in translating complex narratives into engaging screenplay
-      format.
-    goal: Transform the storyline into a script
-    role: Screenwriter
-    tasks:
-      script_creation:
-        description: Transform the developed storyline into a script for the movie
-          about cats in Mars.
-        expected_output: Complete script with dialogues, scenes, and stage directions
-          ready for production.
-    tools:
-    - ''
-  director:
-    backstory: Experienced in visual storytelling and bringing scripts to life on
-      screen.
-    goal: Oversee the production of the movie
-    role: Director
-    tasks:
-      movie_production:
-        description: Take the script and lead the team to produce the movie 'Cats
-          in Mars' based on the provided content and script.
-        expected_output: Completed movie ready for distribution with compelling visuals
-          and engaging storytelling.
-    tools:
-    - ''
-dependencies: []

agents.yaml

@@ -1,39 +0,0 @@
-framework: autogen
-topic: research about the causes of lung disease
-roles:
-  research_analyst:
-    backstory: Experienced in analyzing scientific data related to respiratory health.
-    goal: Analyze data on lung diseases
-    role: Research Analyst
-    tasks:
-      data_analysis:
-        description: Gather and analyze data on the causes and risk factors of lung
-          diseases.
-        expected_output: Report detailing key findings on lung disease causes.
-    tools:
-    - 'InternetSearchTool'
-  medical_writer:
-    backstory: Skilled in translating complex medical information into accessible
-      content.
-    goal: Compile comprehensive content on lung disease causes
-    role: Medical Writer
-    tasks:
-      content_creation:
-        description: Create detailed content summarizing the research findings on
-          lung disease causes.
-        expected_output: Document outlining various causes and risk factors of lung
-          diseases.
-    tools:
-    - ''
-  editor:
-    backstory: Proficient in editing medical content for accuracy and clarity.
-    goal: Review and refine content on lung disease causes
-    role: Editor
-    tasks:
-      content_review:
-        description: Edit and refine the compiled content on lung disease causes for
-          accuracy and coherence.
-        expected_output: Finalized document on lung disease causes ready for dissemination.
-    tools:
-    - ''
-dependencies: []

agents_playbook/create-movie-agents.yaml

@@ -1,42 +0,0 @@
-framework: crewai
-topic: create a movie about cat in mars based on https://www.grammarly.com/blog/how-to-write-a-story/
-roles:
-  story_developer:
-    backstory: Experienced in crafting compelling narratives for a diverse audience.
-    goal: Develop an engaging storyline for a movie about cats in Mars
-    role: Story Developer
-    tasks:
-      storyline_creation:
-        description: Create a captivating storyline for a movie about cats in Mars
-          inspired by the blog post at https://www.grammarly.com/blog/how-to-write-a-story/
-        expected_output: Detailed storyline with key plot points, character motivations,
-          and setting descriptions.
-    tools:
-    - 'ScrapeWebsiteTool'
-  screenwriter:
-    backstory: Proficient in translating complex narratives into engaging screenplay
-      format.
-    goal: Transform the storyline into a script
-    role: Screenwriter
-    tasks:
-      script_creation:
-        description: Transform the developed storyline into a script for the movie
-          about cats in Mars.
-        expected_output: Complete script with dialogues, scenes, and stage directions
-          ready for production.
-    tools:
-    - ''
-  director:
-    backstory: Experienced in visual storytelling and bringing scripts to life on
-      screen.
-    goal: Oversee the production of the movie
-    role: Director
-    tasks:
-      movie_production:
-        description: Take the script and lead the team to produce the movie 'Cats
-          in Mars' based on the provided content and script.
-        expected_output: Completed movie ready for distribution with compelling visuals
-          and engaging storytelling.
-    tools:
-    - ''
-dependencies: []

agents_playbook/finance-research.yaml

@@ -1,43 +0,0 @@
-framework: crewai
-topic: research about nvidia growth
-roles:
-  data_collector:
-    backstory: An experienced researcher with the ability to efficiently collect and
-      organize vast amounts of data.
-    goal: Gather information on Nvidia's growth
-    role: Data Collector
-    tasks:
-      data_collection_task:
-        description: Collect data on Nvidia's growth from various sources such as
-          financial reports, news articles, and company announcements.
-        expected_output: A comprehensive document detailing data points on Nvidia's
-          growth over the years.
-    tools:
-    - 'YouSearchTool'
-  data_analyst:
-    backstory: Specializes in extracting insights from large datasets, proficient
-      in quantitative and qualitative analysis.
-    goal: Analyze the collected data to identify trends and patterns
-    role: Data Analyst
-    tasks:
-      data_analysis_task:
-        description: Analyze the collected data to identify key trends and patterns
-          in Nvidia's growth.
-        expected_output: An analytical report summarizing trends, patterns, and key
-          growth metrics of Nvidia.
-    tools:
-    - ''
-  report_preparer:
-    backstory: Experienced in creating detailed reports and presentations, turning
-      analytical data into actionable insights.
-    goal: Generate a final report on Nvidia's growth
-    role: Report Preparer
-    tasks:
-      report_preparation_task:
-        description: Create a detailed report based on the analysis, highlighting
-          Nvidia's growth trajectory.
-        expected_output: A polished, comprehensive report summarizing Nvidia's growth
-          with visual aids and key insights.
-    tools:
-    - ''
-dependencies: []

agents_playbook/internet-research-agents.yaml

@@ -1,26 +0,0 @@
-framework: crewai
-topic: research about the latest AI News and prepare a detailed report
-roles:
-  research_analyst:
-    backstory: Experienced in gathering and analyzing data related to AI news trends.
-    goal: Analyze AI News trends
-    role: Research Analyst
-    tasks:
-      gather_data:
-        description: Conduct in-depth research on the latest AI News trends from reputable
-          sources.
-        expected_output: Comprehensive report on current AI News trends.
-    tools:
-    - 'InternetSearchTool'
-  content_creator:
-    backstory: Skilled in writing informative and engaging content about AI News.
-    goal: Create detailed reports on AI News
-    role: Content Creator
-    tasks:
-      compile_report:
-        description: Compile all gathered data into a detailed report with analysis
-          and insights.
-        expected_output: Well-structured report on the latest AI News with key insights.
-    tools:
-    - ''
-dependencies: []

agents_playbook/medical-research-pubmed-agents.yaml

@@ -1,39 +0,0 @@
-framework: crewai
-topic: research about the causes of lung disease
-roles:
-  research_analyst:
-    backstory: Experienced in analyzing scientific data related to respiratory health.
-    goal: Analyze data on lung diseases
-    role: Research Analyst
-    tasks:
-      data_analysis:
-        description: Gather and analyze data on the causes and risk factors of lung
-          diseases.
-        expected_output: Report detailing key findings on lung disease causes.
-    tools:
-    - 'PubmedQueryRun'
-  medical_writer:
-    backstory: Skilled in translating complex medical information into accessible
-      content.
-    goal: Compile comprehensive content on lung disease causes
-    role: Medical Writer
-    tasks:
-      content_creation:
-        description: Create detailed content summarizing the research findings on
-          lung disease causes.
-        expected_output: Document outlining various causes and risk factors of lung
-          diseases.
-    tools:
-    - ''
-  editor:
-    backstory: Proficient in editing medical content for accuracy and clarity.
-    goal: Review and refine content on lung disease causes
-    role: Editor
-    tasks:
-      content_review:
-        description: Edit and refine the compiled content on lung disease causes for
-          accuracy and coherence.
-        expected_output: Finalized document on lung disease causes ready for dissemination.
-    tools:
-    - ''
-dependencies: []

api.py

@@ -1,18 +0,0 @@
-from flask import Flask
-from praisonai import PraisonAI
-import markdown
-
-app = Flask(__name__)
-
-def basic():
-    praison_ai = PraisonAI(agent_file="agents.yaml")
-    return praison_ai.main()
-
-@app.route('/')
-def home():
-    output = basic()
-    html_output = markdown.markdown(output)
-    return f'<html><body>{html_output}</body></html>'
-
-if __name__ == "__main__":
-    app.run(debug=True)

chainlit.md

@@ -1 +0,0 @@
-# Welcome to Praison AI! 🚀🤖

docs/CNAME

@@ -1 +0,0 @@
-docs.praison.ai

docs/Makefile

@@ -1,20 +0,0 @@
-# Minimal makefile for Sphinx documentation
-#
-
-# You can set these variables from the command line, and also
-# from the environment for the first two.
-SPHINXOPTS    ?=
-SPHINXBUILD   ?= sphinx-build
-SOURCEDIR     = .
-BUILDDIR      = _build
-
-# Put it first so that "make" without argument is like "make help".
-help:
-	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
-
-.PHONY: help Makefile
-
-# Catch-all target: route all unknown targets to Sphinx using the new
-# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
-%: Makefile
-	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

docs/agents_playbook.md

@@ -1,60 +0,0 @@
-# Agents Playbook 
-
-## Simple Playbook Example
-
-```yaml
-framework: crewai
-topic: Artificial Intelligence
-roles:
-  screenwriter:
-    backstory: 'Skilled in crafting scripts with engaging dialogue about {topic}.'
-    goal: Create scripts from concepts.
-    role: Screenwriter
-    tasks:
-      scriptwriting_task:
-        description: 'Develop scripts with compelling characters and dialogue about {topic}.'
-        expected_output: 'Complete script ready for production.'
-```
-
-## Detailed Playbook Example
-
-```yaml
-framework: crewai
-topic: Artificial Intelligence
-roles:
-  movie_concept_creator:
-    backstory: 'Creative thinker with a deep understanding of cinematic storytelling,
-      capable of using AI-generated storylines to create unique and compelling movie
-      ideas.'
-    goal: Generate engaging movie concepts using AI storylines
-    role: Movie Concept Creator
-    tasks:
-      movie_concept_development:
-        description: 'Develop movie concepts from AI-generated storylines, ensuring
-          they are engaging and have strong narrative arcs.'
-        expected_output: 'Well-structured movie concept document with character
-          bios, settings, and plot outlines.'
-  screenwriter:
-    backstory: 'Expert in writing engaging dialogue and script structure, able to
-      turn movie concepts into production-ready scripts.'
-    goal: Write compelling scripts based on movie concepts
-    role: Screenwriter
-    tasks:
-      scriptwriting_task:
-        description: 'Turn movie concepts into polished scripts with well-developed
-          characters, strong dialogue, and effective scene transitions.'
-        expected_output: 'Production-ready script with a beginning, middle, and
-          end, along with character development and engaging dialogues.'
-  editor:
-    backstory: 'Adept at identifying inconsistencies, improving language usage,
-      and maintaining the overall flow of the script.'
-    goal: Refine the scripts and ensure continuity of the movie storyline
-    role: Editor
-    tasks:
-      editing_task:
-        description: 'Review, edit, and refine the scripts to ensure they are cohesive
-          and follow a well-structured narrative.'
-        expected_output: 'A polished final draft of the script with no inconsistencies,
-          strong character development, and effective dialogue.'
-dependencies: []
-```

docs/api.md

@@ -1,6 +0,0 @@
-# API Reference
-::: praisonai
-::: praisonai.auto
-::: praisonai.agents_generator
-::: praisonai.cli
-::: praisonai.deploy

docs/api/praisonai/agents_generator.html

@@ -1,485 +0,0 @@
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-hljs.highlightAll();
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-</head>
-<body>
-<main>
-<article id="content">
-<header>
-<h1 class="title">Module <code>praisonai.agents_generator</code></h1>
-</header>
-<section id="section-intro">
-</section>
-<section>
-</section>
-<section>
-</section>
-<section>
-<h2 class="section-title" id="header-functions">Functions</h2>
-<dl>
-<dt id="praisonai.agents_generator.disable_crewai_telemetry"><code class="name flex">
-<span>def <span class="ident">disable_crewai_telemetry</span></span>(<span>)</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.agents_generator.noop"><code class="name flex">
-<span>def <span class="ident">noop</span></span>(<span>*args, **kwargs)</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-</dl>
-</section>
-<section>
-<h2 class="section-title" id="header-classes">Classes</h2>
-<dl>
-<dt id="praisonai.agents_generator.AgentsGenerator"><code class="flex name class">
-<span>class <span class="ident">AgentsGenerator</span></span>
-<span>(</span><span>agent_file, framework, config_list, log_level=None, agent_callback=None, task_callback=None)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Initialize the AgentsGenerator object.</p>
-<h2 id="parameters">Parameters</h2>
-<p>agent_file (str): The path to the agent file.
-framework (str): The framework to be used for the agents.
-config_list (list): A list of configurations for the agents.
-log_level (int, optional): The logging level to use. Defaults to logging.INFO.
-agent_callback (callable, optional): A callback function to be executed after each agent step.
-task_callback (callable, optional): A callback function to be executed after each tool run.</p>
-<h2 id="attributes">Attributes</h2>
-<dl>
-<dt><strong><code>agent_file</code></strong> :&ensp;<code>str</code></dt>
-<dd>The path to the agent file.</dd>
-<dt><strong><code>framework</code></strong> :&ensp;<code>str</code></dt>
-<dd>The framework to be used for the agents.</dd>
-<dt><strong><code>config_list</code></strong> :&ensp;<code>list</code></dt>
-<dd>A list of configurations for the agents.</dd>
-<dt><strong><code>log_level</code></strong> :&ensp;<code>int</code></dt>
-<dd>The logging level to use.</dd>
-<dt><strong><code>agent_callback</code></strong> :&ensp;<code>callable</code>, optional</dt>
-<dd>A callback function to be executed after each agent step.</dd>
-<dt><strong><code>task_callback</code></strong> :&ensp;<code>callable</code>, optional</dt>
-<dd>A callback function to be executed after each tool run.</dd>
-</dl></div>
-<details class="source">
-<summary>
-<span>Expand source code</span>
-</summary>
-<pre><code class="python">class AgentsGenerator:
-    def __init__(self, agent_file, framework, config_list, log_level=None, agent_callback=None, task_callback=None):
-        &#34;&#34;&#34;
-        Initialize the AgentsGenerator object.
-
-        Parameters:
-            agent_file (str): The path to the agent file.
-            framework (str): The framework to be used for the agents.
-            config_list (list): A list of configurations for the agents.
-            log_level (int, optional): The logging level to use. Defaults to logging.INFO.
-            agent_callback (callable, optional): A callback function to be executed after each agent step.
-            task_callback (callable, optional): A callback function to be executed after each tool run.
-
-        Attributes:
-            agent_file (str): The path to the agent file.
-            framework (str): The framework to be used for the agents.
-            config_list (list): A list of configurations for the agents.
-            log_level (int): The logging level to use.
-            agent_callback (callable, optional): A callback function to be executed after each agent step.
-            task_callback (callable, optional): A callback function to be executed after each tool run.
-        &#34;&#34;&#34;
-        self.agent_file = agent_file
-        self.framework = framework
-        self.config_list = config_list
-        self.log_level = log_level
-        self.agent_callback = agent_callback
-        self.task_callback = task_callback
-        self.log_level = log_level or logging.getLogger().getEffectiveLevel()
-        if self.log_level == logging.NOTSET:
-            self.log_level = os.environ.get(&#39;LOGLEVEL&#39;, &#39;INFO&#39;).upper()
-        
-        logging.basicConfig(level=self.log_level, format=&#39;%(asctime)s - %(levelname)s - %(message)s&#39;)
-        self.logger = logging.getLogger(__name__)
-        self.logger.setLevel(self.log_level)
-        
-    def is_function_or_decorated(self, obj):
-        &#34;&#34;&#34;
-        Checks if the given object is a function or has a __call__ method.
-
-        Parameters:
-            obj (object): The object to be checked.
-
-        Returns:
-            bool: True if the object is a function or has a __call__ method, False otherwise.
-        &#34;&#34;&#34;
-        return inspect.isfunction(obj) or hasattr(obj, &#39;__call__&#39;)
-
-    def load_tools_from_module(self, module_path):
-        &#34;&#34;&#34;
-        Loads tools from a specified module path.
-
-        Parameters:
-            module_path (str): The path to the module containing the tools.
-
-        Returns:
-            dict: A dictionary containing the names of the tools as keys and the corresponding functions or objects as values.
-
-        Raises:
-            FileNotFoundError: If the specified module path does not exist.
-        &#34;&#34;&#34;
-        spec = importlib.util.spec_from_file_location(&#34;tools_module&#34;, module_path)
-        module = importlib.util.module_from_spec(spec)
-        spec.loader.exec_module(module)
-        return {name: obj for name, obj in inspect.getmembers(module, self.is_function_or_decorated)}
-    
-    def load_tools_from_module_class(self, module_path):
-        &#34;&#34;&#34;
-        Loads tools from a specified module path containing classes that inherit from BaseTool or are part of langchain_community.tools package.
-
-        Parameters:
-            module_path (str): The path to the module containing the tools.
-
-        Returns:
-            dict: A dictionary containing the names of the tools as keys and the corresponding initialized instances of the classes as values.
-
-        Raises:
-            FileNotFoundError: If the specified module path does not exist.
-        &#34;&#34;&#34;
-        spec = importlib.util.spec_from_file_location(&#34;tools_module&#34;, module_path)
-        module = importlib.util.module_from_spec(spec)
-        spec.loader.exec_module(module)
-        return {name: obj() for name, obj in inspect.getmembers(module, lambda x: inspect.isclass(x) and (x.__module__.startswith(&#39;langchain_community.tools&#39;) or issubclass(x, BaseTool)) and x is not BaseTool)}
-
-    def load_tools_from_package(self, package_path):
-        &#34;&#34;&#34;
-        Loads tools from a specified package path containing modules with functions or classes.
-
-        Parameters:
-            package_path (str): The path to the package containing the tools.
-
-        Returns:
-            dict: A dictionary containing the names of the tools as keys and the corresponding initialized instances of the classes as values.
-
-        Raises:
-            FileNotFoundError: If the specified package path does not exist.
-
-        This function iterates through all the .py files in the specified package path, excluding those that start with &#34;__&#34;. For each file, it imports the corresponding module and checks if it contains any functions or classes that can be loaded as tools. The function then returns a dictionary containing the names of the tools as keys and the corresponding initialized instances of the classes as values.
-        &#34;&#34;&#34;
-        tools_dict = {}
-        for module_file in os.listdir(package_path):
-            if module_file.endswith(&#39;.py&#39;) and not module_file.startswith(&#39;__&#39;):
-                module_name = f&#34;{package_path.name}.{module_file[:-3]}&#34;  # Remove .py for import
-                module = importlib.import_module(module_name)
-                for name, obj in inspect.getmembers(module, self.is_function_or_decorated):
-                    tools_dict[name] = obj
-        return tools_dict
-
-    def generate_crew_and_kickoff(self):
-        &#34;&#34;&#34;
-        Generates a crew of agents and initiates tasks based on the provided configuration.
-
-        Parameters:
-            agent_file (str): The path to the agent file.
-            framework (str): The framework to be used for the agents.
-            config_list (list): A list of configurations for the agents.
-
-        Returns:
-            str: The output of the tasks performed by the crew of agents.
-
-        Raises:
-            FileNotFoundError: If the specified agent file does not exist.
-
-        This function first loads the agent configuration from the specified file. It then initializes the tools required for the agents based on the specified framework. If the specified framework is &#34;autogen&#34;, it loads the LLM configuration dynamically and creates an AssistantAgent for each role in the configuration. It then adds tools to the agents if specified in the configuration. Finally, it prepares tasks for the agents based on the configuration and initiates the tasks using the crew of agents. If the specified framework is not &#34;autogen&#34;, it creates a crew of agents and initiates tasks based on the configuration.
-        &#34;&#34;&#34;
-        if self.agent_file == &#39;/app/api:app&#39; or self.agent_file == &#39;api:app&#39;:
-            self.agent_file = &#39;agents.yaml&#39;
-        try:
-            with open(self.agent_file, &#39;r&#39;) as f:
-                config = yaml.safe_load(f)
-        except FileNotFoundError:
-            print(f&#34;File not found: {self.agent_file}&#34;)
-            return
-
-        topic = config[&#39;topic&#39;]
-        tools_dict = {
-            &#39;CodeDocsSearchTool&#39;: CodeDocsSearchTool(),
-            &#39;CSVSearchTool&#39;: CSVSearchTool(),
-            &#39;DirectorySearchTool&#39;: DirectorySearchTool(),
-            &#39;DOCXSearchTool&#39;: DOCXSearchTool(),
-            &#39;DirectoryReadTool&#39;: DirectoryReadTool(),
-            &#39;FileReadTool&#39;: FileReadTool(),
-            # &#39;GithubSearchTool&#39;: GithubSearchTool(),
-            # &#39;SeperDevTool&#39;: SeperDevTool(),
-            &#39;TXTSearchTool&#39;: TXTSearchTool(),
-            &#39;JSONSearchTool&#39;: JSONSearchTool(),
-            &#39;MDXSearchTool&#39;: MDXSearchTool(),
-            &#39;PDFSearchTool&#39;: PDFSearchTool(),
-            # &#39;PGSearchTool&#39;: PGSearchTool(),
-            &#39;RagTool&#39;: RagTool(),
-            &#39;ScrapeElementFromWebsiteTool&#39;: ScrapeElementFromWebsiteTool(),
-            &#39;ScrapeWebsiteTool&#39;: ScrapeWebsiteTool(),
-            &#39;WebsiteSearchTool&#39;: WebsiteSearchTool(),
-            &#39;XMLSearchTool&#39;: XMLSearchTool(),
-            &#39;YoutubeChannelSearchTool&#39;: YoutubeChannelSearchTool(),
-            &#39;YoutubeVideoSearchTool&#39;: YoutubeVideoSearchTool(),
-        }
-        root_directory = os.getcwd()
-        tools_py_path = os.path.join(root_directory, &#39;tools.py&#39;)
-        tools_dir_path = Path(root_directory) / &#39;tools&#39;
-        
-        if os.path.isfile(tools_py_path):
-            tools_dict.update(self.load_tools_from_module_class(tools_py_path))
-            self.logger.debug(&#34;tools.py exists in the root directory. Loading tools.py and skipping tools folder.&#34;)
-        elif tools_dir_path.is_dir():
-            tools_dict.update(self.load_tools_from_module_class(tools_dir_path))
-            self.logger.debug(&#34;tools folder exists in the root directory&#34;)
-        
-        framework = self.framework or config.get(&#39;framework&#39;)
-
-        agents = {}
-        tasks = []
-        if framework == &#34;autogen&#34;:
-            # Load the LLM configuration dynamically
-            # print(self.config_list)
-            llm_config = {&#34;config_list&#34;: self.config_list}
-            
-            # Assuming the user proxy agent is set up as per your requirements
-            user_proxy = autogen.UserProxyAgent(
-                name=&#34;User&#34;,
-                human_input_mode=&#34;NEVER&#34;,
-                is_termination_msg=lambda x: (x.get(&#34;content&#34;) or &#34;&#34;).rstrip().rstrip(&#34;.&#34;).lower().endswith(&#34;terminate&#34;) or &#34;TERMINATE&#34; in (x.get(&#34;content&#34;) or &#34;&#34;),
-                code_execution_config={
-                    &#34;work_dir&#34;: &#34;coding&#34;,
-                    &#34;use_docker&#34;: False,
-                },
-                # additional setup for the user proxy agent
-            )
-            
-            for role, details in config[&#39;roles&#39;].items():
-                agent_name = details[&#39;role&#39;].format(topic=topic).replace(&#34;{topic}&#34;, topic)
-                agent_goal = details[&#39;goal&#39;].format(topic=topic)
-                # Creating an AssistantAgent for each role dynamically
-                agents[role] = autogen.AssistantAgent(
-                    name=agent_name,
-                    llm_config=llm_config,
-                    system_message=details[&#39;backstory&#39;].format(topic=topic)+&#34;. Must Reply \&#34;TERMINATE\&#34; in the end when everything is done.&#34;,
-                )
-                for tool in details.get(&#39;tools&#39;, []):
-                    if tool in tools_dict:
-                        try:
-                            tool_class = globals()[f&#39;autogen_{type(tools_dict[tool]).__name__}&#39;]
-                            print(f&#34;Found {tool_class.__name__} for {tool}&#34;)
-                        except KeyError:
-                            print(f&#34;Warning: autogen_{type(tools_dict[tool]).__name__} function not found. Skipping this tool.&#34;)
-                            continue
-                        tool_class(agents[role], user_proxy)
-
-                # Preparing tasks for initiate_chats
-                for task_name, task_details in details.get(&#39;tasks&#39;, {}).items():
-                    description_filled = task_details[&#39;description&#39;].format(topic=topic)
-                    expected_output_filled = task_details[&#39;expected_output&#39;].format(topic=topic)
-                    
-                    chat_task = {
-                        &#34;recipient&#34;: agents[role],
-                        &#34;message&#34;: description_filled,
-                        &#34;summary_method&#34;: &#34;last_msg&#34;, 
-                        # Additional fields like carryover can be added based on dependencies
-                    }
-                    tasks.append(chat_task)
-
-            response = user_proxy.initiate_chats(tasks)
-            result = &#34;### Output ###\n&#34;+response[-1].summary if hasattr(response[-1], &#39;summary&#39;) else &#34;&#34;
-        else:
-            for role, details in config[&#39;roles&#39;].items():
-                role_filled = details[&#39;role&#39;].format(topic=topic)
-                goal_filled = details[&#39;goal&#39;].format(topic=topic)
-                backstory_filled = details[&#39;backstory&#39;].format(topic=topic)
-                
-                # Adding tools to the agent if exists
-                agent_tools = [tools_dict[tool] for tool in details.get(&#39;tools&#39;, []) if tool in tools_dict]
-                agent = Agent(role=role_filled, goal=goal_filled, backstory=backstory_filled, tools=agent_tools, allow_delegation=False)
-                
-                # Set agent callback if provided
-                if self.agent_callback:
-                    agent.step_callback = self.agent_callback
-
-                agents[role] = agent
-
-                for task_name, task_details in details.get(&#39;tasks&#39;, {}).items():
-                    description_filled = task_details[&#39;description&#39;].format(topic=topic)
-                    expected_output_filled = task_details[&#39;expected_output&#39;].format(topic=topic)
-
-                    task = Task(description=description_filled, expected_output=expected_output_filled, agent=agent)
-                    
-                    # Set tool callback if provided
-                    if self.task_callback:
-                        task.callback = self.task_callback
-
-                    tasks.append(task)
-            if agentops:
-                agentops.init()
-            crew = Crew(
-                agents=list(agents.values()),
-                tasks=tasks,
-                verbose=2
-            )
-            if agentops:
-                agentops.end_session(&#34;Success&#34;)
-            
-            self.logger.debug(&#34;Final Crew Configuration:&#34;)
-            self.logger.debug(f&#34;Agents: {crew.agents}&#34;)
-            self.logger.debug(f&#34;Tasks: {crew.tasks}&#34;)
-
-            response = crew.kickoff()
-            result = f&#34;### Task Output ###\n{response}&#34;
-        return result</code></pre>
-</details>
-<h3>Methods</h3>
-<dl>
-<dt id="praisonai.agents_generator.AgentsGenerator.generate_crew_and_kickoff"><code class="name flex">
-<span>def <span class="ident">generate_crew_and_kickoff</span></span>(<span>self)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Generates a crew of agents and initiates tasks based on the provided configuration.</p>
-<h2 id="parameters">Parameters</h2>
-<p>agent_file (str): The path to the agent file.
-framework (str): The framework to be used for the agents.
-config_list (list): A list of configurations for the agents.</p>
-<h2 id="returns">Returns</h2>
-<dl>
-<dt><code>str</code></dt>
-<dd>The output of the tasks performed by the crew of agents.</dd>
-</dl>
-<h2 id="raises">Raises</h2>
-<dl>
-<dt><code>FileNotFoundError</code></dt>
-<dd>If the specified agent file does not exist.</dd>
-</dl>
-<p>This function first loads the agent configuration from the specified file. It then initializes the tools required for the agents based on the specified framework. If the specified framework is "autogen", it loads the LLM configuration dynamically and creates an AssistantAgent for each role in the configuration. It then adds tools to the agents if specified in the configuration. Finally, it prepares tasks for the agents based on the configuration and initiates the tasks using the crew of agents. If the specified framework is not "autogen", it creates a crew of agents and initiates tasks based on the configuration.</p></div>
-</dd>
-<dt id="praisonai.agents_generator.AgentsGenerator.is_function_or_decorated"><code class="name flex">
-<span>def <span class="ident">is_function_or_decorated</span></span>(<span>self, obj)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Checks if the given object is a function or has a <strong>call</strong> method.</p>
-<h2 id="parameters">Parameters</h2>
-<p>obj (object): The object to be checked.</p>
-<h2 id="returns">Returns</h2>
-<dl>
-<dt><code>bool</code></dt>
-<dd>True if the object is a function or has a <strong>call</strong> method, False otherwise.</dd>
-</dl></div>
-</dd>
-<dt id="praisonai.agents_generator.AgentsGenerator.load_tools_from_module"><code class="name flex">
-<span>def <span class="ident">load_tools_from_module</span></span>(<span>self, module_path)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Loads tools from a specified module path.</p>
-<h2 id="parameters">Parameters</h2>
-<p>module_path (str): The path to the module containing the tools.</p>
-<h2 id="returns">Returns</h2>
-<dl>
-<dt><code>dict</code></dt>
-<dd>A dictionary containing the names of the tools as keys and the corresponding functions or objects as values.</dd>
-</dl>
-<h2 id="raises">Raises</h2>
-<dl>
-<dt><code>FileNotFoundError</code></dt>
-<dd>If the specified module path does not exist.</dd>
-</dl></div>
-</dd>
-<dt id="praisonai.agents_generator.AgentsGenerator.load_tools_from_module_class"><code class="name flex">
-<span>def <span class="ident">load_tools_from_module_class</span></span>(<span>self, module_path)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Loads tools from a specified module path containing classes that inherit from BaseTool or are part of langchain_community.tools package.</p>
-<h2 id="parameters">Parameters</h2>
-<p>module_path (str): The path to the module containing the tools.</p>
-<h2 id="returns">Returns</h2>
-<dl>
-<dt><code>dict</code></dt>
-<dd>A dictionary containing the names of the tools as keys and the corresponding initialized instances of the classes as values.</dd>
-</dl>
-<h2 id="raises">Raises</h2>
-<dl>
-<dt><code>FileNotFoundError</code></dt>
-<dd>If the specified module path does not exist.</dd>
-</dl></div>
-</dd>
-<dt id="praisonai.agents_generator.AgentsGenerator.load_tools_from_package"><code class="name flex">
-<span>def <span class="ident">load_tools_from_package</span></span>(<span>self, package_path)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Loads tools from a specified package path containing modules with functions or classes.</p>
-<h2 id="parameters">Parameters</h2>
-<p>package_path (str): The path to the package containing the tools.</p>
-<h2 id="returns">Returns</h2>
-<dl>
-<dt><code>dict</code></dt>
-<dd>A dictionary containing the names of the tools as keys and the corresponding initialized instances of the classes as values.</dd>
-</dl>
-<h2 id="raises">Raises</h2>
-<dl>
-<dt><code>FileNotFoundError</code></dt>
-<dd>If the specified package path does not exist.</dd>
-</dl>
-<p>This function iterates through all the .py files in the specified package path, excluding those that start with "__". For each file, it imports the corresponding module and checks if it contains any functions or classes that can be loaded as tools. The function then returns a dictionary containing the names of the tools as keys and the corresponding initialized instances of the classes as values.</p></div>
-</dd>
-</dl>
-</dd>
-</dl>
-</section>
-</article>
-<nav id="sidebar">
-<div class="toc">
-<ul></ul>
-</div>
-<ul id="index">
-<li><h3>Super-module</h3>
-<ul>
-<li><code><a title="praisonai" href="index.html">praisonai</a></code></li>
-</ul>
-</li>
-<li><h3><a href="#header-functions">Functions</a></h3>
-<ul class="">
-<li><code><a title="praisonai.agents_generator.disable_crewai_telemetry" href="#praisonai.agents_generator.disable_crewai_telemetry">disable_crewai_telemetry</a></code></li>
-<li><code><a title="praisonai.agents_generator.noop" href="#praisonai.agents_generator.noop">noop</a></code></li>
-</ul>
-</li>
-<li><h3><a href="#header-classes">Classes</a></h3>
-<ul>
-<li>
-<h4><code><a title="praisonai.agents_generator.AgentsGenerator" href="#praisonai.agents_generator.AgentsGenerator">AgentsGenerator</a></code></h4>
-<ul class="">
-<li><code><a title="praisonai.agents_generator.AgentsGenerator.generate_crew_and_kickoff" href="#praisonai.agents_generator.AgentsGenerator.generate_crew_and_kickoff">generate_crew_and_kickoff</a></code></li>
-<li><code><a title="praisonai.agents_generator.AgentsGenerator.is_function_or_decorated" href="#praisonai.agents_generator.AgentsGenerator.is_function_or_decorated">is_function_or_decorated</a></code></li>
-<li><code><a title="praisonai.agents_generator.AgentsGenerator.load_tools_from_module" href="#praisonai.agents_generator.AgentsGenerator.load_tools_from_module">load_tools_from_module</a></code></li>
-<li><code><a title="praisonai.agents_generator.AgentsGenerator.load_tools_from_module_class" href="#praisonai.agents_generator.AgentsGenerator.load_tools_from_module_class">load_tools_from_module_class</a></code></li>
-<li><code><a title="praisonai.agents_generator.AgentsGenerator.load_tools_from_package" href="#praisonai.agents_generator.AgentsGenerator.load_tools_from_package">load_tools_from_package</a></code></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</nav>
-</main>
-<footer id="footer">
-<p>Generated by <a href="https://pdoc3.github.io/pdoc" title="pdoc: Python API documentation generator"><cite>pdoc</cite> 0.11.0</a>.</p>
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docs/api/praisonai/auto.html

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-</head>
-<body>
-<main>
-<article id="content">
-<header>
-<h1 class="title">Module <code>praisonai.auto</code></h1>
-</header>
-<section id="section-intro">
-</section>
-<section>
-</section>
-<section>
-</section>
-<section>
-</section>
-<section>
-<h2 class="section-title" id="header-classes">Classes</h2>
-<dl>
-<dt id="praisonai.auto.AutoGenerator"><code class="flex name class">
-<span>class <span class="ident">AutoGenerator</span></span>
-<span>(</span><span>topic='Movie Story writing about AI', agent_file='test.yaml', framework='crewai', config_list: Optional[List[Dict]] = None)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Initialize the AutoGenerator class with the specified topic, agent file, and framework.
-Note: autogen framework is different from this AutoGenerator class.</p>
-<h2 id="args">Args</h2>
-<dl>
-<dt><strong><code>topic</code></strong> :&ensp;<code>str</code>, optional</dt>
-<dd>The topic for the generated team structure. Defaults to "Movie Story writing about AI".</dd>
-<dt><strong><code>agent_file</code></strong> :&ensp;<code>str</code>, optional</dt>
-<dd>The name of the YAML file to save the generated team structure. Defaults to "test.yaml".</dd>
-<dt><strong><code>framework</code></strong> :&ensp;<code>str</code>, optional</dt>
-<dd>The framework for the generated team structure. Defaults to "crewai".</dd>
-<dt><strong><code>config_list</code></strong> :&ensp;<code>Optional[List[Dict]]</code>, optional</dt>
-<dd>A list containing the configuration details for the OpenAI API.
-If None, it defaults to using environment variables or hardcoded values.</dd>
-</dl>
-<h2 id="attributes">Attributes</h2>
-<dl>
-<dt><strong><code>config_list</code></strong> :&ensp;<code>list</code></dt>
-<dd>A list containing the configuration details for the OpenAI API.</dd>
-<dt><strong><code>topic</code></strong> :&ensp;<code>str</code></dt>
-<dd>The specified topic for the generated team structure.</dd>
-<dt><strong><code>agent_file</code></strong> :&ensp;<code>str</code></dt>
-<dd>The specified name of the YAML file to save the generated team structure.</dd>
-<dt><strong><code>framework</code></strong> :&ensp;<code>str</code></dt>
-<dd>The specified framework for the generated team structure.</dd>
-<dt><strong><code>client</code></strong> :&ensp;<code>instructor.Client</code></dt>
-<dd>An instance of the instructor.Client class initialized with the specified OpenAI API configuration.</dd>
-</dl></div>
-<details class="source">
-<summary>
-<span>Expand source code</span>
-</summary>
-<pre><code class="python">class AutoGenerator:
-    def __init__(self, topic=&#34;Movie Story writing about AI&#34;, agent_file=&#34;test.yaml&#34;, framework=&#34;crewai&#34;, config_list: Optional[List[Dict]] = None):
-        &#34;&#34;&#34;
-        Initialize the AutoGenerator class with the specified topic, agent file, and framework.
-        Note: autogen framework is different from this AutoGenerator class.
-
-        Args:
-            topic (str, optional): The topic for the generated team structure. Defaults to &#34;Movie Story writing about AI&#34;.
-            agent_file (str, optional): The name of the YAML file to save the generated team structure. Defaults to &#34;test.yaml&#34;.
-            framework (str, optional): The framework for the generated team structure. Defaults to &#34;crewai&#34;.
-            config_list (Optional[List[Dict]], optional): A list containing the configuration details for the OpenAI API. 
-                                                          If None, it defaults to using environment variables or hardcoded values.
-        Attributes:
-            config_list (list): A list containing the configuration details for the OpenAI API.
-            topic (str): The specified topic for the generated team structure.
-            agent_file (str): The specified name of the YAML file to save the generated team structure.
-            framework (str): The specified framework for the generated team structure.
-            client (instructor.Client): An instance of the instructor.Client class initialized with the specified OpenAI API configuration.
-        &#34;&#34;&#34;
-        self.config_list = config_list or [
-            {
-                &#39;model&#39;: os.environ.get(&#34;OPENAI_MODEL_NAME&#34;, &#34;gpt-4o&#34;),
-                &#39;base_url&#39;: os.environ.get(&#34;OPENAI_API_BASE&#34;, &#34;https://api.openai.com/v1&#34;),
-                &#39;api_key&#39;: os.environ.get(&#34;OPENAI_API_KEY&#34;)
-            }
-        ]
-        self.topic = topic
-        self.agent_file = agent_file
-        self.framework = framework or &#34;crewai&#34;
-        self.client = instructor.patch(
-            OpenAI(
-                base_url=self.config_list[0][&#39;base_url&#39;],
-                api_key=os.getenv(&#34;OPENAI_API_KEY&#34;),
-            ),
-            mode=instructor.Mode.JSON,
-        )
-
-    def generate(self):
-        &#34;&#34;&#34;
-        Generates a team structure for the specified topic.
-
-        Args:
-            None
-
-        Returns:
-            str: The full path of the YAML file containing the generated team structure.
-
-        Raises:
-            Exception: If the generation process fails.
-
-        Usage:
-            generator = AutoGenerator(framework=&#34;crewai&#34;, topic=&#34;Create a movie script about Cat in Mars&#34;)
-            path = generator.generate()
-            print(path)
-        &#34;&#34;&#34;
-        response = self.client.chat.completions.create(
-            model=self.config_list[0][&#39;model&#39;],
-            response_model=TeamStructure,
-            max_retries=10,
-            messages=[
-                {&#34;role&#34;: &#34;system&#34;, &#34;content&#34;: &#34;You are a helpful assistant designed to output complex team structures.&#34;},
-                {&#34;role&#34;: &#34;user&#34;, &#34;content&#34;: self.get_user_content()}
-            ]
-        )
-        json_data = json.loads(response.model_dump_json())
-        self.convert_and_save(json_data)
-        full_path = os.path.abspath(self.agent_file)
-        return full_path
-
-    def convert_and_save(self, json_data):
-        &#34;&#34;&#34;Converts the provided JSON data into the desired YAML format and saves it to a file.
-
-        Args:
-            json_data (dict): The JSON data representing the team structure.
-            topic (str, optional): The topic to be inserted into the YAML. Defaults to &#34;Artificial Intelligence&#34;.
-            agent_file (str, optional): The name of the YAML file to save. Defaults to &#34;test.yaml&#34;.
-        &#34;&#34;&#34;
-
-        yaml_data = {
-            &#34;framework&#34;: self.framework,
-            &#34;topic&#34;: self.topic,
-            &#34;roles&#34;: {},
-            &#34;dependencies&#34;: []
-        }
-
-        for role_id, role_details in json_data[&#39;roles&#39;].items():
-            yaml_data[&#39;roles&#39;][role_id] = {
-                &#34;backstory&#34;: &#34;&#34; + role_details[&#39;backstory&#39;],
-                &#34;goal&#34;: role_details[&#39;goal&#39;],
-                &#34;role&#34;: role_details[&#39;role&#39;],
-                &#34;tasks&#34;: {},
-                # &#34;tools&#34;: role_details.get(&#39;tools&#39;, []),
-                &#34;tools&#34;: [&#39;&#39;]
-            }
-
-            for task_id, task_details in role_details[&#39;tasks&#39;].items():
-                yaml_data[&#39;roles&#39;][role_id][&#39;tasks&#39;][task_id] = {
-                    &#34;description&#34;: &#34;&#34; + task_details[&#39;description&#39;],
-                    &#34;expected_output&#34;: &#34;&#34; + task_details[&#39;expected_output&#39;]
-                }
-
-        # Save to YAML file, maintaining the order
-        with open(self.agent_file, &#39;w&#39;) as f:
-            yaml.dump(yaml_data, f, allow_unicode=True, sort_keys=False)
-
-    def get_user_content(self):
-        &#34;&#34;&#34;
-        Generates a prompt for the OpenAI API to generate a team structure.
-
-        Args:
-            None
-
-        Returns:
-            str: The prompt for the OpenAI API.
-
-        Usage:
-            generator = AutoGenerator(framework=&#34;crewai&#34;, topic=&#34;Create a movie script about Cat in Mars&#34;)
-            prompt = generator.get_user_content()
-            print(prompt)
-        &#34;&#34;&#34;
-        user_content = &#34;&#34;&#34;Generate a team structure for  \&#34;&#34;&#34;&#34; + self.topic + &#34;&#34;&#34;\&#34; task. 
-No Input data will be provided to the team.
-The team will work in sequence. First role will pass the output to the next role, and so on.
-The last role will generate the final output.
-Think step by step.
-With maximum 3 roles, each with 1 task. Include role goals, backstories, task descriptions, and expected outputs.
-List of Available Tools: CodeDocsSearchTool, CSVSearchTool, DirectorySearchTool, DOCXSearchTool, DirectoryReadTool, FileReadTool, TXTSearchTool, JSONSearchTool, MDXSearchTool, PDFSearchTool, RagTool, ScrapeElementFromWebsiteTool, ScrapeWebsiteTool, WebsiteSearchTool, XMLSearchTool, YoutubeChannelSearchTool, YoutubeVideoSearchTool.
-Only use Available Tools. Do Not use any other tools. 
-Example Below: 
-Use below example to understand the structure of the output. 
-The final role you create should satisfy the provided task: &#34;&#34;&#34; + self.topic + &#34;&#34;&#34;.
-{
-&#34;roles&#34;: {
-&#34;narrative_designer&#34;: {
-&#34;role&#34;: &#34;Narrative Designer&#34;,
-&#34;goal&#34;: &#34;Create AI storylines&#34;,
-&#34;backstory&#34;: &#34;Skilled in narrative development for AI, with a focus on story resonance.&#34;,
-&#34;tools&#34;: [&#34;ScrapeWebsiteTool&#34;],
-&#34;tasks&#34;: {
-&#34;story_concept_development&#34;: {
-&#34;description&#34;: &#34;Craft a unique AI story concept with depth and engagement using concept from this page the content https://www.asthebirdfliesblog.com/posts/how-to-write-book-story-development .&#34;,
-&#34;expected_output&#34;: &#34;Document with narrative arcs, character bios, and settings.&#34;
-}
-}
-},
-&#34;scriptwriter&#34;: {
-&#34;role&#34;: &#34;Scriptwriter&#34;,
-&#34;goal&#34;: &#34;Write scripts from AI concepts&#34;,
-&#34;backstory&#34;: &#34;Expert in dialogue and script structure, translating concepts into scripts.&#34;,
-&#34;tasks&#34;: {
-&#34;scriptwriting_task&#34;: {
-&#34;description&#34;: &#34;Turn narrative concepts into scripts, including dialogue and scenes.&#34;,
-&#34;expected_output&#34;: &#34;Production-ready script with dialogue and scene details.&#34;
-}
-}
-}
-}
-}
-        &#34;&#34;&#34;
-        return user_content</code></pre>
-</details>
-<h3>Methods</h3>
-<dl>
-<dt id="praisonai.auto.AutoGenerator.convert_and_save"><code class="name flex">
-<span>def <span class="ident">convert_and_save</span></span>(<span>self, json_data)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Converts the provided JSON data into the desired YAML format and saves it to a file.</p>
-<h2 id="args">Args</h2>
-<dl>
-<dt><strong><code>json_data</code></strong> :&ensp;<code>dict</code></dt>
-<dd>The JSON data representing the team structure.</dd>
-<dt><strong><code>topic</code></strong> :&ensp;<code>str</code>, optional</dt>
-<dd>The topic to be inserted into the YAML. Defaults to "Artificial Intelligence".</dd>
-<dt><strong><code>agent_file</code></strong> :&ensp;<code>str</code>, optional</dt>
-<dd>The name of the YAML file to save. Defaults to "test.yaml".</dd>
-</dl></div>
-</dd>
-<dt id="praisonai.auto.AutoGenerator.generate"><code class="name flex">
-<span>def <span class="ident">generate</span></span>(<span>self)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Generates a team structure for the specified topic.</p>
-<h2 id="args">Args</h2>
-<p>None</p>
-<h2 id="returns">Returns</h2>
-<dl>
-<dt><code>str</code></dt>
-<dd>The full path of the YAML file containing the generated team structure.</dd>
-</dl>
-<h2 id="raises">Raises</h2>
-<dl>
-<dt><code>Exception</code></dt>
-<dd>If the generation process fails.</dd>
-</dl>
-<h2 id="usage">Usage</h2>
-<p>generator = AutoGenerator(framework="crewai", topic="Create a movie script about Cat in Mars")
-path = generator.generate()
-print(path)</p></div>
-</dd>
-<dt id="praisonai.auto.AutoGenerator.get_user_content"><code class="name flex">
-<span>def <span class="ident">get_user_content</span></span>(<span>self)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Generates a prompt for the OpenAI API to generate a team structure.</p>
-<h2 id="args">Args</h2>
-<p>None</p>
-<h2 id="returns">Returns</h2>
-<dl>
-<dt><code>str</code></dt>
-<dd>The prompt for the OpenAI API.</dd>
-</dl>
-<h2 id="usage">Usage</h2>
-<p>generator = AutoGenerator(framework="crewai", topic="Create a movie script about Cat in Mars")
-prompt = generator.get_user_content()
-print(prompt)</p></div>
-</dd>
-</dl>
-</dd>
-<dt id="praisonai.auto.RoleDetails"><code class="flex name class">
-<span>class <span class="ident">RoleDetails</span></span>
-<span>(</span><span>**data: Any)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Usage docs: <a href="https://docs.pydantic.dev/2.7/concepts/models/">https://docs.pydantic.dev/2.7/concepts/models/</a></p>
-<p>A base class for creating Pydantic models.</p>
-<h2 id="attributes">Attributes</h2>
-<dl>
-<dt><strong><code>__class_vars__</code></strong></dt>
-<dd>The names of classvars defined on the model.</dd>
-<dt><strong><code>__private_attributes__</code></strong></dt>
-<dd>Metadata about the private attributes of the model.</dd>
-<dt><strong><code>__signature__</code></strong></dt>
-<dd>The signature for instantiating the model.</dd>
-<dt><strong><code>__pydantic_complete__</code></strong></dt>
-<dd>Whether model building is completed, or if there are still undefined fields.</dd>
-<dt><strong><code>__pydantic_core_schema__</code></strong></dt>
-<dd>The pydantic-core schema used to build the SchemaValidator and SchemaSerializer.</dd>
-<dt><strong><code>__pydantic_custom_init__</code></strong></dt>
-<dd>Whether the model has a custom <code>__init__</code> function.</dd>
-<dt><strong><code>__pydantic_decorators__</code></strong></dt>
-<dd>Metadata containing the decorators defined on the model.
-This replaces <code>Model.__validators__</code> and <code>Model.__root_validators__</code> from Pydantic V1.</dd>
-<dt><strong><code>__pydantic_generic_metadata__</code></strong></dt>
-<dd>Metadata for generic models; contains data used for a similar purpose to
-<strong>args</strong>, <strong>origin</strong>, <strong>parameters</strong> in typing-module generics. May eventually be replaced by these.</dd>
-<dt><strong><code>__pydantic_parent_namespace__</code></strong></dt>
-<dd>Parent namespace of the model, used for automatic rebuilding of models.</dd>
-<dt><strong><code>__pydantic_post_init__</code></strong></dt>
-<dd>The name of the post-init method for the model, if defined.</dd>
-<dt><strong><code>__pydantic_root_model__</code></strong></dt>
-<dd>Whether the model is a <code>RootModel</code>.</dd>
-<dt><strong><code>__pydantic_serializer__</code></strong></dt>
-<dd>The pydantic-core SchemaSerializer used to dump instances of the model.</dd>
-<dt><strong><code>__pydantic_validator__</code></strong></dt>
-<dd>The pydantic-core SchemaValidator used to validate instances of the model.</dd>
-<dt><strong><code>__pydantic_extra__</code></strong></dt>
-<dd>An instance attribute with the values of extra fields from validation when
-<code>model_config['extra'] == 'allow'</code>.</dd>
-<dt><strong><code>__pydantic_fields_set__</code></strong></dt>
-<dd>An instance attribute with the names of fields explicitly set.</dd>
-<dt><strong><code>__pydantic_private__</code></strong></dt>
-<dd>Instance attribute with the values of private attributes set on the model instance.</dd>
-</dl>
-<p>Create a new model by parsing and validating input data from keyword arguments.</p>
-<p>Raises [<code>ValidationError</code>][pydantic_core.ValidationError] if the input data cannot be
-validated to form a valid model.</p>
-<p><code>self</code> is explicitly positional-only to allow <code>self</code> as a field name.</p></div>
-<details class="source">
-<summary>
-<span>Expand source code</span>
-</summary>
-<pre><code class="python">class RoleDetails(BaseModel):
-    role: str
-    goal: str
-    backstory: str
-    tasks: Dict[str, TaskDetails]
-    tools: List[str]</code></pre>
-</details>
-<h3>Ancestors</h3>
-<ul class="hlist">
-<li>pydantic.main.BaseModel</li>
-</ul>
-<h3>Class variables</h3>
-<dl>
-<dt id="praisonai.auto.RoleDetails.backstory"><code class="name">var <span class="ident">backstory</span> : str</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.auto.RoleDetails.goal"><code class="name">var <span class="ident">goal</span> : str</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.auto.RoleDetails.model_computed_fields"><code class="name">var <span class="ident">model_computed_fields</span></code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.auto.RoleDetails.model_config"><code class="name">var <span class="ident">model_config</span></code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.auto.RoleDetails.model_fields"><code class="name">var <span class="ident">model_fields</span></code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.auto.RoleDetails.role"><code class="name">var <span class="ident">role</span> : str</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.auto.RoleDetails.tasks"><code class="name">var <span class="ident">tasks</span> : Dict[str, <a title="praisonai.auto.TaskDetails" href="#praisonai.auto.TaskDetails">TaskDetails</a>]</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.auto.RoleDetails.tools"><code class="name">var <span class="ident">tools</span> : List[str]</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-</dl>
-</dd>
-<dt id="praisonai.auto.TaskDetails"><code class="flex name class">
-<span>class <span class="ident">TaskDetails</span></span>
-<span>(</span><span>**data: Any)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Usage docs: <a href="https://docs.pydantic.dev/2.7/concepts/models/">https://docs.pydantic.dev/2.7/concepts/models/</a></p>
-<p>A base class for creating Pydantic models.</p>
-<h2 id="attributes">Attributes</h2>
-<dl>
-<dt><strong><code>__class_vars__</code></strong></dt>
-<dd>The names of classvars defined on the model.</dd>
-<dt><strong><code>__private_attributes__</code></strong></dt>
-<dd>Metadata about the private attributes of the model.</dd>
-<dt><strong><code>__signature__</code></strong></dt>
-<dd>The signature for instantiating the model.</dd>
-<dt><strong><code>__pydantic_complete__</code></strong></dt>
-<dd>Whether model building is completed, or if there are still undefined fields.</dd>
-<dt><strong><code>__pydantic_core_schema__</code></strong></dt>
-<dd>The pydantic-core schema used to build the SchemaValidator and SchemaSerializer.</dd>
-<dt><strong><code>__pydantic_custom_init__</code></strong></dt>
-<dd>Whether the model has a custom <code>__init__</code> function.</dd>
-<dt><strong><code>__pydantic_decorators__</code></strong></dt>
-<dd>Metadata containing the decorators defined on the model.
-This replaces <code>Model.__validators__</code> and <code>Model.__root_validators__</code> from Pydantic V1.</dd>
-<dt><strong><code>__pydantic_generic_metadata__</code></strong></dt>
-<dd>Metadata for generic models; contains data used for a similar purpose to
-<strong>args</strong>, <strong>origin</strong>, <strong>parameters</strong> in typing-module generics. May eventually be replaced by these.</dd>
-<dt><strong><code>__pydantic_parent_namespace__</code></strong></dt>
-<dd>Parent namespace of the model, used for automatic rebuilding of models.</dd>
-<dt><strong><code>__pydantic_post_init__</code></strong></dt>
-<dd>The name of the post-init method for the model, if defined.</dd>
-<dt><strong><code>__pydantic_root_model__</code></strong></dt>
-<dd>Whether the model is a <code>RootModel</code>.</dd>
-<dt><strong><code>__pydantic_serializer__</code></strong></dt>
-<dd>The pydantic-core SchemaSerializer used to dump instances of the model.</dd>
-<dt><strong><code>__pydantic_validator__</code></strong></dt>
-<dd>The pydantic-core SchemaValidator used to validate instances of the model.</dd>
-<dt><strong><code>__pydantic_extra__</code></strong></dt>
-<dd>An instance attribute with the values of extra fields from validation when
-<code>model_config['extra'] == 'allow'</code>.</dd>
-<dt><strong><code>__pydantic_fields_set__</code></strong></dt>
-<dd>An instance attribute with the names of fields explicitly set.</dd>
-<dt><strong><code>__pydantic_private__</code></strong></dt>
-<dd>Instance attribute with the values of private attributes set on the model instance.</dd>
-</dl>
-<p>Create a new model by parsing and validating input data from keyword arguments.</p>
-<p>Raises [<code>ValidationError</code>][pydantic_core.ValidationError] if the input data cannot be
-validated to form a valid model.</p>
-<p><code>self</code> is explicitly positional-only to allow <code>self</code> as a field name.</p></div>
-<details class="source">
-<summary>
-<span>Expand source code</span>
-</summary>
-<pre><code class="python">class TaskDetails(BaseModel):
-    description: str
-    expected_output: str</code></pre>
-</details>
-<h3>Ancestors</h3>
-<ul class="hlist">
-<li>pydantic.main.BaseModel</li>
-</ul>
-<h3>Class variables</h3>
-<dl>
-<dt id="praisonai.auto.TaskDetails.description"><code class="name">var <span class="ident">description</span> : str</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.auto.TaskDetails.expected_output"><code class="name">var <span class="ident">expected_output</span> : str</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.auto.TaskDetails.model_computed_fields"><code class="name">var <span class="ident">model_computed_fields</span></code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.auto.TaskDetails.model_config"><code class="name">var <span class="ident">model_config</span></code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.auto.TaskDetails.model_fields"><code class="name">var <span class="ident">model_fields</span></code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-</dl>
-</dd>
-<dt id="praisonai.auto.TeamStructure"><code class="flex name class">
-<span>class <span class="ident">TeamStructure</span></span>
-<span>(</span><span>**data: Any)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Usage docs: <a href="https://docs.pydantic.dev/2.7/concepts/models/">https://docs.pydantic.dev/2.7/concepts/models/</a></p>
-<p>A base class for creating Pydantic models.</p>
-<h2 id="attributes">Attributes</h2>
-<dl>
-<dt><strong><code>__class_vars__</code></strong></dt>
-<dd>The names of classvars defined on the model.</dd>
-<dt><strong><code>__private_attributes__</code></strong></dt>
-<dd>Metadata about the private attributes of the model.</dd>
-<dt><strong><code>__signature__</code></strong></dt>
-<dd>The signature for instantiating the model.</dd>
-<dt><strong><code>__pydantic_complete__</code></strong></dt>
-<dd>Whether model building is completed, or if there are still undefined fields.</dd>
-<dt><strong><code>__pydantic_core_schema__</code></strong></dt>
-<dd>The pydantic-core schema used to build the SchemaValidator and SchemaSerializer.</dd>
-<dt><strong><code>__pydantic_custom_init__</code></strong></dt>
-<dd>Whether the model has a custom <code>__init__</code> function.</dd>
-<dt><strong><code>__pydantic_decorators__</code></strong></dt>
-<dd>Metadata containing the decorators defined on the model.
-This replaces <code>Model.__validators__</code> and <code>Model.__root_validators__</code> from Pydantic V1.</dd>
-<dt><strong><code>__pydantic_generic_metadata__</code></strong></dt>
-<dd>Metadata for generic models; contains data used for a similar purpose to
-<strong>args</strong>, <strong>origin</strong>, <strong>parameters</strong> in typing-module generics. May eventually be replaced by these.</dd>
-<dt><strong><code>__pydantic_parent_namespace__</code></strong></dt>
-<dd>Parent namespace of the model, used for automatic rebuilding of models.</dd>
-<dt><strong><code>__pydantic_post_init__</code></strong></dt>
-<dd>The name of the post-init method for the model, if defined.</dd>
-<dt><strong><code>__pydantic_root_model__</code></strong></dt>
-<dd>Whether the model is a <code>RootModel</code>.</dd>
-<dt><strong><code>__pydantic_serializer__</code></strong></dt>
-<dd>The pydantic-core SchemaSerializer used to dump instances of the model.</dd>
-<dt><strong><code>__pydantic_validator__</code></strong></dt>
-<dd>The pydantic-core SchemaValidator used to validate instances of the model.</dd>
-<dt><strong><code>__pydantic_extra__</code></strong></dt>
-<dd>An instance attribute with the values of extra fields from validation when
-<code>model_config['extra'] == 'allow'</code>.</dd>
-<dt><strong><code>__pydantic_fields_set__</code></strong></dt>
-<dd>An instance attribute with the names of fields explicitly set.</dd>
-<dt><strong><code>__pydantic_private__</code></strong></dt>
-<dd>Instance attribute with the values of private attributes set on the model instance.</dd>
-</dl>
-<p>Create a new model by parsing and validating input data from keyword arguments.</p>
-<p>Raises [<code>ValidationError</code>][pydantic_core.ValidationError] if the input data cannot be
-validated to form a valid model.</p>
-<p><code>self</code> is explicitly positional-only to allow <code>self</code> as a field name.</p></div>
-<details class="source">
-<summary>
-<span>Expand source code</span>
-</summary>
-<pre><code class="python">class TeamStructure(BaseModel):
-    roles: Dict[str, RoleDetails]</code></pre>
-</details>
-<h3>Ancestors</h3>
-<ul class="hlist">
-<li>pydantic.main.BaseModel</li>
-</ul>
-<h3>Class variables</h3>
-<dl>
-<dt id="praisonai.auto.TeamStructure.model_computed_fields"><code class="name">var <span class="ident">model_computed_fields</span></code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.auto.TeamStructure.model_config"><code class="name">var <span class="ident">model_config</span></code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.auto.TeamStructure.model_fields"><code class="name">var <span class="ident">model_fields</span></code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.auto.TeamStructure.roles"><code class="name">var <span class="ident">roles</span> : Dict[str, <a title="praisonai.auto.RoleDetails" href="#praisonai.auto.RoleDetails">RoleDetails</a>]</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-</dl>
-</dd>
-</dl>
-</section>
-</article>
-<nav id="sidebar">
-<div class="toc">
-<ul></ul>
-</div>
-<ul id="index">
-<li><h3>Super-module</h3>
-<ul>
-<li><code><a title="praisonai" href="index.html">praisonai</a></code></li>
-</ul>
-</li>
-<li><h3><a href="#header-classes">Classes</a></h3>
-<ul>
-<li>
-<h4><code><a title="praisonai.auto.AutoGenerator" href="#praisonai.auto.AutoGenerator">AutoGenerator</a></code></h4>
-<ul class="">
-<li><code><a title="praisonai.auto.AutoGenerator.convert_and_save" href="#praisonai.auto.AutoGenerator.convert_and_save">convert_and_save</a></code></li>
-<li><code><a title="praisonai.auto.AutoGenerator.generate" href="#praisonai.auto.AutoGenerator.generate">generate</a></code></li>
-<li><code><a title="praisonai.auto.AutoGenerator.get_user_content" href="#praisonai.auto.AutoGenerator.get_user_content">get_user_content</a></code></li>
-</ul>
-</li>
-<li>
-<h4><code><a title="praisonai.auto.RoleDetails" href="#praisonai.auto.RoleDetails">RoleDetails</a></code></h4>
-<ul class="">
-<li><code><a title="praisonai.auto.RoleDetails.backstory" href="#praisonai.auto.RoleDetails.backstory">backstory</a></code></li>
-<li><code><a title="praisonai.auto.RoleDetails.goal" href="#praisonai.auto.RoleDetails.goal">goal</a></code></li>
-<li><code><a title="praisonai.auto.RoleDetails.model_computed_fields" href="#praisonai.auto.RoleDetails.model_computed_fields">model_computed_fields</a></code></li>
-<li><code><a title="praisonai.auto.RoleDetails.model_config" href="#praisonai.auto.RoleDetails.model_config">model_config</a></code></li>
-<li><code><a title="praisonai.auto.RoleDetails.model_fields" href="#praisonai.auto.RoleDetails.model_fields">model_fields</a></code></li>
-<li><code><a title="praisonai.auto.RoleDetails.role" href="#praisonai.auto.RoleDetails.role">role</a></code></li>
-<li><code><a title="praisonai.auto.RoleDetails.tasks" href="#praisonai.auto.RoleDetails.tasks">tasks</a></code></li>
-<li><code><a title="praisonai.auto.RoleDetails.tools" href="#praisonai.auto.RoleDetails.tools">tools</a></code></li>
-</ul>
-</li>
-<li>
-<h4><code><a title="praisonai.auto.TaskDetails" href="#praisonai.auto.TaskDetails">TaskDetails</a></code></h4>
-<ul class="">
-<li><code><a title="praisonai.auto.TaskDetails.description" href="#praisonai.auto.TaskDetails.description">description</a></code></li>
-<li><code><a title="praisonai.auto.TaskDetails.expected_output" href="#praisonai.auto.TaskDetails.expected_output">expected_output</a></code></li>
-<li><code><a title="praisonai.auto.TaskDetails.model_computed_fields" href="#praisonai.auto.TaskDetails.model_computed_fields">model_computed_fields</a></code></li>
-<li><code><a title="praisonai.auto.TaskDetails.model_config" href="#praisonai.auto.TaskDetails.model_config">model_config</a></code></li>
-<li><code><a title="praisonai.auto.TaskDetails.model_fields" href="#praisonai.auto.TaskDetails.model_fields">model_fields</a></code></li>
-</ul>
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-<li>
-<h4><code><a title="praisonai.auto.TeamStructure" href="#praisonai.auto.TeamStructure">TeamStructure</a></code></h4>
-<ul class="">
-<li><code><a title="praisonai.auto.TeamStructure.model_computed_fields" href="#praisonai.auto.TeamStructure.model_computed_fields">model_computed_fields</a></code></li>
-<li><code><a title="praisonai.auto.TeamStructure.model_config" href="#praisonai.auto.TeamStructure.model_config">model_config</a></code></li>
-<li><code><a title="praisonai.auto.TeamStructure.model_fields" href="#praisonai.auto.TeamStructure.model_fields">model_fields</a></code></li>
-<li><code><a title="praisonai.auto.TeamStructure.roles" href="#praisonai.auto.TeamStructure.roles">roles</a></code></li>
-</ul>
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-<main>
-<article id="content">
-<header>
-<h1 class="title">Module <code>praisonai.cli</code></h1>
-</header>
-<section id="section-intro">
-</section>
-<section>
-</section>
-<section>
-</section>
-<section>
-</section>
-<section>
-<h2 class="section-title" id="header-classes">Classes</h2>
-<dl>
-<dt id="praisonai.cli.PraisonAI"><code class="flex name class">
-<span>class <span class="ident">PraisonAI</span></span>
-<span>(</span><span>agent_file='agents.yaml', framework='', auto=False, init=False)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Initialize the PraisonAI object with default parameters.</p>
-<h2 id="parameters">Parameters</h2>
-<p>agent_file (str): The default agent file to use. Defaults to "agents.yaml".
-framework (str): The default framework to use. Defaults to "crewai".
-auto (bool): A flag indicating whether to enable auto mode. Defaults to False.
-init (bool): A flag indicating whether to enable initialization mode. Defaults to False.</p>
-<h2 id="attributes">Attributes</h2>
-<dl>
-<dt><strong><code>config_list</code></strong> :&ensp;<code>list</code></dt>
-<dd>A list of configuration dictionaries for the OpenAI API.</dd>
-<dt><strong><code>agent_file</code></strong> :&ensp;<code>str</code></dt>
-<dd>The agent file to use.</dd>
-<dt><strong><code>framework</code></strong> :&ensp;<code>str</code></dt>
-<dd>The framework to use.</dd>
-<dt><strong><code>auto</code></strong> :&ensp;<code>bool</code></dt>
-<dd>A flag indicating whether to enable auto mode.</dd>
-<dt><strong><code>init</code></strong> :&ensp;<code>bool</code></dt>
-<dd>A flag indicating whether to enable initialization mode.</dd>
-</dl></div>
-<details class="source">
-<summary>
-<span>Expand source code</span>
-</summary>
-<pre><code class="python">class PraisonAI:
-    def __init__(self, agent_file=&#34;agents.yaml&#34;, framework=&#34;&#34;, auto=False, init=False):
-        &#34;&#34;&#34;
-        Initialize the PraisonAI object with default parameters.
-
-        Parameters:
-            agent_file (str): The default agent file to use. Defaults to &#34;agents.yaml&#34;.
-            framework (str): The default framework to use. Defaults to &#34;crewai&#34;.
-            auto (bool): A flag indicating whether to enable auto mode. Defaults to False.
-            init (bool): A flag indicating whether to enable initialization mode. Defaults to False.
-
-        Attributes:
-            config_list (list): A list of configuration dictionaries for the OpenAI API.
-            agent_file (str): The agent file to use.
-            framework (str): The framework to use.
-            auto (bool): A flag indicating whether to enable auto mode.
-            init (bool): A flag indicating whether to enable initialization mode.
-        &#34;&#34;&#34;
-        self.config_list = [
-            {
-                &#39;model&#39;: os.environ.get(&#34;OPENAI_MODEL_NAME&#34;, &#34;gpt-4o&#34;),
-                &#39;base_url&#39;: os.environ.get(&#34;OPENAI_API_BASE&#34;, &#34;https://api.openai.com/v1&#34;),
-                &#39;api_key&#39;: os.environ.get(&#34;OPENAI_API_KEY&#34;)
-            }
-        ]
-        self.agent_file = agent_file
-        self.framework = framework
-        self.auto = auto
-        self.init = init
-
-    def main(self):
-        &#34;&#34;&#34;
-        The main function of the PraisonAI object. It parses the command-line arguments,
-        initializes the necessary attributes, and then calls the appropriate methods based on the
-        provided arguments.
-
-        Args:
-            self (PraisonAI): An instance of the PraisonAI class.
-    
-        Returns:
-            Any: Depending on the arguments provided, the function may return a result from the
-            AgentsGenerator, a deployment result from the CloudDeployer, or a message indicating
-            the successful creation of a file.
-        &#34;&#34;&#34;
-        args = self.parse_args()
-        if args is None:
-            agents_generator = AgentsGenerator(self.agent_file, self.framework, self.config_list)
-            result = agents_generator.generate_crew_and_kickoff()
-            return result
-        if args.deploy:
-            from .deploy import CloudDeployer
-            deployer = CloudDeployer()
-            deployer.run_commands()
-            return
-        invocation_cmd = &#34;praisonai&#34;
-        version_string = f&#34;PraisonAI version {__version__}&#34;
-        
-        self.framework = args.framework or self.framework 
-        
-        if args.agent_file:
-            if args.agent_file.startswith(&#34;tests.test&#34;): # Argument used for testing purposes. eg: python -m unittest tests.test 
-                print(&#34;test&#34;)
-            else:
-                self.agent_file = args.agent_file
-        
-        
-        if args.auto or args.init:
-            temp_topic = &#39; &#39;.join(args.auto) if args.auto else &#39; &#39;.join(args.init)
-            self.topic = temp_topic
-        elif self.auto or self.init:  # Use the auto attribute if args.auto is not provided
-            self.topic = self.auto
-            
-        if args.auto or self.auto:
-            self.agent_file = &#34;test.yaml&#34;
-            generator = AutoGenerator(topic=self.topic , framework=self.framework, agent_file=self.agent_file)
-            self.agent_file = generator.generate()
-            agents_generator = AgentsGenerator(self.agent_file, self.framework, self.config_list)
-            result = agents_generator.generate_crew_and_kickoff()
-            return result
-        elif args.init or self.init:
-            self.agent_file = &#34;agents.yaml&#34;
-            generator = AutoGenerator(topic=self.topic , framework=self.framework, agent_file=self.agent_file)
-            self.agent_file = generator.generate()
-            print(&#34;File {} created successfully&#34;.format(self.agent_file))
-            return &#34;File {} created successfully&#34;.format(self.agent_file)
-        
-        if args.ui:
-            if args.ui == &#34;gradio&#34;:
-                self.create_gradio_interface()
-            elif args.ui == &#34;chainlit&#34;:
-                self.create_chainlit_interface()
-            else:
-                # Modify below code to allow default ui
-                agents_generator = AgentsGenerator(self.agent_file, self.framework, self.config_list)
-                result = agents_generator.generate_crew_and_kickoff()
-                return result
-        else:
-            agents_generator = AgentsGenerator(self.agent_file, self.framework, self.config_list)
-            result = agents_generator.generate_crew_and_kickoff()
-            return result
-            
-    def parse_args(self):
-        &#34;&#34;&#34;
-        Parse the command-line arguments for the PraisonAI CLI.
-
-        Args:
-            self (PraisonAI): An instance of the PraisonAI class.
-
-        Returns:
-            argparse.Namespace: An object containing the parsed command-line arguments.
-
-        Raises:
-            argparse.ArgumentError: If the arguments provided are invalid.
-
-        Example:
-            &gt;&gt;&gt; args = praison_ai.parse_args()
-            &gt;&gt;&gt; print(args.agent_file)  # Output: &#39;agents.yaml&#39;
-        &#34;&#34;&#34;
-        parser = argparse.ArgumentParser(prog=&#34;praisonai&#34;, description=&#34;praisonAI command-line interface&#34;)
-        parser.add_argument(&#34;--framework&#34;, choices=[&#34;crewai&#34;, &#34;autogen&#34;], help=&#34;Specify the framework&#34;)
-        parser.add_argument(&#34;--ui&#34;, choices=[&#34;chainlit&#34;, &#34;gradio&#34;], help=&#34;Specify the UI framework (gradio or chainlit).&#34;)
-        parser.add_argument(&#34;--auto&#34;, nargs=argparse.REMAINDER, help=&#34;Enable auto mode and pass arguments for it&#34;)
-        parser.add_argument(&#34;--init&#34;, nargs=argparse.REMAINDER, help=&#34;Enable auto mode and pass arguments for it&#34;)
-        parser.add_argument(&#34;agent_file&#34;, nargs=&#34;?&#34;, help=&#34;Specify the agent file&#34;)
-        parser.add_argument(&#34;--deploy&#34;, action=&#34;store_true&#34;, help=&#34;Deploy the application&#34;)  # New argument
-        args, unknown_args = parser.parse_known_args()
-
-        if unknown_args and unknown_args[0] == &#39;-b&#39; and unknown_args[1] == &#39;api:app&#39;:
-            args.agent_file = &#39;agents.yaml&#39;
-        if args.agent_file == &#39;api:app&#39; or args.agent_file == &#39;/app/api:app&#39;:
-            args.agent_file = &#39;agents.yaml&#39;
-        if args.agent_file == &#39;ui&#39;:
-            args.ui = &#39;chainlit&#39;
-
-        return args
-
-    def create_gradio_interface(self):
-        &#34;&#34;&#34;
-        Create a Gradio interface for generating agents and performing tasks.
-
-        Args:
-            self (PraisonAI): An instance of the PraisonAI class.
-
-        Returns:
-            None: This method does not return any value. It launches the Gradio interface.
-
-        Raises:
-            None: This method does not raise any exceptions.
-
-        Example:
-            &gt;&gt;&gt; praison_ai.create_gradio_interface()
-        &#34;&#34;&#34;
-        if GRADIO_AVAILABLE:
-            def generate_crew_and_kickoff_interface(auto_args, framework):
-                &#34;&#34;&#34;
-                Generate a crew and kick off tasks based on the provided auto arguments and framework.
-
-                Args:
-                    auto_args (list): Topic.
-                    framework (str): The framework to use for generating agents.
-
-                Returns:
-                    str: A string representing the result of generating the crew and kicking off tasks.
-
-                Raises:
-                    None: This method does not raise any exceptions.
-
-                Example:
-                    &gt;&gt;&gt; result = generate_crew_and_kickoff_interface(&#34;Create a movie about Cat in Mars&#34;, &#34;crewai&#34;)
-                    &gt;&gt;&gt; print(result)
-                &#34;&#34;&#34;
-                self.framework = framework
-                self.agent_file = &#34;test.yaml&#34;
-                generator = AutoGenerator(topic=auto_args , framework=self.framework)
-                self.agent_file = generator.generate()
-                agents_generator = AgentsGenerator(self.agent_file, self.framework, self.config_list)
-                result = agents_generator.generate_crew_and_kickoff()
-                return result
-
-            gr.Interface(
-                fn=generate_crew_and_kickoff_interface,
-                inputs=[gr.Textbox(lines=2, label=&#34;Auto Args&#34;), gr.Dropdown(choices=[&#34;crewai&#34;, &#34;autogen&#34;], label=&#34;Framework&#34;)],
-                outputs=&#34;textbox&#34;,
-                title=&#34;Praison AI Studio&#34;,
-                description=&#34;Create Agents and perform tasks&#34;,
-                theme=&#34;default&#34;
-            ).launch()
-        else:
-            print(&#34;ERROR: Gradio is not installed. Please install it with &#39;pip install gradio&#39; to use this feature.&#34;) 
-        
-    def create_chainlit_interface(self):
-        &#34;&#34;&#34;
-        Create a Chainlit interface for generating agents and performing tasks.
-
-        This function sets up a Chainlit application that listens for messages.
-        When a message is received, it runs PraisonAI with the provided message as the topic.
-        The generated agents are then used to perform tasks.
-
-        Returns:
-            None: This function does not return any value. It starts the Chainlit application.
-        &#34;&#34;&#34;
-        if CHAINLIT_AVAILABLE:
-            import praisonai
-            os.environ[&#34;CHAINLIT_PORT&#34;] = &#34;8082&#34;
-            # Get the path to the &#39;public&#39; folder within the package
-            public_folder = os.path.join(os.path.dirname(praisonai.__file__), &#39;public&#39;)
-            if not os.path.exists(&#34;public&#34;):  # Check if the folder exists in the current directory
-                if os.path.exists(public_folder):
-                    shutil.copytree(public_folder, &#39;public&#39;, dirs_exist_ok=True)
-                    logging.info(&#34;Public folder copied successfully!&#34;)
-                else:
-                    logging.info(&#34;Public folder not found in the package.&#34;)
-            else:
-                logging.info(&#34;Public folder already exists.&#34;)
-            chainlit_ui_path = os.path.join(os.path.dirname(praisonai.__file__), &#39;chainlit_ui.py&#39;)
-            chainlit_run([chainlit_ui_path])
-        else:
-            print(&#34;ERROR: Chainlit is not installed. Please install it with &#39;pip install chainlit&#39; to use the UI.&#34;)        </code></pre>
-</details>
-<h3>Methods</h3>
-<dl>
-<dt id="praisonai.cli.PraisonAI.create_chainlit_interface"><code class="name flex">
-<span>def <span class="ident">create_chainlit_interface</span></span>(<span>self)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Create a Chainlit interface for generating agents and performing tasks.</p>
-<p>This function sets up a Chainlit application that listens for messages.
-When a message is received, it runs PraisonAI with the provided message as the topic.
-The generated agents are then used to perform tasks.</p>
-<h2 id="returns">Returns</h2>
-<dl>
-<dt><code>None</code></dt>
-<dd>This function does not return any value. It starts the Chainlit application.</dd>
-</dl></div>
-</dd>
-<dt id="praisonai.cli.PraisonAI.create_gradio_interface"><code class="name flex">
-<span>def <span class="ident">create_gradio_interface</span></span>(<span>self)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Create a Gradio interface for generating agents and performing tasks.</p>
-<h2 id="args">Args</h2>
-<dl>
-<dt><strong><code>self</code></strong> :&ensp;<code><a title="praisonai.cli.PraisonAI" href="#praisonai.cli.PraisonAI">PraisonAI</a></code></dt>
-<dd>An instance of the PraisonAI class.</dd>
-</dl>
-<h2 id="returns">Returns</h2>
-<dl>
-<dt><code>None</code></dt>
-<dd>This method does not return any value. It launches the Gradio interface.</dd>
-</dl>
-<h2 id="raises">Raises</h2>
-<dl>
-<dt><code>None</code></dt>
-<dd>This method does not raise any exceptions.</dd>
-</dl>
-<h2 id="example">Example</h2>
-<pre><code class="language-python-repl">&gt;&gt;&gt; praison_ai.create_gradio_interface()
-</code></pre></div>
-</dd>
-<dt id="praisonai.cli.PraisonAI.main"><code class="name flex">
-<span>def <span class="ident">main</span></span>(<span>self)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>The main function of the PraisonAI object. It parses the command-line arguments,
-initializes the necessary attributes, and then calls the appropriate methods based on the
-provided arguments.</p>
-<h2 id="args">Args</h2>
-<dl>
-<dt><strong><code>self</code></strong> :&ensp;<code><a title="praisonai.cli.PraisonAI" href="#praisonai.cli.PraisonAI">PraisonAI</a></code></dt>
-<dd>An instance of the PraisonAI class.</dd>
-</dl>
-<h2 id="returns">Returns</h2>
-<dl>
-<dt><code>Any</code></dt>
-<dd>Depending on the arguments provided, the function may return a result from the</dd>
-</dl>
-<p>AgentsGenerator, a deployment result from the CloudDeployer, or a message indicating
-the successful creation of a file.</p></div>
-</dd>
-<dt id="praisonai.cli.PraisonAI.parse_args"><code class="name flex">
-<span>def <span class="ident">parse_args</span></span>(<span>self)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Parse the command-line arguments for the PraisonAI CLI.</p>
-<h2 id="args">Args</h2>
-<dl>
-<dt><strong><code>self</code></strong> :&ensp;<code><a title="praisonai.cli.PraisonAI" href="#praisonai.cli.PraisonAI">PraisonAI</a></code></dt>
-<dd>An instance of the PraisonAI class.</dd>
-</dl>
-<h2 id="returns">Returns</h2>
-<dl>
-<dt><code>argparse.Namespace</code></dt>
-<dd>An object containing the parsed command-line arguments.</dd>
-</dl>
-<h2 id="raises">Raises</h2>
-<dl>
-<dt><code>argparse.ArgumentError</code></dt>
-<dd>If the arguments provided are invalid.</dd>
-</dl>
-<h2 id="example">Example</h2>
-<pre><code class="language-python-repl">&gt;&gt;&gt; args = praison_ai.parse_args()
-&gt;&gt;&gt; print(args.agent_file)  # Output: 'agents.yaml'
-</code></pre></div>
-</dd>
-</dl>
-</dd>
-</dl>
-</section>
-</article>
-<nav id="sidebar">
-<div class="toc">
-<ul></ul>
-</div>
-<ul id="index">
-<li><h3>Super-module</h3>
-<ul>
-<li><code><a title="praisonai" href="index.html">praisonai</a></code></li>
-</ul>
-</li>
-<li><h3><a href="#header-classes">Classes</a></h3>
-<ul>
-<li>
-<h4><code><a title="praisonai.cli.PraisonAI" href="#praisonai.cli.PraisonAI">PraisonAI</a></code></h4>
-<ul class="">
-<li><code><a title="praisonai.cli.PraisonAI.create_chainlit_interface" href="#praisonai.cli.PraisonAI.create_chainlit_interface">create_chainlit_interface</a></code></li>
-<li><code><a title="praisonai.cli.PraisonAI.create_gradio_interface" href="#praisonai.cli.PraisonAI.create_gradio_interface">create_gradio_interface</a></code></li>
-<li><code><a title="praisonai.cli.PraisonAI.main" href="#praisonai.cli.PraisonAI.main">main</a></code></li>
-<li><code><a title="praisonai.cli.PraisonAI.parse_args" href="#praisonai.cli.PraisonAI.parse_args">parse_args</a></code></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
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-<footer id="footer">
-<p>Generated by <a href="https://pdoc3.github.io/pdoc" title="pdoc: Python API documentation generator"><cite>pdoc</cite> 0.11.0</a>.</p>
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docs/api/praisonai/deploy.html

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-<main>
-<article id="content">
-<header>
-<h1 class="title">Module <code>praisonai.deploy</code></h1>
-</header>
-<section id="section-intro">
-</section>
-<section>
-</section>
-<section>
-</section>
-<section>
-</section>
-<section>
-<h2 class="section-title" id="header-classes">Classes</h2>
-<dl>
-<dt id="praisonai.deploy.CloudDeployer"><code class="flex name class">
-<span>class <span class="ident">CloudDeployer</span></span>
-</code></dt>
-<dd>
-<div class="desc"><p>A class for deploying a cloud-based application.</p>
-<h2 id="attributes">Attributes</h2>
-<p>None</p>
-<h2 id="methods">Methods</h2>
-<p><strong>init</strong>(self):
-Loads environment variables from .env file or system and sets them.</p>
-<p>Loads environment variables from .env file or system and sets them.</p>
-<h2 id="parameters">Parameters</h2>
-<p>self: An instance of the CloudDeployer class.</p>
-<h2 id="returns">Returns</h2>
-<p>None</p>
-<h2 id="raises">Raises</h2>
-<p>None</p></div>
-<details class="source">
-<summary>
-<span>Expand source code</span>
-</summary>
-<pre><code class="python">class CloudDeployer:
-    &#34;&#34;&#34;
-    A class for deploying a cloud-based application.
-
-    Attributes:
-        None
-
-    Methods:
-        __init__(self):
-            Loads environment variables from .env file or system and sets them.
-
-    &#34;&#34;&#34;
-    def __init__(self):
-        &#34;&#34;&#34;
-        Loads environment variables from .env file or system and sets them.
-
-        Parameters:
-            self: An instance of the CloudDeployer class.
-
-        Returns:
-            None
-
-        Raises:
-            None
-
-        &#34;&#34;&#34;
-        # Load environment variables from .env file or system
-        load_dotenv()
-        self.set_environment_variables()
-
-    def create_dockerfile(self):
-        &#34;&#34;&#34;
-        Creates a Dockerfile for the application.
-
-        Parameters:
-            self: An instance of the CloudDeployer class.
-
-        Returns:
-            None
-
-        Raises:
-            None
-
-        This method creates a Dockerfile in the current directory with the specified content.
-        The Dockerfile is used to build a Docker image for the application.
-        The content of the Dockerfile includes instructions to use the Python 3.11-slim base image,
-        set the working directory to /app, copy the current directory contents into the container,
-        install the required Python packages (flask, praisonai, gunicorn, and markdown),
-        expose port 8080, and run the application using Gunicorn.
-        &#34;&#34;&#34;
-        with open(&#34;Dockerfile&#34;, &#34;w&#34;) as file:
-            file.write(&#34;FROM python:3.11-slim\n&#34;)
-            file.write(&#34;WORKDIR /app\n&#34;)
-            file.write(&#34;COPY . .\n&#34;)
-            file.write(&#34;RUN pip install flask praisonai==0.0.36 gunicorn markdown\n&#34;)
-            file.write(&#34;EXPOSE 8080\n&#34;)
-            file.write(&#39;CMD [&#34;gunicorn&#34;, &#34;-b&#34;, &#34;0.0.0.0:8080&#34;, &#34;api:app&#34;]\n&#39;)
-            
-    def create_api_file(self):
-        &#34;&#34;&#34;
-        Creates an API file for the application.
-
-        Parameters:
-            self (CloudDeployer): An instance of the CloudDeployer class.
-
-        Returns:
-            None
-
-        This method creates an API file named &#34;api.py&#34; in the current directory. The file contains a basic Flask application that uses the PraisonAI library to run a simple agent and returns the output as an HTML page. The application listens on the root path (&#34;/&#34;) and uses the Markdown library to format the output.
-        &#34;&#34;&#34;
-        with open(&#34;api.py&#34;, &#34;w&#34;) as file:
-            file.write(&#34;from flask import Flask\n&#34;)
-            file.write(&#34;from praisonai import PraisonAI\n&#34;)
-            file.write(&#34;import markdown\n\n&#34;)
-            file.write(&#34;app = Flask(__name__)\n\n&#34;)
-            file.write(&#34;def basic():\n&#34;)
-            file.write(&#34;    praison_ai = PraisonAI(agent_file=\&#34;agents.yaml\&#34;)\n&#34;)
-            file.write(&#34;    return praison_ai.main()\n\n&#34;)
-            file.write(&#34;@app.route(&#39;/&#39;)\n&#34;)
-            file.write(&#34;def home():\n&#34;)
-            file.write(&#34;    output = basic()\n&#34;)
-            file.write(&#34;    html_output = markdown.markdown(output)\n&#34;)
-            file.write(&#34;    return f&#39;&lt;html&gt;&lt;body&gt;{html_output}&lt;/body&gt;&lt;/html&gt;&#39;\n\n&#34;)
-            file.write(&#34;if __name__ == \&#34;__main__\&#34;:\n&#34;)
-            file.write(&#34;    app.run(debug=True)\n&#34;)
-    
-    def set_environment_variables(self):
-        &#34;&#34;&#34;Sets environment variables with fallback to .env values or defaults.&#34;&#34;&#34;
-        os.environ[&#34;OPENAI_MODEL_NAME&#34;] = os.getenv(&#34;OPENAI_MODEL_NAME&#34;, &#34;gpt-4o&#34;)
-        os.environ[&#34;OPENAI_API_KEY&#34;] = os.getenv(&#34;OPENAI_API_KEY&#34;, &#34;Enter your API key&#34;)
-        os.environ[&#34;OPENAI_API_BASE&#34;] = os.getenv(&#34;OPENAI_API_BASE&#34;, &#34;https://api.openai.com/v1&#34;)
-
-    def run_commands(self):
-        &#34;&#34;&#34;
-        Sets environment variables with fallback to .env values or defaults.
-
-        Parameters:
-            None
-
-        Returns:
-            None
-
-        Raises:
-            None
-
-        This method sets environment variables for the application. It uses the `os.environ` dictionary to set the following environment variables:
-
-        - `OPENAI_MODEL_NAME`: The name of the OpenAI model to use. If not specified in the .env file, it defaults to &#34;gpt-4o&#34;.
-        - `OPENAI_API_KEY`: The API key for accessing the OpenAI API. If not specified in the .env file, it defaults to &#34;Enter your API key&#34;.
-        - `OPENAI_API_BASE`: The base URL for the OpenAI API. If not specified in the .env file, it defaults to &#34;https://api.openai.com/v1&#34;.
-        &#34;&#34;&#34;
-        self.create_api_file()
-        self.create_dockerfile()
-        &#34;&#34;&#34;Runs a sequence of shell commands for deployment, continues on error.&#34;&#34;&#34;
-        commands = [
-            &#34;yes | gcloud auth configure-docker us-central1-docker.pkg.dev&#34;,
-            &#34;gcloud artifacts repositories create praisonai-repository --repository-format=docker --location=us-central1&#34;,
-            &#34;docker build --platform linux/amd64 -t gcr.io/$(gcloud config get-value project)/praisonai-app:latest .&#34;,
-            &#34;docker tag gcr.io/$(gcloud config get-value project)/praisonai-app:latest us-central1-docker.pkg.dev/$(gcloud config get-value project)/praisonai-repository/praisonai-app:latest&#34;,
-            &#34;docker push us-central1-docker.pkg.dev/$(gcloud config get-value project)/praisonai-repository/praisonai-app:latest&#34;,
-            &#34;gcloud run deploy praisonai-service --image us-central1-docker.pkg.dev/$(gcloud config get-value project)/praisonai-repository/praisonai-app:latest --platform managed --region us-central1 --allow-unauthenticated --set-env-vars OPENAI_MODEL_NAME=${OPENAI_MODEL_NAME},OPENAI_API_KEY=${OPENAI_API_KEY},OPENAI_API_BASE=${OPENAI_API_BASE}&#34;
-        ]
-
-        for cmd in commands:
-            try:
-                subprocess.run(cmd, shell=True, check=True)
-            except subprocess.CalledProcessError as e:
-                print(f&#34;ERROR: Command &#39;{e.cmd}&#39; failed with exit status {e.returncode}&#34;)
-                print(f&#34;Continuing with the next command...&#34;)</code></pre>
-</details>
-<h3>Methods</h3>
-<dl>
-<dt id="praisonai.deploy.CloudDeployer.create_api_file"><code class="name flex">
-<span>def <span class="ident">create_api_file</span></span>(<span>self)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Creates an API file for the application.</p>
-<h2 id="parameters">Parameters</h2>
-<p>self (CloudDeployer): An instance of the CloudDeployer class.</p>
-<h2 id="returns">Returns</h2>
-<p>None
-This method creates an API file named "api.py" in the current directory. The file contains a basic Flask application that uses the PraisonAI library to run a simple agent and returns the output as an HTML page. The application listens on the root path ("/") and uses the Markdown library to format the output.</p></div>
-</dd>
-<dt id="praisonai.deploy.CloudDeployer.create_dockerfile"><code class="name flex">
-<span>def <span class="ident">create_dockerfile</span></span>(<span>self)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Creates a Dockerfile for the application.</p>
-<h2 id="parameters">Parameters</h2>
-<p>self: An instance of the CloudDeployer class.</p>
-<h2 id="returns">Returns</h2>
-<p>None</p>
-<h2 id="raises">Raises</h2>
-<p>None
-This method creates a Dockerfile in the current directory with the specified content.
-The Dockerfile is used to build a Docker image for the application.
-The content of the Dockerfile includes instructions to use the Python 3.11-slim base image,
-set the working directory to /app, copy the current directory contents into the container,
-install the required Python packages (flask, praisonai, gunicorn, and markdown),
-expose port 8080, and run the application using Gunicorn.</p></div>
-</dd>
-<dt id="praisonai.deploy.CloudDeployer.run_commands"><code class="name flex">
-<span>def <span class="ident">run_commands</span></span>(<span>self)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Sets environment variables with fallback to .env values or defaults.</p>
-<h2 id="parameters">Parameters</h2>
-<p>None</p>
-<h2 id="returns">Returns</h2>
-<p>None</p>
-<h2 id="raises">Raises</h2>
-<p>None
-This method sets environment variables for the application. It uses the <code>os.environ</code> dictionary to set the following environment variables:</p>
-<ul>
-<li><code>OPENAI_MODEL_NAME</code>: The name of the OpenAI model to use. If not specified in the .env file, it defaults to "gpt-4o".</li>
-<li><code>OPENAI_API_KEY</code>: The API key for accessing the OpenAI API. If not specified in the .env file, it defaults to "Enter your API key".</li>
-<li><code>OPENAI_API_BASE</code>: The base URL for the OpenAI API. If not specified in the .env file, it defaults to "https://api.openai.com/v1".</li>
-</ul></div>
-</dd>
-<dt id="praisonai.deploy.CloudDeployer.set_environment_variables"><code class="name flex">
-<span>def <span class="ident">set_environment_variables</span></span>(<span>self)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Sets environment variables with fallback to .env values or defaults.</p></div>
-</dd>
-</dl>
-</dd>
-</dl>
-</section>
-</article>
-<nav id="sidebar">
-<div class="toc">
-<ul></ul>
-</div>
-<ul id="index">
-<li><h3>Super-module</h3>
-<ul>
-<li><code><a title="praisonai" href="index.html">praisonai</a></code></li>
-</ul>
-</li>
-<li><h3><a href="#header-classes">Classes</a></h3>
-<ul>
-<li>
-<h4><code><a title="praisonai.deploy.CloudDeployer" href="#praisonai.deploy.CloudDeployer">CloudDeployer</a></code></h4>
-<ul class="">
-<li><code><a title="praisonai.deploy.CloudDeployer.create_api_file" href="#praisonai.deploy.CloudDeployer.create_api_file">create_api_file</a></code></li>
-<li><code><a title="praisonai.deploy.CloudDeployer.create_dockerfile" href="#praisonai.deploy.CloudDeployer.create_dockerfile">create_dockerfile</a></code></li>
-<li><code><a title="praisonai.deploy.CloudDeployer.run_commands" href="#praisonai.deploy.CloudDeployer.run_commands">run_commands</a></code></li>
-<li><code><a title="praisonai.deploy.CloudDeployer.set_environment_variables" href="#praisonai.deploy.CloudDeployer.set_environment_variables">set_environment_variables</a></code></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</nav>
-</main>
-<footer id="footer">
-<p>Generated by <a href="https://pdoc3.github.io/pdoc" title="pdoc: Python API documentation generator"><cite>pdoc</cite> 0.11.0</a>.</p>
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-</html>

docs/api/praisonai/inbuilt_tools/autogen_tools.html

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-<main>
-<article id="content">
-<header>
-<h1 class="title">Module <code>praisonai.inbuilt_tools.autogen_tools</code></h1>
-</header>
-<section id="section-intro">
-</section>
-<section>
-</section>
-<section>
-</section>
-<section>
-<h2 class="section-title" id="header-functions">Functions</h2>
-<dl>
-<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_BaseTool"><code class="name flex">
-<span>def <span class="ident">autogen_BaseTool</span></span>(<span>assistant, user_proxy)</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_CSVSearchTool"><code class="name flex">
-<span>def <span class="ident">autogen_CSVSearchTool</span></span>(<span>assistant, user_proxy)</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_CodeDocsSearchTool"><code class="name flex">
-<span>def <span class="ident">autogen_CodeDocsSearchTool</span></span>(<span>assistant, user_proxy)</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_DDGS"><code class="name flex">
-<span>def <span class="ident">autogen_DDGS</span></span>(<span>assistant, user_proxy)</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_DOCXSearchTool"><code class="name flex">
-<span>def <span class="ident">autogen_DOCXSearchTool</span></span>(<span>assistant, user_proxy)</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_DirectoryReadTool"><code class="name flex">
-<span>def <span class="ident">autogen_DirectoryReadTool</span></span>(<span>assistant, user_proxy)</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_DirectorySearchTool"><code class="name flex">
-<span>def <span class="ident">autogen_DirectorySearchTool</span></span>(<span>assistant, user_proxy)</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_FileReadTool"><code class="name flex">
-<span>def <span class="ident">autogen_FileReadTool</span></span>(<span>assistant, user_proxy)</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_InternetSearchTool"><code class="name flex">
-<span>def <span class="ident">autogen_InternetSearchTool</span></span>(<span>assistant, user_proxy)</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_JSONSearchTool"><code class="name flex">
-<span>def <span class="ident">autogen_JSONSearchTool</span></span>(<span>assistant, user_proxy)</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_MDXSearchTool"><code class="name flex">
-<span>def <span class="ident">autogen_MDXSearchTool</span></span>(<span>assistant, user_proxy)</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_PDFSearchTool"><code class="name flex">
-<span>def <span class="ident">autogen_PDFSearchTool</span></span>(<span>assistant, user_proxy)</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_RagTool"><code class="name flex">
-<span>def <span class="ident">autogen_RagTool</span></span>(<span>assistant, user_proxy)</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_ScrapeElementFromWebsiteTool"><code class="name flex">
-<span>def <span class="ident">autogen_ScrapeElementFromWebsiteTool</span></span>(<span>assistant, user_proxy)</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_ScrapeWebsiteTool"><code class="name flex">
-<span>def <span class="ident">autogen_ScrapeWebsiteTool</span></span>(<span>assistant, user_proxy)</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_TXTSearchTool"><code class="name flex">
-<span>def <span class="ident">autogen_TXTSearchTool</span></span>(<span>assistant, user_proxy)</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_WebsiteSearchTool"><code class="name flex">
-<span>def <span class="ident">autogen_WebsiteSearchTool</span></span>(<span>assistant, user_proxy)</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_XMLSearchTool"><code class="name flex">
-<span>def <span class="ident">autogen_XMLSearchTool</span></span>(<span>assistant, user_proxy)</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_YoutubeChannelSearchTool"><code class="name flex">
-<span>def <span class="ident">autogen_YoutubeChannelSearchTool</span></span>(<span>assistant, user_proxy)</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_YoutubeVideoSearchTool"><code class="name flex">
-<span>def <span class="ident">autogen_YoutubeVideoSearchTool</span></span>(<span>assistant, user_proxy)</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.inbuilt_tools.autogen_tools.create_autogen_tool_function"><code class="name flex">
-<span>def <span class="ident">create_autogen_tool_function</span></span>(<span>tool_name)</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-</dl>
-</section>
-<section>
-</section>
-</article>
-<nav id="sidebar">
-<div class="toc">
-<ul></ul>
-</div>
-<ul id="index">
-<li><h3>Super-module</h3>
-<ul>
-<li><code><a title="praisonai.inbuilt_tools" href="index.html">praisonai.inbuilt_tools</a></code></li>
-</ul>
-</li>
-<li><h3><a href="#header-functions">Functions</a></h3>
-<ul class="">
-<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_BaseTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_BaseTool">autogen_BaseTool</a></code></li>
-<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_CSVSearchTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_CSVSearchTool">autogen_CSVSearchTool</a></code></li>
-<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_CodeDocsSearchTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_CodeDocsSearchTool">autogen_CodeDocsSearchTool</a></code></li>
-<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_DDGS" href="#praisonai.inbuilt_tools.autogen_tools.autogen_DDGS">autogen_DDGS</a></code></li>
-<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_DOCXSearchTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_DOCXSearchTool">autogen_DOCXSearchTool</a></code></li>
-<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_DirectoryReadTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_DirectoryReadTool">autogen_DirectoryReadTool</a></code></li>
-<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_DirectorySearchTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_DirectorySearchTool">autogen_DirectorySearchTool</a></code></li>
-<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_FileReadTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_FileReadTool">autogen_FileReadTool</a></code></li>
-<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_InternetSearchTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_InternetSearchTool">autogen_InternetSearchTool</a></code></li>
-<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_JSONSearchTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_JSONSearchTool">autogen_JSONSearchTool</a></code></li>
-<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_MDXSearchTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_MDXSearchTool">autogen_MDXSearchTool</a></code></li>
-<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_PDFSearchTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_PDFSearchTool">autogen_PDFSearchTool</a></code></li>
-<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_RagTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_RagTool">autogen_RagTool</a></code></li>
-<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_ScrapeElementFromWebsiteTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_ScrapeElementFromWebsiteTool">autogen_ScrapeElementFromWebsiteTool</a></code></li>
-<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_ScrapeWebsiteTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_ScrapeWebsiteTool">autogen_ScrapeWebsiteTool</a></code></li>
-<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_TXTSearchTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_TXTSearchTool">autogen_TXTSearchTool</a></code></li>
-<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_WebsiteSearchTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_WebsiteSearchTool">autogen_WebsiteSearchTool</a></code></li>
-<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_XMLSearchTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_XMLSearchTool">autogen_XMLSearchTool</a></code></li>
-<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_YoutubeChannelSearchTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_YoutubeChannelSearchTool">autogen_YoutubeChannelSearchTool</a></code></li>
-<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_YoutubeVideoSearchTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_YoutubeVideoSearchTool">autogen_YoutubeVideoSearchTool</a></code></li>
-<li><code><a title="praisonai.inbuilt_tools.autogen_tools.create_autogen_tool_function" href="#praisonai.inbuilt_tools.autogen_tools.create_autogen_tool_function">create_autogen_tool_function</a></code></li>
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-<section id="section-intro">
-</section>
-<section>
-</section>
-<section>
-</section>
-<section>
-<h2 class="section-title" id="header-functions">Functions</h2>
-<dl>
-<dt id="praisonai.test.generate_crew_and_kickoff"><code class="name flex">
-<span>def <span class="ident">generate_crew_and_kickoff</span></span>(<span>agent_file)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>This function generates a crew of agents and kicks off tasks based on the configuration provided in a YAML file.</p>
-<p>Parameters:
-agent_file (str): The path to the YAML file containing the configuration for the agents and tasks.</p>
-<p>Returns:
-str: The result of the last task executed by the crew.</p></div>
-</dd>
-</dl>
-</section>
-<section>
-</section>
-</article>
-<nav id="sidebar">
-<div class="toc">
-<ul></ul>
-</div>
-<ul id="index">
-<li><h3>Super-module</h3>
-<ul>
-<li><code><a title="praisonai" href="index.html">praisonai</a></code></li>
-</ul>
-</li>
-<li><h3><a href="#header-functions">Functions</a></h3>
-<ul class="">
-<li><code><a title="praisonai.test.generate_crew_and_kickoff" href="#praisonai.test.generate_crew_and_kickoff">generate_crew_and_kickoff</a></code></li>
-</ul>
-</li>
-</ul>
-</nav>
-</main>
-<footer id="footer">
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docs/api/praisonai/version.html

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-<title>praisonai.version API documentation</title>
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-<style media="screen and (min-width: 700px)">@media screen and (min-width:700px){#sidebar{width:30%;height:100vh;overflow:auto;position:sticky;top:0}#content{width:70%;max-width:100ch;padding:3em 4em;border-left:1px solid #ddd}pre code{font-size:1em}.name{font-size:1em}main{display:flex;flex-direction:row-reverse;justify-content:flex-end}.toc ul ul,#index ul ul{padding-left:1em}.toc > ul > li{margin-top:.5em}}</style>
-<style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
-<script defer src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.9.0/highlight.min.js" integrity="sha512-D9gUyxqja7hBtkWpPWGt9wfbfaMGVt9gnyCvYa+jojwwPHLCzUm5i8rpk7vD7wNee9bA35eYIjobYPaQuKS1MQ==" crossorigin></script>
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-hljs.highlightAll();
-})</script>
-</head>
-<body>
-<main>
-<article id="content">
-<header>
-<h1 class="title">Module <code>praisonai.version</code></h1>
-</header>
-<section id="section-intro">
-</section>
-<section>
-</section>
-<section>
-</section>
-<section>
-</section>
-<section>
-</section>
-</article>
-<nav id="sidebar">
-<div class="toc">
-<ul></ul>
-</div>
-<ul id="index">
-<li><h3>Super-module</h3>
-<ul>
-<li><code><a title="praisonai" href="index.html">praisonai</a></code></li>
-</ul>
-</li>
-</ul>
-</nav>
-</main>
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-</footer>
-</body>
-</html>

docs/auto.md

@@ -1,6 +0,0 @@
-# Auto
-## Full Automatic Mode
-
-```bash
-praisonai --auto create a movie script about Dog in Moon
-```

docs/conf.py

@@ -1,28 +0,0 @@
-# Configuration file for the Sphinx documentation builder.
-#
-# For the full list of built-in configuration values, see the documentation:
-# https://www.sphinx-doc.org/en/master/usage/configuration.html
-
-# -- Project information -----------------------------------------------------
-# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
-
-project = 'PraisonAI'
-copyright = '2024, Mervin Praison'
-author = 'Mervin Praison'
-release = '7'
-
-# -- General configuration ---------------------------------------------------
-# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
-
-extensions = ['sphinx.ext.autodoc', 'sphinx.ext.napoleon', 'sphinx_rtd_theme']
-
-templates_path = ['_templates']
-exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
-
-
-
-# -- Options for HTML output -------------------------------------------------
-# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
-
-html_theme = 'sphinx_rtd_theme'
-html_static_path = ['_static']

docs/contributing.md

@@ -1,10 +0,0 @@
-# Contributing
-
-- Fork on GitHub: Use the "Fork" button on the repository page.
-- Clone your fork: `git clone https://github.com/yourusername/praisonAI.git`
-- Create a branch: `git checkout -b new-feature`
-- Make changes and commit: `git commit -am "Add some feature"`
-- Push to your fork: `git push origin new-feature`
-- Submit a pull request via GitHub's web interface.
-- Await feedback from project maintainers.
-

docs/custom_tools.md

@@ -1,135 +0,0 @@
-# Create Custom Tools
-
-Sure! Let's go through the steps to install and set up the PraisonAI tool.
-
-## Step 1: Install the `praisonai` Package
-
-First, you need to install the `praisonai` package. Open your terminal and run the following command:
-
-```bash
-pip install praisonai
-```
-
-## Step 2: Create the `InternetSearchTool`
-
-Next, create a file named `tools.py` and add the following code to define the `InternetSearchTool`:
-
-```python
-from duckduckgo_search import DDGS
-from praisonai_tools import BaseTool
-
-class InternetSearchTool(BaseTool):
-    name: str = "Internet Search Tool"
-    description: str = "Search Internet for relevant information based on a query or latest news"
-
-    def _run(self, query: str):
-        ddgs = DDGS()
-        results = ddgs.text(keywords=query, region='wt-wt', safesearch='moderate', max_results=5)
-        return results
-```
-
-## Step 3: Define the Agent Configuration
-
-Create a file named `agents.yaml` and add the following content to configure the agent:
-
-```yaml
-framework: crewai
-topic: research about the causes of lung disease
-roles:
-  research_analyst:
-    backstory: Experienced in analyzing scientific data related to respiratory health.
-    goal: Analyze data on lung diseases
-    role: Research Analyst
-    tasks:
-      data_analysis:
-        description: Gather and analyze data on the causes and risk factors of lung diseases.
-        expected_output: Report detailing key findings on lung disease causes.
-    tools:
-    - InternetSearchTool
-```
-
-## Step 4: Run the PraisonAI Tool
-
-To run the PraisonAI tool, simply type the following command in your terminal:
-
-```bash
-praisonai
-```
-
-If you want to run the `autogen` framework, use:
-
-```bash
-praisonai --framework autogen
-```
-
-## Prerequisites
-
-Ensure you have the `duckduckgo_search` package installed. If not, you can install it using:
-
-```bash
-pip install duckduckgo_search
-```
-
-That's it! You should now have the PraisonAI tool installed and configured.
-
-## Other information
-
-### TL;DR to Create a Custom Tool
-
-```bash
-pip install praisonai duckduckgo-search
-export OPENAI_API_KEY="Enter your API key"
-praisonai --init research about the latest AI News and prepare a detailed report
-```
-
-- Add `- InternetSearchTool` in the agents.yaml file in the tools section. 
-- Create a file called tools.py and add this code [tools.py](./tools.py)
-
-```bash
-praisonai
-```
-
-### Pre-requisite to Create a Custom Tool
-`agents.yaml` file should be present in the current directory. 
-
-If it doesn't exist, create it by running the command `praisonai --init research about the latest AI News and prepare a detailed report`.
-
-#### Step 1 to Create a Custom Tool
-
-Create a file called tools.py in the same directory as the agents.yaml file.
-
-```python
-# example tools.py
-from duckduckgo_search import DDGS
-from praisonai_tools import BaseTool
-
-class InternetSearchTool(BaseTool):
-    name: str = "InternetSearchTool"
-    description: str = "Search Internet for relevant information based on a query or latest news"
-
-    def _run(self, query: str):
-        ddgs = DDGS()
-        results = ddgs.text(keywords=query, region='wt-wt', safesearch='moderate', max_results=5)
-        return results
-```
-
-#### Step 2 to Create a Custom Tool
-
-Add the tool to the agents.yaml file as show below under the tools section `- InternetSearchTool`.
-
-```yaml
-framework: crewai
-topic: research about the latest AI News and prepare a detailed report
-roles:
-  research_analyst:
-    backstory: Experienced in gathering and analyzing data related to AI news trends.
-    goal: Analyze AI News trends
-    role: Research Analyst
-    tasks:
-      gather_data:
-        description: Conduct in-depth research on the latest AI News trends from reputable
-          sources.
-        expected_output: Comprehensive report on current AI News trends.
-    tools:
-    - InternetSearchTool
-```

docs/deploy.md

@@ -1,31 +0,0 @@
-# Deploy 
-
-## Google Cloud
-
-```bash
-gcloud init
-gcloud services enable run.googleapis.com
-gcloud services enable containerregistry.googleapis.com
-gcloud services enable cloudbuild.googleapis.com
-
-export OPENAI_MODEL_NAME="gpt-4o"
-export OPENAI_API_KEY="Enter your API key"
-export OPENAI_API_BASE="https://api.openai.com/v1"
-
-yes | gcloud auth configure-docker us-central1-docker.pkg.dev 
-gcloud artifacts repositories create praisonai-repository --repository-format=docker --location=us-central1
-
-PROJECT_ID=$(gcloud config get-value project)
-TAG="latest"
-docker build --platform linux/amd64 -t gcr.io/${PROJECT_ID}/praisonai-app:${TAG} .
-docker tag gcr.io/${PROJECT_ID}/praisonai-app:${TAG} us-central1-docker.pkg.dev/${PROJECT_ID}/praisonai-repository/praisonai-app:${TAG}
-docker push us-central1-docker.pkg.dev/${PROJECT_ID}/praisonai-repository/praisonai-app:${TAG}
-
-gcloud run deploy praisonai-service \
-    --image us-central1-docker.pkg.dev/${PROJECT_ID}/praisonai-repository/praisonai-app:${TAG} \
-    --platform managed \
-    --region us-central1 \
-    --allow-unauthenticated \
-    --set-env-vars OPENAI_MODEL_NAME=${OPENAI_MODEL_NAME},OPENAI_API_KEY=${OPENAI_API_KEY},OPENAI_API_BASE=${OPENAI_API_BASE}
-```
-

docs/duckduckgo.md

@@ -1,22 +0,0 @@
-# DuckDuckGo Praison AI Integration
-
-```bash
-pip install duckduckgo_search
-```
-
-Create a file called tools.py in the same directory as the agents.yaml file.
-
-```python
-# example tools.py
-from duckduckgo_search import DDGS
-from praisonai_tools import BaseTool
-
-class InternetSearchTool(BaseTool):
-    name: str = "InternetSearchTool"
-    description: str = "Search Internet for relevant information based on a query or latest news"
-
-    def _run(self, query: str):
-        ddgs = DDGS()
-        results = ddgs.text(keywords=query, region='wt-wt', safesearch='moderate', max_results=5)
-        return results
-```

docs/firecrawl.md

@@ -1,42 +0,0 @@
-# Firecrawl PraisonAI Integration
-
-## Firecrawl running in Localhost:3002
-
-```python
-from firecrawl import FirecrawlApp
-from praisonai_tools import BaseTool
-import re
-
-class WebPageScraperTool(BaseTool):
-    name: str = "Web Page Scraper Tool"
-    description: str = "Scrape and extract information from a given web page URL."
-
-    def _run(self, url: str) -> str:
-        app = FirecrawlApp(api_url='http://localhost:3002')
-        response = app.scrape_url(url=url)
-        content = response["content"]
-        # Remove all content above the line "========================================================"
-        if "========================================================" in content:
-            content = content.split("========================================================", 1)[1]
-
-        # Remove all menu items and similar patterns
-        content = re.sub(r'\*\s+\[.*?\]\(.*?\)', '', content)
-        content = re.sub(r'\[Skip to the content\]\(.*?\)', '', content)
-        content = re.sub(r'\[.*?\]\(.*?\)', '', content)
-        content = re.sub(r'\s*Menu\s*', '', content)
-        content = re.sub(r'\s*Search\s*', '', content)
-        content = re.sub(r'Categories\s*', '', content)
-
-        # Remove all URLs
-        content = re.sub(r'http\S+', '', content)
-        
-        # Remove empty lines or lines with only whitespace
-        content = '\n'.join([line for line in content.split('\n') if line.strip()])
-
-        # Limit to the first 1000 words
-        words = content.split()
-        if len(words) > 1000:
-            content = ' '.join(words[:1000])
-        
-        return content
-```

docs/generate_docs.sh

@@ -1,11 +0,0 @@
-#!/bin/sh
-set -e  # Exit if any command fails
-
-# Install necessary packages
-pip install pdoc3 mkdocs mkdocs-material 
-
-# Generate HTML documentation with pdoc
-pdoc --html --skip-errors -o docs/api praisonai --force
-
-# Build the MkDocs site
-mkdocs build

docs/generate_from_readme.sh

@@ -1,64 +0,0 @@
-#!/bin/bash
-
-# Ensure we are in the docs folder
-cd "$(dirname "$0")"
-
-# Define the sections and their corresponding filenames
-sections=(
-  "Praison AI:index.md"
-  "TL;DR:tldr.md"
-  "Installation:installation.md"
-  "Initialise:initialise.md"
-  "Run:run.md"
-  "Create Custom Tools:create_custom_tools.md"
-  "Test:test.md"
-  "Agents Playbook:agents_playbook.md"
-  "Include praisonai package in your project:include_package.md"
-  "Deploy:deploy.md"
-  "Other Models:other_models.md"
-  "Contributing:contributing.md"
-)
-
-# Function to extract section content
-extract_section() {
-  section_name="$1"
-  input_file="$2"
-  output_file="$3"
-  
-  # Escape special characters in section name for use in regex
-  section_name_escaped=$(echo "$section_name" | sed 's/[.[\*^$(){}?+|]/\\&/g')
-  
-  # Extract content
-  if command -v gsed > /dev/null; then
-    gsed -n "/## $section_name_escaped/,/## /{p;}" "$input_file" | gsed '$d' >> "$output_file"
-  else
-    sed -n "/## $section_name_escaped/,/## /{p;}" "$input_file" | sed '$d' >> "$output_file"
-  fi
-}
-
-# Extract content above the first section from README.md
-if command -v gsed > /dev/null; then
-  gsed -n '1,/^## /{p;}' ../README.md | gsed '$d' > index.md
-else
-  sed -n '1,/^## /{p;}' ../README.md | sed '$d' > index.md
-fi
-
-# Create the files and add content
-for section in "${sections[@]}"; do
-  section_name="${section%%:*}"
-  filename="${section##*:}"
-  if [[ "$filename" == "index.md" ]]; then
-    extract_section "$section_name" ../README.md index.md
-  elif [[ "$section_name" == "TL;DR" ]]; then
-    extract_section "$section_name" ../README.md index.md
-  else
-    extract_section "$section_name" ../README.md "$filename"
-  fi
-done
-
-# Special handling for the last section (Contributing)
-if command -v gsed > /dev/null; then
-  gsed -n "/## Contributing/,\$p" ../README.md > "contributing.md"
-else
-  sed -n "/## Contributing/,\$p" ../README.md > "contributing.md"
-fi

docs/home.md

@@ -1,52 +0,0 @@
-# Home
-
-## Installation
-
-```bash
-pip install praisonai
-```
-
-## Initialise
-
-```bash
-export OPENAI_API_KEY="Enter your API key"
-```
-
-Generate your OPENAI API KEY from here: https://platform.openai.com/api-keys
-
-Note: You can use other providers such as Ollama, Mistral ... etc. Details are provided at the bottom.
-    
-```bash
-praisonai --init create a movie script about dog in moon
-```
-This will automatically create agents.yaml file in the current directory.
-
-### To initialse with a specific agent framework (Optional):
-
-```bash
-praisonai --framework autogen --init create movie script about cat in mars
-```
-
-## Run
-
-```bash
-praisonai
-```
-
-or 
-    
-```bash
-python -m praisonai
-```
-
-### Specify the agent framework (Optional):
-
-```bash
-praisonai --framework autogen
-```
-
-### Full Automatic Mode
-
-```bash
-praisonai --auto create a movie script about Dog in Moon
-```

docs/index.md

@@ -1,26 +0,0 @@
-# Praison AI
-
-Praison AI, leveraging both AutoGen and CrewAI or any other agent framework, represents a low-code, centralised framework designed to simplify the creation and orchestration of multi-agent systems for various LLM applications, emphasizing ease of use, customization, and human-agent interaction.
-
-## Prerequisite:
-### Export API KEY
-
-```bash
-export OPENAI_API_KEY="Enter your API key"
-```
-
-## Installation
-
-```bash
-pip install praisonai
-```
-
-## Automatically Create Agents to Perform a Task
-
-```bash
-praisonai --init create a movie script about dog in moon
-```
-## Run
-```bash
-praisonai
-```

docs/index.rst

@@ -1,20 +0,0 @@
-.. PraisonAI documentation master file, created by
-   sphinx-quickstart on Mon Jun 10 12:11:57 2024.
-   You can adapt this file completely to your liking, but it should at least
-   contain the root `toctree` directive.
-
-Welcome to PraisonAI's documentation!
-=====================================
-
-.. toctree::
-   :maxdepth: 2
-   :caption: Contents:
-
-
-
-Indices and tables
-==================
-
-* :ref:`genindex`
-* :ref:`modindex`
-* :ref:`search`

docs/initialise.md

@@ -1,14 +0,0 @@
-# Initialise
-
-```bash
-export OPENAI_API_KEY="Enter your API key"
-```
-
-Generate your OPENAI API KEY from here: https://platform.openai.com/api-keys
-
-Note: You can use other providers such as Ollama, Mistral ... etc. Details are provided at the bottom.
-    
-```bash
-praisonai --init create a movie script about dog in moon
-```
-This will automatically create agents.yaml file in the current directory.

docs/installation.md

@@ -1,5 +0,0 @@
-# Installation
-
-```bash
-pip install praisonai
-```

docs/langchain.md

@@ -1,69 +0,0 @@
-# Langchain Tools
-
-## Integrate Langchain Direct Tools
-
-```bash
-pip install youtube_search praisonai langchain_community langchain
-```
-
-```python
-# tools.py
-from langchain_community.tools import YouTubeSearchTool
-```
-
-```yaml
-# agents.yaml
-framework: crewai
-topic: research about the causes of lung disease
-roles:
-  research_analyst:
-    backstory: Experienced in analyzing scientific data related to respiratory health.
-    goal: Analyze data on lung diseases
-    role: Research Analyst
-    tasks:
-      data_analysis:
-        description: Gather and analyze data on the causes and risk factors of lung
-          diseases.
-        expected_output: Report detailing key findings on lung disease causes.
-    tools:
-    - 'YouTubeSearchTool'
-```
-
-## Integrate Langchain with Wrappers
-
-```bash
-pip install wikipedia langchain_community
-```
-
-```python
-# tools.py
-from langchain_community.utilities import WikipediaAPIWrapper
-class WikipediaSearchTool(BaseTool):
-    name: str = "WikipediaSearchTool"
-    description: str = "Search Wikipedia for relevant information based on a query."
-
-    def _run(self, query: str):
-        api_wrapper = WikipediaAPIWrapper(top_k_results=4, doc_content_chars_max=100)
-        results = api_wrapper.load(query=query)
-        return results
-```
-
-```yaml
-# agents.yaml
-framework: crewai
-topic: research about nvidia growth
-roles:
-  data_collector:
-    backstory: An experienced researcher with the ability to efficiently collect and
-      organize vast amounts of data.
-    goal: Gather information on Nvidia's growth by providing the Ticket Symbol to YahooFinanceNewsTool
-    role: Data Collector
-    tasks:
-      data_collection_task:
-        description: Collect data on Nvidia's growth from various sources such as
-          financial reports, news articles, and company announcements.
-        expected_output: A comprehensive document detailing data points on Nvidia's
-          growth over the years.
-    tools:
-    - 'WikipediaSearchTool'
-```

docs/make.bat

@@ -1,35 +0,0 @@
-@ECHO OFF
-
-pushd %~dp0
-
-REM Command file for Sphinx documentation
-
-if "%SPHINXBUILD%" == "" (
-	set SPHINXBUILD=sphinx-build
-)
-set SOURCEDIR=.
-set BUILDDIR=_build
-
-%SPHINXBUILD% >NUL 2>NUL
-if errorlevel 9009 (
-	echo.
-	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
-	echo.installed, then set the SPHINXBUILD environment variable to point
-	echo.to the full path of the 'sphinx-build' executable. Alternatively you
-	echo.may add the Sphinx directory to PATH.
-	echo.
-	echo.If you don't have Sphinx installed, grab it from
-	echo.https://www.sphinx-doc.org/
-	exit /b 1
-)
-
-if "%1" == "" goto help
-
-%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
-goto end
-
-:help
-%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
-
-:end
-popd

docs/other_models.md

@@ -1,24 +0,0 @@
-# Other Models
-
-```bash
-# Ollama
-OPENAI_API_BASE='http://localhost:11434/v1'
-OPENAI_MODEL_NAME='mistral'
-OPENAI_API_KEY='NA'
-
-# FastChat
-OPENAI_API_BASE="http://localhost:8001/v1"
-OPENAI_MODEL_NAME='oh-2.5m7b-q51'
-OPENAI_API_KEY=NA
-
-# LM Studio
-OPENAI_API_BASE="http://localhost:1234/v1"
-OPENAI_MODEL_NAME=NA
-OPENAI_API_KEY=NA
-
-# Mistral API
-OPENAI_API_BASE=https://api.mistral.ai/v1
-OPENAI_MODEL_NAME="mistral-small"
-OPENAI_API_KEY=your-mistral-api-key
-```
-

docs/reddit.md

@@ -1,32 +0,0 @@
-# Reddit PraisonAI Integration
-
-```bash
-export REDDIT_USER_AGENT=[USER]
-export REDDIT_CLIENT_SECRET=xxxxxx
-export REDDIT_CLIENT_ID=xxxxxx
-```
-
-tools.py
-
-```python
-from langchain_community.tools.reddit_search.tool import RedditSearchRun
-```
-
-agents.yaml
-
-```yaml
-framework: crewai
-topic: research about the causes of lung disease
-roles:
-  research_analyst:
-    backstory: Experienced in analyzing scientific data related to respiratory health.
-    goal: Analyze data on lung diseases
-    role: Research Analyst
-    tasks:
-      data_analysis:
-        description: Gather and analyze data on the causes and risk factors of lung
-          diseases.
-        expected_output: Report detailing key findings on lung disease causes.
-    tools:
-    - 'RedditSearchRun'
-```

docs/requirements.in

@@ -1,12 +0,0 @@
-sphinx
-sphinx_rtd_theme
-sphinx-autodoc-typehints
-mkdocs
-markdown-include
-mkdocs-material
-mkdocs-jupyter
-pymdown-extensions
-Pygments
-pdoc3
-xmlrunner
-unittest2

docs/requirements.txt

@@ -1,24 +0,0 @@
-sphinx
-sphinx_rtd_theme
-sphinx-autodoc-typehints
-mkdocs
-markdown-include
-mkdocs-material
-mkdocs-jupyter
-mkdocs-glightbox
-pymdown-extensions
-Pygments
-mkdocstrings 
-mkdocs-apidoc 
-mkdocstrings-python
-mkdocs-jupyter
-pdoc3
-rich
-pyautogen
-crewai
-gradio
-duckduckgo_search
-praisonai_tools
-open-interpreter
-xmlrunner
-unittest2

docs/run.md

@@ -1,23 +0,0 @@
-# Run
-
-```bash
-praisonai
-```
-
-or 
-    
-```bash
-python -m praisonai
-```
-
-## Specify the agent framework (Optional):
-
-```bash
-praisonai --framework autogen
-```
-
-## Full Automatic Mode
-
-```bash
-praisonai --auto create a movie script about Dog in Moon
-```

docs/tavily.md

@@ -1,15 +0,0 @@
-# Tavily PraisonAI Integration
-
-```python
-from praisonai_tools import BaseTool
-from langchain.utilities.tavily_search import TavilySearchAPIWrapper
-
-class TavilyTool(BaseTool):
-    name: str = "TavilyTool"
-    description: str = "Search Tavily for relevant information based on a query."
-
-    def _run(self, query: str):
-        api_wrapper = TavilySearchAPIWrapper()
-        results = api_wrapper.results(query=query, max_results=5)
-        return results
-```

docs/test.md

@@ -1,5 +0,0 @@
-# Test
-    
-```bash
-python -m unittest tests.test 
-```

docs/tldr.md

@@ -1,20 +0,0 @@
-# TL;DR
-
-## Run throught Terminal
-
-```bash
-pip install praisonai
-export OPENAI_API_KEY="Enter your API key"
-praisonai --init create a movie script about dog in moon
-praisonai
-```
-
-## User Interface
-
-```bash
-pip install -U "praisonai[ui]"
-export OPENAI_API_KEY="Enter your API key"
-chainlit create-secret
-export CHAINLIT_AUTH_SECRET=xxxxxxxx
-praisonai ui
-```

docs/tools.md

@@ -1,43 +0,0 @@
-# Tools
-
-## Inbuild Tools
-
-- CodeDocsSearchTool
-- CSVSearchTool
-- DirectorySearchTool
-- DirectoryReadTool
-- DOCXSearchTool
-- FileReadTool
-- GithubSearchTool
-- SerperDevTool
-- TXTSearchTool
-- JSONSearchTool
-- MDXSearchTool
-- PDFSearchTool
-- PGSearchTool
-- RagTool
-- ScrapeElementFromWebsiteTool
-- ScrapeWebsiteTool
-- SeleniumScrapingTool
-- WebsiteSearchTool
-- XMLSearchTool
-- YoutubeChannelSearchTool
-- YoutubeVideoSearchTool
-
-## Example Usage
-
-```yaml
-framework: crewai
-topic: research about the causes of lung disease
-roles:
-  research_analyst:
-    backstory: Experienced in analyzing scientific data related to respiratory health.
-    goal: Analyze data on lung diseases
-    role: Research Analyst
-    tasks:
-      data_analysis:
-        description: Gather and analyze data on the causes and risk factors of lung diseases.
-        expected_output: Report detailing key findings on lung disease causes.
-    tools:
-    - WebsiteSearchTool
-```

docs/ui.md

@@ -1,83 +0,0 @@
-# PraisonAI User Interface (UI)
-
-## Chainlit
-```bash
-pip install -U "praisonai[ui]"
-export OPENAI_API_KEY="Enter your API key"
-chainlit create-secret
-export CHAINLIT_AUTH_SECRET=xxxxxxxx
-praisonai ui
-```
-
-or 
-
-```bash
-python -m praisonai ui
-```
-
-Default Username: admin
-Default Password: admin
-
-### To Change Username and Password
-
-create .env file in the root folder of the project
-Add below Variables and required Username/Password
-```
-CHAINLIT_USERNAME=admin
-CHAINLIT_USERNAME=admin
-```
-
-## Gradio
-```bash
-pip install "praisonai[gradio]"
-export OPENAI_API_KEY="Enter your API key"
-praisonai --ui gradio
-```
-
-## Streamlit
-
-```bash
-git clone https://github.com/leporejoseph/PraisonAi-Streamlit
-cd PraisonAi-Streamlit
-pip install -r requirements.txt
-streamlit run app.py
-```
-
-## Using Chainlit (with Pictures)
-
-## Run Automatically
-
-### Install Required Package
-![Install Required Package](images/ui-step-1.png)
-
-### User Interface
-![User Interface](images/ui-step-2.png)
-
-### Select Auto Mode
-![Select Auto Mode](images/ui-step-4.png)
-
-### Configure Agent Settings
-![Configure Agent Settings](images/ui-step-3.png)
-
-### Define a Task to Auto Generate Agents and Run
-![Define a Task](images/ui-step-5.png)
-
-### Output
-![Output](images/ui-step-6.png)
-
-## Run Manually
-
-### Select Manual Mode
-![Select Manual Mode](images/ui-step-7.png)
-
-### Modify Agents and Tools
-![Modify Agents and Tools](images/ui-step-10.png)
-
-## Review Generated Agents
-![Review Generated Agents](images/ui-step-9.png)
-
-### Run Agents
-![Run Agents](images/ui-step-8.png)
-
-### Manual Model Output
-![Manual Model Output](images/ui-step-11.png)

docs/wikipedia.md

@@ -1,38 +0,0 @@
-# Wikipedia PraisonAI Integration
-
-```bash
-pip install wikipedia langchain_community
-```
-
-```python
-# tools.py
-from langchain_community.utilities import WikipediaAPIWrapper
-class WikipediaSearchTool(BaseTool):
-    name: str = "WikipediaSearchTool"
-    description: str = "Search Wikipedia for relevant information based on a query."
-
-    def _run(self, query: str):
-        api_wrapper = WikipediaAPIWrapper(top_k_results=4, doc_content_chars_max=100)
-        results = api_wrapper.load(query=query)
-        return results
-```
-
-```yaml
-# agents.yaml
-framework: crewai
-topic: research about nvidia growth
-roles:
-  data_collector:
-    backstory: An experienced researcher with the ability to efficiently collect and
-      organize vast amounts of data.
-    goal: Gather information on Nvidia's growth by providing the Ticket Symbol to YahooFinanceNewsTool
-    role: Data Collector
-    tasks:
-      data_collection_task:
-        description: Collect data on Nvidia's growth from various sources such as
-          financial reports, news articles, and company announcements.
-        expected_output: A comprehensive document detailing data points on Nvidia's
-          growth over the years.
-    tools:
-    - 'WikipediaSearchTool'
-```

docs/wrapper.md

@@ -1,45 +0,0 @@
-## Include praisonai package in your project
-
-Note: Please create agents.yaml file before hand. 
-
-```python
-from praisonai import PraisonAI
-
-def basic(): # Basic Mode
-    praison_ai = PraisonAI(agent_file="agents.yaml")
-    praison_ai.main()
-
-if __name__ == "__main__":
-    basic()
-```
-
-## All in one
-
-```python
-from praisonai import PraisonAI
-
-def basic(): # Basic Mode
-    praison_ai = PraisonAI(agent_file="agents.yaml")
-    praison_ai.main()
-    
-def advanced(): # Advanced Mode with options
-    praison_ai = PraisonAI(
-        agent_file="agents.yaml",
-        framework="autogen",
-    )
-    praison_ai.main()
-    
-def auto(): # Full Automatic Mode
-    praison_ai = PraisonAI(
-        auto="Create a movie script about car in mars",
-        framework="autogen"
-    )
-    print(praison_ai.framework)
-    praison_ai.main()
-
-if __name__ == "__main__":
-    basic()
-    advanced()
-    auto()
-```
-

docs/you.com.md

@@ -1,36 +0,0 @@
-# You.com PraisonAI Integration
-
-```bash
-export YDC_API_KEY=xxxxxxxxxxxx
-```
-tools.py
-```python
-from langchain_community.utilities.you import YouSearchAPIWrapper
-class YouSearchTool(BaseTool):
-    name: str = "You Search Tool"
-    description: str = "Search You.com for relevant information based on a query."
-
-    def _run(self, query: str):
-        api_wrapper = YouSearchAPIWrapper()
-        results = api_wrapper.results(query=query, max_results=5)
-        return results
-```
-
-agents.yaml
-
-```yaml
-framework: crewai
-topic: research about the causes of lung disease
-roles:
-  research_analyst:
-    backstory: Experienced in analyzing scientific data related to respiratory health.
-    goal: Analyze data on lung diseases
-    role: Research Analyst
-    tasks:
-      data_analysis:
-        description: Gather and analyze data on the causes and risk factors of lung
-          diseases.
-        expected_output: Report detailing key findings on lung disease causes.
-    tools:
-    - 'YouSearchTool'
-```

docs/youtube.md

@@ -1,28 +0,0 @@
-# YouTube Search PraisonAI Integration
-
-```bash
-pip install youtube_search praisonai langchain_community langchain
-```
-
-```python
-# tools.py
-from langchain_community.tools import YouTubeSearchTool
-```
-
-```yaml
-# agents.yaml
-framework: crewai
-topic: research about the causes of lung disease
-roles:
-  research_analyst:
-    backstory: Experienced in analyzing scientific data related to respiratory health.
-    goal: Analyze data on lung diseases
-    role: Research Analyst
-    tasks:
-      data_analysis:
-        description: Gather and analyze data on the causes and risk factors of lung
-          diseases.
-        expected_output: Report detailing key findings on lung disease causes.
-    tools:
-    - 'YouTubeSearchTool'
-```

example.py

@@ -1,25 +0,0 @@
-from praisonai import PraisonAI
-
-def basic():
-    praison_ai = PraisonAI(agent_file="agents.yaml")
-    praison_ai.main()
-    
-def advanced():
-    praison_ai = PraisonAI(
-        agent_file="agents.yaml",
-        framework="autogen",
-    )
-    praison_ai.main()
-    
-def auto():
-    praison_ai = PraisonAI(
-        auto="Create a movie script about car in mars",
-        framework="autogen"
-    )
-    print(praison_ai.framework)
-    praison_ai.main()
-
-if __name__ == "__main__":
-    basic()
-    advanced()
-    auto()

examples/researchers.yml

@@ -1,41 +0,0 @@
-framework: "crewai"
-topic: "Artificial Intelligence"
-
-roles:
-  researcher:
-    role: "Senior Data Researcher"
-    goal: "Uncover cutting-edge developments in {topic}"
-    backstory: > 
-      You're a seasoned researcher with a knack for uncovering the latest
-      developments in {topic}. Known for your ability to find the most relevant
-      information and present it in a clear and concise manner.
-    tools:
-      - search_tool: 
-          engine: google
-    tasks:
-      research_task:
-        description: >
-          Conduct thorough research about {topic}, focusing on the latest 
-          trends and technologies.
-        expected_output: > 
-          A list of 10 bullet points summarizing key findings with links to 
-          relevant sources. 
-
-  reporting_analyst:
-    role: "Reporting Analyst"
-    goal: "Create detailed reports based on {topic} data analysis and research findings."
-    backstory: >
-      You're a meticulous analyst with a keen eye for detail. You're known for 
-      your ability to turn complex data into clear and concise reports, making 
-      it easy for others to understand and act on the information you provide.
-    tasks:
-      reporting_task:
-        description: >
-          Review the research findings and create a comprehensive report on 
-          {topic}. Include insights, potential opportunities, and recommendations.
-        expected_output: >
-          A well-structured report in Markdown format, ready for distribution.
-
-dependencies:
- - task: reporting_task
-   depends_on: research_task  

examples/scriptwriters.yml

@@ -1,41 +0,0 @@
-framework: "autogen"
-topic: "Artificial Intelligence"
-
-roles:
-  narrative_designer:
-    role: "Narrative Designer"
-    goal: "Craft engaging storylines and narratives for {topic}"
-    backstory: >
-      You're a creative visionary skilled in weaving complex narratives and 
-      developing engaging story worlds within {topic}. Your expertise lies in 
-      conceptualizing the overarching story and ensuring it resonates with the audience.
-    tasks:
-      story_concept_development:
-        description: >
-          Develop a compelling story concept for {topic}, focusing on originality, 
-          thematic depth, and audience engagement. Outline the main narrative arcs,
-          characters, and settings.
-        expected_output: >
-          A comprehensive document outlining the story concept, including key narrative 
-          arcs, character bios, and settings descriptions.
-
-  scriptwriter:
-    role: "Scriptwriter"
-    goal: "Transform story concepts into detailed scripts for {topic}"
-    backstory: >
-      With a flair for dialogue and a deep understanding of pacing and structure,
-      you excel at bringing narratives to life through scriptwriting. Your work 
-      turns story concepts into blueprints for compelling {topic} experiences.
-    tasks:
-      scriptwriting_task:
-        description: >
-          Based on the narrative concept, write a detailed script for {topic}. 
-          Include dialogue, stage directions, and scene descriptions that bring 
-          the story to life.
-        expected_output: >
-          A detailed script ready for production, including dialogue, stage 
-          directions, and scene descriptions.
-
-dependencies:
- - task: scriptwriting_task
-   depends_on: story_concept_development  

hub/tools/tools.py

@@ -1,72 +0,0 @@
-# tools.py
-from duckduckgo_search import DDGS
-from praisonai_tools import BaseTool
-
-class InternetSearchTool(BaseTool):
-    name: str = "InternetSearchTool"
-    description: str = "Search Internet for relevant information based on a query or latest news"
-
-    def _run(self, query: str):
-        ddgs = DDGS()
-        results = ddgs.text(keywords=query, region='wt-wt', safesearch='moderate', max_results=5)
-        return results
-    
-from langchain_community.tools.pubmed.tool import PubmedQueryRun
-from langchain_community.tools.reddit_search.tool import RedditSearchRun
-# pip install tavily-python
-from langchain_community.utilities.tavily_search import TavilySearchAPIWrapper
-from langchain_community.utilities.you import YouSearchAPIWrapper
-# pip install youtube_search
-from langchain_community.tools import YouTubeSearchTool
-# pip install wikipedia
-from langchain_community.utilities import WikipediaAPIWrapper
-
-class TavilyTool(BaseTool):
-    name: str = "TavilyTool"
-    description: str = "Search Tavily for relevant information based on a query."
-
-    def _run(self, query: str):
-        api_wrapper = TavilySearchAPIWrapper()
-        results = api_wrapper.results(query=query, max_results=5)
-        return results
-
-class YouSearchTool(BaseTool):
-    name: str = "You Search Tool"
-    description: str = "Search You.com for relevant information based on a query."
-
-    def _run(self, query: str):
-        api_wrapper = YouSearchAPIWrapper()
-        results = api_wrapper.results(query=query, max_results=5)
-        return results
-
-class WikipediaSearchTool(BaseTool):
-    name: str = "WikipediaSearchTool"
-    description: str = "Search Wikipedia for relevant information based on a query."
-
-    def _run(self, query: str):
-        api_wrapper = WikipediaAPIWrapper(top_k_results=4, doc_content_chars_max=100)
-        results = api_wrapper.load(query=query)
-        return results
-
-# pip install open-interpreter
-from interpreter import interpreter
-from praisonai_tools import BaseTool
-
-class OpenInterpreterTool(BaseTool):
-    name: str = "Open Interpreter Tool"
-    description: str = "Use Open Interpreter configured to create and execute code based on natural language commands."
-
-    def __init__(self):
-        super().__init__()
-        interpreter.auto_run = True
-
-    def _run(self, code: str):
-        result = interpreter.chat(code)
-        return result
-
-# Example usage
-if __name__ == "__main__":
-    tool = OpenInterpreterTool()
-    code = "Plot AAPL and META's normalized stock prices"
-    output = tool._run(code)
-    print(output)

mkdocs.yml

@@ -1,64 +0,0 @@
-site_name: PraisonAI Documentation
-site_url: https://docs.praison.ai
-site_author: MervinPraison
-
-repo_name: MervinPraison/PraisonAI
-repo_url: https://github.com/MervinPraison/PraisonAI
-
-nav:
-  - 🏠 Home:
-      - Home: home.md
-      - TL;DR: tldr.md
-      - User Interface: ui.md
-      - Other Models: other_models.md
-
-  - ⚡ Getting Started:
-      - Installation: installation.md
-      - Initialise: initialise.md
-      - Run: run.md
-
-  - ⚙️ Tools:
-      - Tools: tools.md
-      - Create Custom Tools: custom_tools.md
-      - DuckDuckGo: duckduckgo.md
-      - Langchain: langchain.md
-      - Firecrawl: firecrawl.md
-      - Wikipedia: wikipedia.md
-      - YouTube: youtube.md
-      - Tavily: tavily.md
-      - Reddit: reddit.md
-      - You.com: you.com.md
-
-  - 📚 Developers:
-      - Test: test.md
-      - Agents Playbook: agents_playbook.md
-      - Wrapper: wrapper.md
-      - Deploy: deploy.md
-
-  - 🤗 Contributing:
-      - Home: contributing.md
-
-  - 🛠️ API reference:
-      - API: api.md
-      - API Code: api/praisonai/index.html
-
-theme:
-  name: material
-
-markdown_extensions:
-  - admonition
-  - attr_list
-  - md_in_html
-  - toc:
-      permalink: true
-  - pymdownx.superfences
-  - pymdownx.details
-
-plugins:
-  - search
-  - mkdocstrings:
-      handlers:
-        python:
-          selection:
-            docstring_style: google
-  # - glightbox

package.json

@@ -0,0 +1,42 @@
+{
+  "name": "admin-ui",
+  "version": "0.1.0",
+  "private": true,
+  "dependencies": {
+    "@chakra-ui/react": "^2.8.2",
+    "@emotion/react": "^11.11.4",
+    "@emotion/styled": "^11.11.5",
+    "@testing-library/jest-dom": "^5.17.0",
+    "@testing-library/react": "^13.4.0",
+    "@testing-library/user-event": "^13.5.0",
+    "framer-motion": "^11.2.0",
+    "react": "^18.3.1",
+    "react-dom": "^18.3.1",
+    "react-scripts": "5.0.1",
+    "web-vitals": "^2.1.4"
+  },
+  "scripts": {
+    "start": "react-scripts start",
+    "build": "react-scripts build",
+    "test": "react-scripts test",
+    "eject": "react-scripts eject"
+  },
+  "eslintConfig": {
+    "extends": [
+      "react-app",
+      "react-app/jest"
+    ]
+  },
+  "browserslist": {
+    "production": [
+      ">0.2%",
+      "not dead",
+      "not op_mini all"
+    ],
+    "development": [
+      "last 1 chrome version",
+      "last 1 firefox version",
+      "last 1 safari version"
+    ]
+  }
+}

praisonai/__init__.py

@@ -1,6 +0,0 @@
-# Disable OpenTelemetry SDK
-import os
-os.environ["OTEL_SDK_DISABLED"] = "true"
-os.environ["EC_TELEMETRY"] = "false"
-from .cli import PraisonAI
-from .version import __version__

praisonai/__main__.py

@@ -1,10 +0,0 @@
-# __main__.py
-
-from .cli import PraisonAI
-
-def main():
-    praison_ai = PraisonAI()
-    praison_ai.main()
-
-if __name__ == "__main__":
-    main()

praisonai/agents_generator.py

@@ -1,314 +0,0 @@
-# praisonai/agents_generator.py
-
-import sys
-from .version import __version__
-import yaml, os
-from rich import print
-from dotenv import load_dotenv
-from crewai import Agent, Task, Crew
-from crewai.telemetry import Telemetry
-load_dotenv()
-import autogen
-import argparse
-from .auto import AutoGenerator
-from praisonai_tools import (
-    CodeDocsSearchTool, CSVSearchTool, DirectorySearchTool, DOCXSearchTool, DirectoryReadTool,
-    FileReadTool, TXTSearchTool, JSONSearchTool, MDXSearchTool, PDFSearchTool, RagTool,
-    ScrapeElementFromWebsiteTool, ScrapeWebsiteTool, WebsiteSearchTool, XMLSearchTool, YoutubeChannelSearchTool,
-    YoutubeVideoSearchTool
-)
-from .inbuilt_tools import *
-import inspect
-from pathlib import Path
-import importlib
-import importlib.util
-from praisonai_tools import BaseTool
-import os
-import logging
-
-agentops = False
-try:
-    import agentops
-    agentops = True
-except ImportError:
-    agentops = False
-
-os.environ["OTEL_SDK_DISABLED"] = "true"
-
-def noop(*args, **kwargs):
-    pass
-
-def disable_crewai_telemetry():
-    for attr in dir(Telemetry):
-        if callable(getattr(Telemetry, attr)) and not attr.startswith("__"):
-            setattr(Telemetry, attr, noop)
-            
-disable_crewai_telemetry()
-
-class AgentsGenerator:
-    def __init__(self, agent_file, framework, config_list, log_level=None, agent_callback=None, task_callback=None):
-        """
-        Initialize the AgentsGenerator object.
-
-        Parameters:
-            agent_file (str): The path to the agent file.
-            framework (str): The framework to be used for the agents.
-            config_list (list): A list of configurations for the agents.
-            log_level (int, optional): The logging level to use. Defaults to logging.INFO.
-            agent_callback (callable, optional): A callback function to be executed after each agent step.
-            task_callback (callable, optional): A callback function to be executed after each tool run.
-
-        Attributes:
-            agent_file (str): The path to the agent file.
-            framework (str): The framework to be used for the agents.
-            config_list (list): A list of configurations for the agents.
-            log_level (int): The logging level to use.
-            agent_callback (callable, optional): A callback function to be executed after each agent step.
-            task_callback (callable, optional): A callback function to be executed after each tool run.
-        """
-        self.agent_file = agent_file
-        self.framework = framework
-        self.config_list = config_list
-        self.log_level = log_level
-        self.agent_callback = agent_callback
-        self.task_callback = task_callback
-        self.log_level = log_level or logging.getLogger().getEffectiveLevel()
-        if self.log_level == logging.NOTSET:
-            self.log_level = os.environ.get('LOGLEVEL', 'INFO').upper()
-        
-        logging.basicConfig(level=self.log_level, format='%(asctime)s - %(levelname)s - %(message)s')
-        self.logger = logging.getLogger(__name__)
-        self.logger.setLevel(self.log_level)
-        
-    def is_function_or_decorated(self, obj):
-        """
-        Checks if the given object is a function or has a __call__ method.
-
-        Parameters:
-            obj (object): The object to be checked.
-
-        Returns:
-            bool: True if the object is a function or has a __call__ method, False otherwise.
-        """
-        return inspect.isfunction(obj) or hasattr(obj, '__call__')
-
-    def load_tools_from_module(self, module_path):
-        """
-        Loads tools from a specified module path.
-
-        Parameters:
-            module_path (str): The path to the module containing the tools.
-
-        Returns:
-            dict: A dictionary containing the names of the tools as keys and the corresponding functions or objects as values.
-
-        Raises:
-            FileNotFoundError: If the specified module path does not exist.
-        """
-        spec = importlib.util.spec_from_file_location("tools_module", module_path)
-        module = importlib.util.module_from_spec(spec)
-        spec.loader.exec_module(module)
-        return {name: obj for name, obj in inspect.getmembers(module, self.is_function_or_decorated)}
-    
-    def load_tools_from_module_class(self, module_path):
-        """
-        Loads tools from a specified module path containing classes that inherit from BaseTool or are part of langchain_community.tools package.
-
-        Parameters:
-            module_path (str): The path to the module containing the tools.
-
-        Returns:
-            dict: A dictionary containing the names of the tools as keys and the corresponding initialized instances of the classes as values.
-
-        Raises:
-            FileNotFoundError: If the specified module path does not exist.
-        """
-        spec = importlib.util.spec_from_file_location("tools_module", module_path)
-        module = importlib.util.module_from_spec(spec)
-        spec.loader.exec_module(module)
-        return {name: obj() for name, obj in inspect.getmembers(module, lambda x: inspect.isclass(x) and (x.__module__.startswith('langchain_community.tools') or issubclass(x, BaseTool)) and x is not BaseTool)}
-
-    def load_tools_from_package(self, package_path):
-        """
-        Loads tools from a specified package path containing modules with functions or classes.
-
-        Parameters:
-            package_path (str): The path to the package containing the tools.
-
-        Returns:
-            dict: A dictionary containing the names of the tools as keys and the corresponding initialized instances of the classes as values.
-
-        Raises:
-            FileNotFoundError: If the specified package path does not exist.
-
-        This function iterates through all the .py files in the specified package path, excluding those that start with "__". For each file, it imports the corresponding module and checks if it contains any functions or classes that can be loaded as tools. The function then returns a dictionary containing the names of the tools as keys and the corresponding initialized instances of the classes as values.
-        """
-        tools_dict = {}
-        for module_file in os.listdir(package_path):
-            if module_file.endswith('.py') and not module_file.startswith('__'):
-                module_name = f"{package_path.name}.{module_file[:-3]}"  # Remove .py for import
-                module = importlib.import_module(module_name)
-                for name, obj in inspect.getmembers(module, self.is_function_or_decorated):
-                    tools_dict[name] = obj
-        return tools_dict
-
-    def generate_crew_and_kickoff(self):
-        """
-        Generates a crew of agents and initiates tasks based on the provided configuration.
-
-        Parameters:
-            agent_file (str): The path to the agent file.
-            framework (str): The framework to be used for the agents.
-            config_list (list): A list of configurations for the agents.
-
-        Returns:
-            str: The output of the tasks performed by the crew of agents.
-
-        Raises:
-            FileNotFoundError: If the specified agent file does not exist.
-
-        This function first loads the agent configuration from the specified file. It then initializes the tools required for the agents based on the specified framework. If the specified framework is "autogen", it loads the LLM configuration dynamically and creates an AssistantAgent for each role in the configuration. It then adds tools to the agents if specified in the configuration. Finally, it prepares tasks for the agents based on the configuration and initiates the tasks using the crew of agents. If the specified framework is not "autogen", it creates a crew of agents and initiates tasks based on the configuration.
-        """
-        if self.agent_file == '/app/api:app' or self.agent_file == 'api:app':
-            self.agent_file = 'agents.yaml'
-        try:
-            with open(self.agent_file, 'r') as f:
-                config = yaml.safe_load(f)
-        except FileNotFoundError:
-            print(f"File not found: {self.agent_file}")
-            return
-
-        topic = config['topic']
-        tools_dict = {
-            'CodeDocsSearchTool': CodeDocsSearchTool(),
-            'CSVSearchTool': CSVSearchTool(),
-            'DirectorySearchTool': DirectorySearchTool(),
-            'DOCXSearchTool': DOCXSearchTool(),
-            'DirectoryReadTool': DirectoryReadTool(),
-            'FileReadTool': FileReadTool(),
-            # 'GithubSearchTool': GithubSearchTool(),
-            # 'SeperDevTool': SeperDevTool(),
-            'TXTSearchTool': TXTSearchTool(),
-            'JSONSearchTool': JSONSearchTool(),
-            'MDXSearchTool': MDXSearchTool(),
-            'PDFSearchTool': PDFSearchTool(),
-            # 'PGSearchTool': PGSearchTool(),
-            'RagTool': RagTool(),
-            'ScrapeElementFromWebsiteTool': ScrapeElementFromWebsiteTool(),
-            'ScrapeWebsiteTool': ScrapeWebsiteTool(),
-            'WebsiteSearchTool': WebsiteSearchTool(),
-            'XMLSearchTool': XMLSearchTool(),
-            'YoutubeChannelSearchTool': YoutubeChannelSearchTool(),
-            'YoutubeVideoSearchTool': YoutubeVideoSearchTool(),
-        }
-        root_directory = os.getcwd()
-        tools_py_path = os.path.join(root_directory, 'tools.py')
-        tools_dir_path = Path(root_directory) / 'tools'
-        
-        if os.path.isfile(tools_py_path):
-            tools_dict.update(self.load_tools_from_module_class(tools_py_path))
-            self.logger.debug("tools.py exists in the root directory. Loading tools.py and skipping tools folder.")
-        elif tools_dir_path.is_dir():
-            tools_dict.update(self.load_tools_from_module_class(tools_dir_path))
-            self.logger.debug("tools folder exists in the root directory")
-        
-        framework = self.framework or config.get('framework')
-
-        agents = {}
-        tasks = []
-        if framework == "autogen":
-            # Load the LLM configuration dynamically
-            # print(self.config_list)
-            llm_config = {"config_list": self.config_list}
-            
-            # Assuming the user proxy agent is set up as per your requirements
-            user_proxy = autogen.UserProxyAgent(
-                name="User",
-                human_input_mode="NEVER",
-                is_termination_msg=lambda x: (x.get("content") or "").rstrip().rstrip(".").lower().endswith("terminate") or "TERMINATE" in (x.get("content") or ""),
-                code_execution_config={
-                    "work_dir": "coding",
-                    "use_docker": False,
-                },
-                # additional setup for the user proxy agent
-            )
-            
-            for role, details in config['roles'].items():
-                agent_name = details['role'].format(topic=topic).replace("{topic}", topic)
-                agent_goal = details['goal'].format(topic=topic)
-                # Creating an AssistantAgent for each role dynamically
-                agents[role] = autogen.AssistantAgent(
-                    name=agent_name,
-                    llm_config=llm_config,
-                    system_message=details['backstory'].format(topic=topic)+". Must Reply \"TERMINATE\" in the end when everything is done.",
-                )
-                for tool in details.get('tools', []):
-                    if tool in tools_dict:
-                        try:
-                            tool_class = globals()[f'autogen_{type(tools_dict[tool]).__name__}']
-                            print(f"Found {tool_class.__name__} for {tool}")
-                        except KeyError:
-                            print(f"Warning: autogen_{type(tools_dict[tool]).__name__} function not found. Skipping this tool.")
-                            continue
-                        tool_class(agents[role], user_proxy)
-
-                # Preparing tasks for initiate_chats
-                for task_name, task_details in details.get('tasks', {}).items():
-                    description_filled = task_details['description'].format(topic=topic)
-                    expected_output_filled = task_details['expected_output'].format(topic=topic)
-                    
-                    chat_task = {
-                        "recipient": agents[role],
-                        "message": description_filled,
-                        "summary_method": "last_msg", 
-                        # Additional fields like carryover can be added based on dependencies
-                    }
-                    tasks.append(chat_task)
-
-            response = user_proxy.initiate_chats(tasks)
-            result = "### Output ###\n"+response[-1].summary if hasattr(response[-1], 'summary') else ""
-        else:
-            for role, details in config['roles'].items():
-                role_filled = details['role'].format(topic=topic)
-                goal_filled = details['goal'].format(topic=topic)
-                backstory_filled = details['backstory'].format(topic=topic)
-                
-                # Adding tools to the agent if exists
-                agent_tools = [tools_dict[tool] for tool in details.get('tools', []) if tool in tools_dict]
-                agent = Agent(role=role_filled, goal=goal_filled, backstory=backstory_filled, tools=agent_tools, allow_delegation=False)
-                
-                # Set agent callback if provided
-                if self.agent_callback:
-                    agent.step_callback = self.agent_callback
-
-                agents[role] = agent
-
-                for task_name, task_details in details.get('tasks', {}).items():
-                    description_filled = task_details['description'].format(topic=topic)
-                    expected_output_filled = task_details['expected_output'].format(topic=topic)
-
-                    task = Task(description=description_filled, expected_output=expected_output_filled, agent=agent)
-                    
-                    # Set tool callback if provided
-                    if self.task_callback:
-                        task.callback = self.task_callback
-
-                    tasks.append(task)
-            if agentops:
-                agentops.init()
-            crew = Crew(
-                agents=list(agents.values()),
-                tasks=tasks,
-                verbose=2
-            )
-            if agentops:
-                agentops.end_session("Success")
-            
-            self.logger.debug("Final Crew Configuration:")
-            self.logger.debug(f"Agents: {crew.agents}")
-            self.logger.debug(f"Tasks: {crew.tasks}")
-
-            response = crew.kickoff()
-            result = f"### Task Output ###\n{response}"
-        return result
-

praisonai/auto.py

@@ -1,188 +0,0 @@
-from openai import OpenAI
-from pydantic import BaseModel
-from typing import Dict, List, Optional
-import instructor
-import os
-import json
-import yaml
-from rich import print
-
-# Define Pydantic models outside of the generate method
-class TaskDetails(BaseModel):
-    description: str
-    expected_output: str
-
-class RoleDetails(BaseModel):
-    role: str
-    goal: str
-    backstory: str
-    tasks: Dict[str, TaskDetails]
-    tools: List[str]
-
-class TeamStructure(BaseModel):
-    roles: Dict[str, RoleDetails]
-
-class AutoGenerator:
-    def __init__(self, topic="Movie Story writing about AI", agent_file="test.yaml", framework="crewai", config_list: Optional[List[Dict]] = None):
-        """
-        Initialize the AutoGenerator class with the specified topic, agent file, and framework.
-        Note: autogen framework is different from this AutoGenerator class.
-
-        Args:
-            topic (str, optional): The topic for the generated team structure. Defaults to "Movie Story writing about AI".
-            agent_file (str, optional): The name of the YAML file to save the generated team structure. Defaults to "test.yaml".
-            framework (str, optional): The framework for the generated team structure. Defaults to "crewai".
-            config_list (Optional[List[Dict]], optional): A list containing the configuration details for the OpenAI API. 
-                                                          If None, it defaults to using environment variables or hardcoded values.
-        Attributes:
-            config_list (list): A list containing the configuration details for the OpenAI API.
-            topic (str): The specified topic for the generated team structure.
-            agent_file (str): The specified name of the YAML file to save the generated team structure.
-            framework (str): The specified framework for the generated team structure.
-            client (instructor.Client): An instance of the instructor.Client class initialized with the specified OpenAI API configuration.
-        """
-        self.config_list = config_list or [
-            {
-                'model': os.environ.get("OPENAI_MODEL_NAME", "gpt-4o"),
-                'base_url': os.environ.get("OPENAI_API_BASE", "https://api.openai.com/v1"),
-                'api_key': os.environ.get("OPENAI_API_KEY")
-            }
-        ]
-        self.topic = topic
-        self.agent_file = agent_file
-        self.framework = framework or "crewai"
-        self.client = instructor.patch(
-            OpenAI(
-                base_url=self.config_list[0]['base_url'],
-                api_key=os.getenv("OPENAI_API_KEY"),
-            ),
-            mode=instructor.Mode.JSON,
-        )
-
-    def generate(self):
-        """
-        Generates a team structure for the specified topic.
-
-        Args:
-            None
-
-        Returns:
-            str: The full path of the YAML file containing the generated team structure.
-
-        Raises:
-            Exception: If the generation process fails.
-
-        Usage:
-            generator = AutoGenerator(framework="crewai", topic="Create a movie script about Cat in Mars")
-            path = generator.generate()
-            print(path)
-        """
-        response = self.client.chat.completions.create(
-            model=self.config_list[0]['model'],
-            response_model=TeamStructure,
-            max_retries=10,
-            messages=[
-                {"role": "system", "content": "You are a helpful assistant designed to output complex team structures."},
-                {"role": "user", "content": self.get_user_content()}
-            ]
-        )
-        json_data = json.loads(response.model_dump_json())
-        self.convert_and_save(json_data)
-        full_path = os.path.abspath(self.agent_file)
-        return full_path
-
-    def convert_and_save(self, json_data):
-        """Converts the provided JSON data into the desired YAML format and saves it to a file.
-
-        Args:
-            json_data (dict): The JSON data representing the team structure.
-            topic (str, optional): The topic to be inserted into the YAML. Defaults to "Artificial Intelligence".
-            agent_file (str, optional): The name of the YAML file to save. Defaults to "test.yaml".
-        """
-
-        yaml_data = {
-            "framework": self.framework,
-            "topic": self.topic,
-            "roles": {},
-            "dependencies": []
-        }
-
-        for role_id, role_details in json_data['roles'].items():
-            yaml_data['roles'][role_id] = {
-                "backstory": "" + role_details['backstory'],
-                "goal": role_details['goal'],
-                "role": role_details['role'],
-                "tasks": {},
-                # "tools": role_details.get('tools', []),
-                "tools": ['']
-            }
-
-            for task_id, task_details in role_details['tasks'].items():
-                yaml_data['roles'][role_id]['tasks'][task_id] = {
-                    "description": "" + task_details['description'],
-                    "expected_output": "" + task_details['expected_output']
-                }
-
-        # Save to YAML file, maintaining the order
-        with open(self.agent_file, 'w') as f:
-            yaml.dump(yaml_data, f, allow_unicode=True, sort_keys=False)
-
-    def get_user_content(self):
-        """
-        Generates a prompt for the OpenAI API to generate a team structure.
-
-        Args:
-            None
-
-        Returns:
-            str: The prompt for the OpenAI API.
-
-        Usage:
-            generator = AutoGenerator(framework="crewai", topic="Create a movie script about Cat in Mars")
-            prompt = generator.get_user_content()
-            print(prompt)
-        """
-        user_content = """Generate a team structure for  \"""" + self.topic + """\" task. 
-No Input data will be provided to the team.
-The team will work in sequence. First role will pass the output to the next role, and so on.
-The last role will generate the final output.
-Think step by step.
-With maximum 3 roles, each with 1 task. Include role goals, backstories, task descriptions, and expected outputs.
-List of Available Tools: CodeDocsSearchTool, CSVSearchTool, DirectorySearchTool, DOCXSearchTool, DirectoryReadTool, FileReadTool, TXTSearchTool, JSONSearchTool, MDXSearchTool, PDFSearchTool, RagTool, ScrapeElementFromWebsiteTool, ScrapeWebsiteTool, WebsiteSearchTool, XMLSearchTool, YoutubeChannelSearchTool, YoutubeVideoSearchTool.
-Only use Available Tools. Do Not use any other tools. 
-Example Below: 
-Use below example to understand the structure of the output. 
-The final role you create should satisfy the provided task: """ + self.topic + """.
-{
-"roles": {
-"narrative_designer": {
-"role": "Narrative Designer",
-"goal": "Create AI storylines",
-"backstory": "Skilled in narrative development for AI, with a focus on story resonance.",
-"tools": ["ScrapeWebsiteTool"],
-"tasks": {
-"story_concept_development": {
-"description": "Craft a unique AI story concept with depth and engagement using concept from this page the content https://www.asthebirdfliesblog.com/posts/how-to-write-book-story-development .",
-"expected_output": "Document with narrative arcs, character bios, and settings."
-}
-}
-},
-"scriptwriter": {
-"role": "Scriptwriter",
-"goal": "Write scripts from AI concepts",
-"backstory": "Expert in dialogue and script structure, translating concepts into scripts.",
-"tasks": {
-"scriptwriting_task": {
-"description": "Turn narrative concepts into scripts, including dialogue and scenes.",
-"expected_output": "Production-ready script with dialogue and scene details."
-}
-}
-}
-}
-}
-        """
-        return user_content
-
-    
-# generator = AutoGenerator(framework="crewai", topic="Create a movie script about Cat in Mars")
-# print(generator.generate())

praisonai/chainlit_ui.py

@@ -1,302 +0,0 @@
-# praisonai/chainlit_ui.py
-from praisonai.agents_generator import AgentsGenerator 
-from praisonai.auto import AutoGenerator 
-import chainlit as cl
-import os
-from chainlit.types import ThreadDict
-from chainlit.input_widget import Select, TextInput
-from typing import Optional
-from dotenv import load_dotenv
-load_dotenv()
-from contextlib import redirect_stdout
-from io import StringIO
-
-framework = "crewai"
-config_list = [
-            {
-                'model': os.environ.get("OPENAI_MODEL_NAME", "gpt-4o"),
-                'base_url': os.environ.get("OPENAI_API_BASE", "https://api.openai.com/v1"),
-                'api_key': os.environ.get("OPENAI_API_KEY", "")
-            }
-        ]
-agent_file = "test.yaml"
-
-actions=[
-    cl.Action(name="run", value="run", label="✅ Run"),
-    cl.Action(name="modify", value="modify", label="🔧 Modify"),
-]
-
-@cl.action_callback("run")
-async def on_run(action):
-    await main(cl.Message(content=""))
-
-@cl.action_callback("modify")
-async def on_modify(action):
-    await cl.Message(content="Modify the agents and tools from below settings").send()
-    
-
-@cl.set_chat_profiles
-async def set_profiles(current_user: cl.User):
-    return [
-        cl.ChatProfile(
-            name="Auto",
-            markdown_description="Automatically generate agents and tasks based on your input.",
-            starters=[
-                cl.Starter(
-                    label="Create a movie script",
-                    message="Create a movie script about a futuristic society where AI and humans coexist, focusing on the conflict and resolution between them. Start with an intriguing opening scene.",
-                    icon="/public/movie.svg",
-                ),
-                cl.Starter(
-                    label="Design a fantasy world",
-                    message="Design a detailed fantasy world with unique geography, cultures, and magical systems. Start by describing the main continent and its inhabitants.",
-                    icon="/public/fantasy.svg",
-                ),
-                cl.Starter(
-                    label="Write a futuristic political thriller",
-                    message="Write a futuristic political thriller involving a conspiracy within a global government. Start with a high-stakes meeting that sets the plot in motion.",
-                    icon="/public/thriller.svg",
-                ),
-                cl.Starter(
-                    label="Develop a new board game",
-                    message="Develop a new, innovative board game. Describe the game's objective, rules, and unique mechanics. Create a scenario to illustrate gameplay.",
-                    icon="/public/game.svg",
-                ),
-            ]
-        ),
-        cl.ChatProfile(
-            name="Manual",
-            markdown_description="Manually define your agents and tasks using a YAML file.",
-        ),
-    ]
-
-
-@cl.on_chat_start
-async def start_chat():
-    cl.user_session.set(
-        "message_history",
-        [{"role": "system", "content": "You are a helpful assistant."}],
-    )
-    
-    # Create tools.py if it doesn't exist
-    if not os.path.exists("tools.py"):
-        with open("tools.py", "w") as f:
-            f.write("# Add your custom tools here\n")
-    
-    settings = await cl.ChatSettings(
-        [
-            TextInput(id="Model", label="OpenAI - Model", initial=config_list[0]['model']),
-            TextInput(id="BaseUrl", label="OpenAI - Base URL", initial=config_list[0]['base_url']),
-            TextInput(id="ApiKey", label="OpenAI - API Key", initial=config_list[0]['api_key']), 
-            Select(
-                id="Framework",
-                label="Framework",
-                values=["crewai", "autogen"],
-                initial_index=0,
-            ),
-        ]
-    ).send()
-    cl.user_session.set("settings", settings)
-    chat_profile = cl.user_session.get("chat_profile")
-    if chat_profile=="Manual":
-        
-        agent_file = "agents.yaml"
-        full_agent_file_path = os.path.abspath(agent_file)  # Get full path
-        if os.path.exists(full_agent_file_path):
-            with open(full_agent_file_path, 'r') as f:
-                yaml_content = f.read()
-            msg = cl.Message(content=yaml_content, language="yaml")
-            await msg.send()
-            
-                
-        full_tools_file_path = os.path.abspath("tools.py")  # Get full path
-        if os.path.exists(full_tools_file_path):
-            with open(full_tools_file_path, 'r') as f:
-                tools_content = f.read()
-            msg = cl.Message(content=tools_content, language="python")
-            await msg.send()
-
-        settings = await cl.ChatSettings(
-            [
-                TextInput(id="Model", label="OpenAI - Model", initial=config_list[0]['model']),
-                TextInput(id="BaseUrl", label="OpenAI - Base URL", initial=config_list[0]['base_url']),
-                TextInput(id="ApiKey", label="OpenAI - API Key", initial=config_list[0]['api_key']), 
-                Select(
-                    id="Framework",
-                    label="Framework",
-                    values=["crewai", "autogen"],
-                    initial_index=0,
-                ),
-                TextInput(id="agents", label="agents.yaml", initial=yaml_content, multiline=True),
-                TextInput(id="tools", label="tools.py", initial=tools_content, multiline=True),
-            ]
-        ).send()
-        cl.user_session.set("settings", settings)
-        
-        res = await cl.AskActionMessage(
-            content="Pick an action!",
-            actions=actions,
-        ).send()
-        if res and res.get("value") == "modify":
-            await cl.Message(content="Modify the agents and tools from below settings", actions=actions).send()
-        elif res and res.get("value") == "run":
-            await main(cl.Message(content="", actions=actions))
-
-    await on_settings_update(settings)
-    
-@cl.on_settings_update
-async def on_settings_update(settings):
-    """Handle updates to the ChatSettings form."""
-    global config_list, framework
-    config_list[0]['model'] = settings["Model"]
-    config_list[0]['base_url'] = settings["BaseUrl"]
-    config_list[0]['api_key'] = settings["ApiKey"]
-    os.environ["OPENAI_API_KEY"] = config_list[0]['api_key']
-    os.environ["OPENAI_MODEL_NAME"] = config_list[0]['model']
-    os.environ["OPENAI_API_BASE"] = config_list[0]['base_url']
-    framework = settings["Framework"]
-    
-    if "agents" in settings:
-        with open("agents.yaml", "w") as f:
-            f.write(settings["agents"])
-    if "tools" in settings:
-        with open("tools.py", "w") as f:
-            f.write(settings["tools"])
-    
-    print("Settings updated")
-
-@cl.on_chat_resume
-async def on_chat_resume(thread: ThreadDict):
-    message_history = cl.user_session.get("message_history", [])
-    root_messages = [m for m in thread["steps"] if m["parentId"] is None]
-    for message in root_messages:
-        if message["type"] == "user_message":
-            message_history.append({"role": "user", "content": message["output"]})
-        elif message["type"] == "ai_message":
-            message_history.append({"role": "assistant", "content": message["content"]})
-    cl.user_session.set("message_history", message_history)
-
-# @cl.step(type="tool")
-# async def tool(data: Optional[str] = None, language: Optional[str] = None):
-#     return cl.Message(content=data, language=language)
-
-@cl.step(type="tool", show_input=False)
-async def run_agents(agent_file: str, framework: str):
-    """Runs the agents and returns the result."""
-    agents_generator = AgentsGenerator(agent_file, framework, config_list)
-    current_step = cl.context.current_step
-    print("Current Step:", current_step)
-
-    stdout_buffer = StringIO()
-    with redirect_stdout(stdout_buffer):
-        result = agents_generator.generate_crew_and_kickoff()
-        
-    complete_output = stdout_buffer.getvalue()
-
-    async with cl.Step(name="gpt4", type="llm", show_input=True) as step:
-        step.input = ""
-        
-        for line in stdout_buffer.getvalue().splitlines():
-            print(line)
-            await step.stream_token(line)
-            
-        tool_res = await output(complete_output)
-
-    yield result
-
-@cl.step(type="tool", show_input=False, language="yaml")
-async def output(output):
-    return output
-
-@cl.step(type="tool", show_input=False, language="yaml")
-def agent(output):
-    return(f"""
-        Agent Step Completed!
-        Output: {output}
-    """)
-
-@cl.step(type="tool", show_input=False, language="yaml")
-def task(output):
-    return(f"""
-        Task Completed!
-        Task: {output.description}
-        Output: {output.raw_output}
-        {output}
-    """)
-
-@cl.on_message
-async def main(message: cl.Message):
-    """Run PraisonAI with the provided message as the topic."""
-    message_history = cl.user_session.get("message_history")
-    if message_history is None:
-        message_history = []
-        cl.user_session.set("message_history", message_history)
-    message_history.append({"role": "user", "content": message.content})
-    topic = message.content
-    chat_profile = cl.user_session.get("chat_profile")
-
-    if chat_profile == "Auto":
-        agent_file = "agents.yaml"
-        generator = AutoGenerator(topic=topic, agent_file=agent_file, framework=framework, config_list=config_list)
-        await cl.sleep(2)
-        agent_file = generator.generate()
-        agents_generator = AgentsGenerator(
-            agent_file, 
-            framework, 
-            config_list, 
-            # agent_callback=agent,
-            # task_callback=task
-        )
-        # Capture stdout
-        stdout_buffer = StringIO()
-        with redirect_stdout(stdout_buffer):
-            result = agents_generator.generate_crew_and_kickoff()
-            
-        complete_output = stdout_buffer.getvalue()
-        tool_res = await output(complete_output)
-        msg = cl.Message(content=result)
-        await msg.send()
-        message_history.append({"role": "assistant", "content": message.content})
-    else:  # chat_profile == "Manual"
-        agent_file = "agents.yaml"
-        full_agent_file_path = os.path.abspath(agent_file)  # Get full path
-        full_tools_file_path = os.path.abspath("tools.py")  
-        if os.path.exists(full_agent_file_path):
-            with open(full_agent_file_path, 'r') as f:
-                yaml_content = f.read()
-            # tool_res = await tool()
-            msg_agents = cl.Message(content=yaml_content, language="yaml")
-            await msg_agents.send()
-            if os.path.exists(full_tools_file_path):
-                with open(full_tools_file_path, 'r') as f:
-                    tools_content = f.read()
-                msg_tools = cl.Message(content=tools_content, language="python")
-                await msg_tools.send()
-        else:
-            # If the file doesn't exist, follow the same process as "Auto"
-            generator = AutoGenerator(topic=topic, agent_file=agent_file, framework=framework, config_list=config_list)
-            agent_file = generator.generate()
-
-        agents_generator = AgentsGenerator(agent_file, framework, config_list)
-        result = agents_generator.generate_crew_and_kickoff()
-        msg = cl.Message(content=result, actions=actions)
-        await msg.send()
-        message_history.append({"role": "assistant", "content": message.content})
-
-# Load environment variables from .env file
-load_dotenv()
-
-# Get username and password from environment variables
-username = os.getenv("CHAINLIT_USERNAME", "admin")  # Default to "admin" if not found
-password = os.getenv("CHAINLIT_PASSWORD", "admin")  # Default to "admin" if not found
-
-@cl.password_auth_callback
-def auth_callback(username: str, password: str):
-    # Fetch the user matching username from your database
-    # and compare the hashed password with the value stored in the database
-    if (username, password) == (username, password):
-        return cl.User(
-            identifier=username, metadata={"role": "ADMIN", "provider": "credentials"}
-        )
-    else:
-        return None

praisonai/cli.py

@@ -1,252 +0,0 @@
-# praisonai/cli.py
-
-import sys
-from .version import __version__
-import yaml, os
-from rich import print
-from dotenv import load_dotenv
-from crewai import Agent, Task, Crew
-load_dotenv()
-import autogen
-import argparse
-from .auto import AutoGenerator
-from .agents_generator import AgentsGenerator
-from .inbuilt_tools import *
-import shutil
-import logging
-logging.basicConfig(level=os.environ.get('LOGLEVEL', 'INFO'), format='%(asctime)s - %(levelname)s - %(message)s')
-
-try:
-    from chainlit.cli import chainlit_run
-    CHAINLIT_AVAILABLE = True
-except ImportError:
-    CHAINLIT_AVAILABLE = False
-
-try:
-    import gradio as gr
-    GRADIO_AVAILABLE = True
-except ImportError:
-    GRADIO_AVAILABLE = False
-
-class PraisonAI:
-    def __init__(self, agent_file="agents.yaml", framework="", auto=False, init=False):
-        """
-        Initialize the PraisonAI object with default parameters.
-
-        Parameters:
-            agent_file (str): The default agent file to use. Defaults to "agents.yaml".
-            framework (str): The default framework to use. Defaults to "crewai".
-            auto (bool): A flag indicating whether to enable auto mode. Defaults to False.
-            init (bool): A flag indicating whether to enable initialization mode. Defaults to False.
-
-        Attributes:
-            config_list (list): A list of configuration dictionaries for the OpenAI API.
-            agent_file (str): The agent file to use.
-            framework (str): The framework to use.
-            auto (bool): A flag indicating whether to enable auto mode.
-            init (bool): A flag indicating whether to enable initialization mode.
-        """
-        self.config_list = [
-            {
-                'model': os.environ.get("OPENAI_MODEL_NAME", "gpt-4o"),
-                'base_url': os.environ.get("OPENAI_API_BASE", "https://api.openai.com/v1"),
-                'api_key': os.environ.get("OPENAI_API_KEY")
-            }
-        ]
-        self.agent_file = agent_file
-        self.framework = framework
-        self.auto = auto
-        self.init = init
-
-    def main(self):
-        """
-        The main function of the PraisonAI object. It parses the command-line arguments,
-        initializes the necessary attributes, and then calls the appropriate methods based on the
-        provided arguments.
-
-        Args:
-            self (PraisonAI): An instance of the PraisonAI class.
-    
-        Returns:
-            Any: Depending on the arguments provided, the function may return a result from the
-            AgentsGenerator, a deployment result from the CloudDeployer, or a message indicating
-            the successful creation of a file.
-        """
-        args = self.parse_args()
-        if args is None:
-            agents_generator = AgentsGenerator(self.agent_file, self.framework, self.config_list)
-            result = agents_generator.generate_crew_and_kickoff()
-            return result
-        if args.deploy:
-            from .deploy import CloudDeployer
-            deployer = CloudDeployer()
-            deployer.run_commands()
-            return
-        invocation_cmd = "praisonai"
-        version_string = f"PraisonAI version {__version__}"
-        
-        self.framework = args.framework or self.framework 
-        
-        if args.agent_file:
-            if args.agent_file.startswith("tests.test"): # Argument used for testing purposes. eg: python -m unittest tests.test 
-                print("test")
-            else:
-                self.agent_file = args.agent_file
-        
-        
-        if args.auto or args.init:
-            temp_topic = ' '.join(args.auto) if args.auto else ' '.join(args.init)
-            self.topic = temp_topic
-        elif self.auto or self.init:  # Use the auto attribute if args.auto is not provided
-            self.topic = self.auto
-            
-        if args.auto or self.auto:
-            self.agent_file = "test.yaml"
-            generator = AutoGenerator(topic=self.topic , framework=self.framework, agent_file=self.agent_file)
-            self.agent_file = generator.generate()
-            agents_generator = AgentsGenerator(self.agent_file, self.framework, self.config_list)
-            result = agents_generator.generate_crew_and_kickoff()
-            return result
-        elif args.init or self.init:
-            self.agent_file = "agents.yaml"
-            generator = AutoGenerator(topic=self.topic , framework=self.framework, agent_file=self.agent_file)
-            self.agent_file = generator.generate()
-            print("File {} created successfully".format(self.agent_file))
-            return "File {} created successfully".format(self.agent_file)
-        
-        if args.ui:
-            if args.ui == "gradio":
-                self.create_gradio_interface()
-            elif args.ui == "chainlit":
-                self.create_chainlit_interface()
-            else:
-                # Modify below code to allow default ui
-                agents_generator = AgentsGenerator(self.agent_file, self.framework, self.config_list)
-                result = agents_generator.generate_crew_and_kickoff()
-                return result
-        else:
-            agents_generator = AgentsGenerator(self.agent_file, self.framework, self.config_list)
-            result = agents_generator.generate_crew_and_kickoff()
-            return result
-            
-    def parse_args(self):
-        """
-        Parse the command-line arguments for the PraisonAI CLI.
-
-        Args:
-            self (PraisonAI): An instance of the PraisonAI class.
-
-        Returns:
-            argparse.Namespace: An object containing the parsed command-line arguments.
-
-        Raises:
-            argparse.ArgumentError: If the arguments provided are invalid.
-
-        Example:
-            >>> args = praison_ai.parse_args()
-            >>> print(args.agent_file)  # Output: 'agents.yaml'
-        """
-        parser = argparse.ArgumentParser(prog="praisonai", description="praisonAI command-line interface")
-        parser.add_argument("--framework", choices=["crewai", "autogen"], help="Specify the framework")
-        parser.add_argument("--ui", choices=["chainlit", "gradio"], help="Specify the UI framework (gradio or chainlit).")
-        parser.add_argument("--auto", nargs=argparse.REMAINDER, help="Enable auto mode and pass arguments for it")
-        parser.add_argument("--init", nargs=argparse.REMAINDER, help="Enable auto mode and pass arguments for it")
-        parser.add_argument("agent_file", nargs="?", help="Specify the agent file")
-        parser.add_argument("--deploy", action="store_true", help="Deploy the application")  # New argument
-        args, unknown_args = parser.parse_known_args()
-
-        if unknown_args and unknown_args[0] == '-b' and unknown_args[1] == 'api:app':
-            args.agent_file = 'agents.yaml'
-        if args.agent_file == 'api:app' or args.agent_file == '/app/api:app':
-            args.agent_file = 'agents.yaml'
-        if args.agent_file == 'ui':
-            args.ui = 'chainlit'
-
-        return args
-
-    def create_gradio_interface(self):
-        """
-        Create a Gradio interface for generating agents and performing tasks.
-
-        Args:
-            self (PraisonAI): An instance of the PraisonAI class.
-
-        Returns:
-            None: This method does not return any value. It launches the Gradio interface.
-
-        Raises:
-            None: This method does not raise any exceptions.
-
-        Example:
-            >>> praison_ai.create_gradio_interface()
-        """
-        if GRADIO_AVAILABLE:
-            def generate_crew_and_kickoff_interface(auto_args, framework):
-                """
-                Generate a crew and kick off tasks based on the provided auto arguments and framework.
-
-                Args:
-                    auto_args (list): Topic.
-                    framework (str): The framework to use for generating agents.
-
-                Returns:
-                    str: A string representing the result of generating the crew and kicking off tasks.
-
-                Raises:
-                    None: This method does not raise any exceptions.
-
-                Example:
-                    >>> result = generate_crew_and_kickoff_interface("Create a movie about Cat in Mars", "crewai")
-                    >>> print(result)
-                """
-                self.framework = framework
-                self.agent_file = "test.yaml"
-                generator = AutoGenerator(topic=auto_args , framework=self.framework)
-                self.agent_file = generator.generate()
-                agents_generator = AgentsGenerator(self.agent_file, self.framework, self.config_list)
-                result = agents_generator.generate_crew_and_kickoff()
-                return result
-
-            gr.Interface(
-                fn=generate_crew_and_kickoff_interface,
-                inputs=[gr.Textbox(lines=2, label="Auto Args"), gr.Dropdown(choices=["crewai", "autogen"], label="Framework")],
-                outputs="textbox",
-                title="Praison AI Studio",
-                description="Create Agents and perform tasks",
-                theme="default"
-            ).launch()
-        else:
-            print("ERROR: Gradio is not installed. Please install it with 'pip install gradio' to use this feature.") 
-        
-    def create_chainlit_interface(self):
-        """
-        Create a Chainlit interface for generating agents and performing tasks.
-
-        This function sets up a Chainlit application that listens for messages.
-        When a message is received, it runs PraisonAI with the provided message as the topic.
-        The generated agents are then used to perform tasks.
-
-        Returns:
-            None: This function does not return any value. It starts the Chainlit application.
-        """
-        if CHAINLIT_AVAILABLE:
-            import praisonai
-            os.environ["CHAINLIT_PORT"] = "8082"
-            # Get the path to the 'public' folder within the package
-            public_folder = os.path.join(os.path.dirname(praisonai.__file__), 'public')
-            if not os.path.exists("public"):  # Check if the folder exists in the current directory
-                if os.path.exists(public_folder):
-                    shutil.copytree(public_folder, 'public', dirs_exist_ok=True)
-                    logging.info("Public folder copied successfully!")
-                else:
-                    logging.info("Public folder not found in the package.")
-            else:
-                logging.info("Public folder already exists.")
-            chainlit_ui_path = os.path.join(os.path.dirname(praisonai.__file__), 'chainlit_ui.py')
-            chainlit_run([chainlit_ui_path])
-        else:
-            print("ERROR: Chainlit is not installed. Please install it with 'pip install chainlit' to use the UI.")        
-
-if __name__ == "__main__":
-    praison_ai = PraisonAI()
-    praison_ai.main()

praisonai/deploy.py

@@ -1,138 +0,0 @@
-import subprocess
-import os
-from dotenv import load_dotenv
-
-class CloudDeployer:
-    """
-    A class for deploying a cloud-based application.
-
-    Attributes:
-        None
-
-    Methods:
-        __init__(self):
-            Loads environment variables from .env file or system and sets them.
-
-    """
-    def __init__(self):
-        """
-        Loads environment variables from .env file or system and sets them.
-
-        Parameters:
-            self: An instance of the CloudDeployer class.
-
-        Returns:
-            None
-
-        Raises:
-            None
-
-        """
-        # Load environment variables from .env file or system
-        load_dotenv()
-        self.set_environment_variables()
-
-    def create_dockerfile(self):
-        """
-        Creates a Dockerfile for the application.
-
-        Parameters:
-            self: An instance of the CloudDeployer class.
-
-        Returns:
-            None
-
-        Raises:
-            None
-
-        This method creates a Dockerfile in the current directory with the specified content.
-        The Dockerfile is used to build a Docker image for the application.
-        The content of the Dockerfile includes instructions to use the Python 3.11-slim base image,
-        set the working directory to /app, copy the current directory contents into the container,
-        install the required Python packages (flask, praisonai, gunicorn, and markdown),
-        expose port 8080, and run the application using Gunicorn.
-        """
-        with open("Dockerfile", "w") as file:
-            file.write("FROM python:3.11-slim\n")
-            file.write("WORKDIR /app\n")
-            file.write("COPY . .\n")
-            file.write("RUN pip install flask praisonai==0.0.36 gunicorn markdown\n")
-            file.write("EXPOSE 8080\n")
-            file.write('CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]\n')
-            
-    def create_api_file(self):
-        """
-        Creates an API file for the application.
-
-        Parameters:
-            self (CloudDeployer): An instance of the CloudDeployer class.
-
-        Returns:
-            None
-
-        This method creates an API file named "api.py" in the current directory. The file contains a basic Flask application that uses the PraisonAI library to run a simple agent and returns the output as an HTML page. The application listens on the root path ("/") and uses the Markdown library to format the output.
-        """
-        with open("api.py", "w") as file:
-            file.write("from flask import Flask\n")
-            file.write("from praisonai import PraisonAI\n")
-            file.write("import markdown\n\n")
-            file.write("app = Flask(__name__)\n\n")
-            file.write("def basic():\n")
-            file.write("    praison_ai = PraisonAI(agent_file=\"agents.yaml\")\n")
-            file.write("    return praison_ai.main()\n\n")
-            file.write("@app.route('/')\n")
-            file.write("def home():\n")
-            file.write("    output = basic()\n")
-            file.write("    html_output = markdown.markdown(output)\n")
-            file.write("    return f'<html><body>{html_output}</body></html>'\n\n")
-            file.write("if __name__ == \"__main__\":\n")
-            file.write("    app.run(debug=True)\n")
-    
-    def set_environment_variables(self):
-        """Sets environment variables with fallback to .env values or defaults."""
-        os.environ["OPENAI_MODEL_NAME"] = os.getenv("OPENAI_MODEL_NAME", "gpt-4o")
-        os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY", "Enter your API key")
-        os.environ["OPENAI_API_BASE"] = os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1")
-
-    def run_commands(self):
-        """
-        Sets environment variables with fallback to .env values or defaults.
-
-        Parameters:
-            None
-
-        Returns:
-            None
-
-        Raises:
-            None
-
-        This method sets environment variables for the application. It uses the `os.environ` dictionary to set the following environment variables:
-
-        - `OPENAI_MODEL_NAME`: The name of the OpenAI model to use. If not specified in the .env file, it defaults to "gpt-4o".
-        - `OPENAI_API_KEY`: The API key for accessing the OpenAI API. If not specified in the .env file, it defaults to "Enter your API key".
-        - `OPENAI_API_BASE`: The base URL for the OpenAI API. If not specified in the .env file, it defaults to "https://api.openai.com/v1".
-        """
-        self.create_api_file()
-        self.create_dockerfile()
-        """Runs a sequence of shell commands for deployment, continues on error."""
-        commands = [
-            "yes | gcloud auth configure-docker us-central1-docker.pkg.dev",
-            "gcloud artifacts repositories create praisonai-repository --repository-format=docker --location=us-central1",
-            "docker build --platform linux/amd64 -t gcr.io/$(gcloud config get-value project)/praisonai-app:latest .",
-            "docker tag gcr.io/$(gcloud config get-value project)/praisonai-app:latest us-central1-docker.pkg.dev/$(gcloud config get-value project)/praisonai-repository/praisonai-app:latest",
-            "docker push us-central1-docker.pkg.dev/$(gcloud config get-value project)/praisonai-repository/praisonai-app:latest",
-            "gcloud run deploy praisonai-service --image us-central1-docker.pkg.dev/$(gcloud config get-value project)/praisonai-repository/praisonai-app:latest --platform managed --region us-central1 --allow-unauthenticated --set-env-vars OPENAI_MODEL_NAME=${OPENAI_MODEL_NAME},OPENAI_API_KEY=${OPENAI_API_KEY},OPENAI_API_BASE=${OPENAI_API_BASE}"
-        ]
-
-        for cmd in commands:
-            try:
-                subprocess.run(cmd, shell=True, check=True)
-            except subprocess.CalledProcessError as e:
-                print(f"ERROR: Command '{e.cmd}' failed with exit status {e.returncode}")
-                print(f"Continuing with the next command...")
-
-# Usage
-if __name__ == "__main__":
-    deployer = CloudDeployer()
-    deployer.run_commands()

praisonai/inbuilt_tools/__init__.py

@@ -1,2 +0,0 @@
-# from .base_tool import BaseTool, Tool, tool
-from .autogen_tools import *

praisonai/inbuilt_tools/autogen_tools.py

@@ -1,209 +0,0 @@
-# praisonai/inbuilt_tools/autogen_tools.py
-
-from praisonai_tools import (
-    CodeDocsSearchTool, CSVSearchTool, DirectorySearchTool, DOCXSearchTool, DirectoryReadTool,
-    FileReadTool, TXTSearchTool, JSONSearchTool, MDXSearchTool, PDFSearchTool, RagTool,
-    ScrapeElementFromWebsiteTool, ScrapeWebsiteTool, WebsiteSearchTool, XMLSearchTool, YoutubeChannelSearchTool,
-    YoutubeVideoSearchTool
-)
-from typing import Any
-from autogen import register_function
-import os
-import importlib
-from pathlib import Path
-import os
-import inspect
-import sys
-import logging
-logging.basicConfig(level=os.environ.get('LOGLEVEL', 'INFO'), format='%(asctime)s - %(levelname)s - %(message)s')
-
-def create_autogen_tool_function(tool_name):
-    def autogen_tool(assistant, user_proxy):
-        def register_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
-            def tool_func(query: str) -> Any:
-                tool_instance = tool_class()
-                return tool_instance.run(query=query)
-            register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
-
-        root_directory = os.getcwd()
-        tools_py_path = os.path.join(root_directory, 'tools.py')
-        tools_dir_path = Path(root_directory) / 'tools'
-
-        if os.path.isfile(tools_py_path):
-            print(f"{tools_py_path} exists in the root directory. Loading {tools_py_path} and skipping tools folder.")
-            tool_module = importlib.import_module("tools")
-        elif tools_dir_path.is_dir():
-            print(f"tools folder exists in the root directory. Loading {tool_name} from tools/{tool_name}.py.")
-            tool_module = importlib.import_module(f"tools.{tool_name}")
-        else:
-            raise ImportError("Neither tools.py nor tools directory found in the root directory.")
-
-        Tool = getattr(tool_module, tool_name)
-
-        register_tool(Tool, tool_name, f"Description for {tool_name}", assistant, user_proxy)
-
-    return autogen_tool
-
-# Load tools.py
-sys.path.insert(0, os.getcwd())
-root_directory = os.getcwd()
-tools_py_path = os.path.join(root_directory, 'tools.py')
-tools_dir_path = Path(root_directory) / 'tools'
-
-tools_module = None
-
-if os.path.isfile(tools_py_path):
-    logging.info(f"{tools_py_path} exists in the root directory. Loading {tools_py_path} and skipping tools folder.")
-    tools_module = importlib.import_module("tools")
-elif tools_dir_path.is_dir():
-    logging.info(f"tools folder exists in the root directory. Loading {tool_name} from tools/{tool_name}.py.")
-    tools_module = importlib.import_module(f"tools.{tool_name}")
-
-# Create autogen_TOOL_NAME_HERE function for each tool
-if tools_module is not None:
-    for name, obj in inspect.getmembers(tools_module):
-        if inspect.isclass(obj):
-            globals()[f"autogen_{name}"] = create_autogen_tool_function(name)
-
-def autogen_CodeDocsSearchTool(assistant, user_proxy):
-    def register_code_docs_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
-        def tool_func(docs_url: str, search_query: str) -> Any:
-            tool_instance = tool_class(docs_url=docs_url, search_query=search_query)
-            return tool_instance.run()
-        register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
-    register_code_docs_search_tool(CodeDocsSearchTool, "code_docs_search_tool", "Search a Code Docs content(search_query: 'string', docs_url: 'string') - A tool that can be used to semantic search a query from a Code Docs content.", assistant, user_proxy)
-
-def autogen_CSVSearchTool(assistant, user_proxy):
-    def register_csv_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
-        def tool_func(csv: str, search_query: str) -> Any:
-            tool_instance = tool_class(csv=csv, search_query=search_query)
-            return tool_instance.run()
-        register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
-    register_csv_search_tool(CSVSearchTool, "csv_search_tool", "Search a CSV's content(search_query: 'string', csv: 'string') - A tool that can be used to semantic search a query from a CSV's content.", assistant, user_proxy)
-
-def autogen_DirectorySearchTool(assistant, user_proxy):
-    def register_directory_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
-        def tool_func(directory: str, search_query: str) -> Any:
-            tool_instance = tool_class(directory=directory, search_query=search_query)
-            return tool_instance.run()
-        register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
-    register_directory_search_tool(DirectorySearchTool, "directory_search_tool", "Search a directory's content(search_query: 'string', directory: 'string') - A tool that can be used to semantic search a query from a directory's content.", assistant, user_proxy)
-
-def autogen_DOCXSearchTool(assistant, user_proxy):
-    def register_docx_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
-        def tool_func(docx: str, search_query: str) -> Any:
-            tool_instance = tool_class(docx=docx, search_query=search_query)
-            return tool_instance.run()
-        register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
-    register_docx_search_tool(DOCXSearchTool, "docx_search_tool", "Search a DOCX's content(search_query: 'string', docx: 'string') - A tool that can be used to semantic search a query from a DOCX's content.", assistant, user_proxy)
-
-# DirectoryReadTool
-def autogen_DirectoryReadTool(assistant, user_proxy):
-    def register_directory_read_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
-        def tool_func(directory: str) -> Any:
-            tool_instance = tool_class(directory=directory)
-            return tool_instance.run()
-        register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
-    register_directory_read_tool(DirectoryReadTool, "directory_read_tool", "List files in directory(directory: 'string') - A tool that can be used to recursively list a directory's content.", assistant, user_proxy)
-
-# FileReadTool
-def autogen_FileReadTool(assistant, user_proxy):
-    def register_file_read_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
-        def tool_func(file_path: str) -> Any:
-            tool_instance = tool_class(file_path=file_path)
-            return tool_instance.run()
-        register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
-    register_file_read_tool(FileReadTool, "file_read_tool", "Read a file's content(file_path: 'string') - A tool that can be used to read a file's content.", assistant, user_proxy)
-
-# TXTSearchTool
-def autogen_TXTSearchTool(assistant, user_proxy):
-    def register_txt_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
-        def tool_func(txt: str, search_query: str) -> Any:
-            tool_instance = tool_class(txt=txt, search_query=search_query)
-            return tool_instance.run()
-        register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
-    register_txt_search_tool(TXTSearchTool, "txt_search_tool", "Search a txt's content(search_query: 'string', txt: 'string') - A tool that can be used to semantic search a query from a txt's content.", assistant, user_proxy)
-
-def autogen_JSONSearchTool(assistant, user_proxy):
-    def register_json_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
-        def tool_func(json_path: str, search_query: str) -> Any:
-            tool_instance = tool_class(json_path=json_path, search_query=search_query)
-            return tool_instance.run()
-        register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
-    register_json_search_tool(JSONSearchTool, "json_search_tool", "Search a JSON's content(search_query: 'string', json_path: 'string') - A tool that can be used to semantic search a query from a JSON's content.", assistant, user_proxy)
-
-def autogen_MDXSearchTool(assistant, user_proxy):
-    def register_mdx_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
-        def tool_func(mdx: str, search_query: str) -> Any:
-            tool_instance = tool_class(mdx=mdx, search_query=search_query)
-            return tool_instance.run()
-        register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
-    register_mdx_search_tool(MDXSearchTool, "mdx_search_tool", "Search a MDX's content(search_query: 'string', mdx: 'string') - A tool that can be used to semantic search a query from a MDX's content.", assistant, user_proxy)
-
-def autogen_PDFSearchTool(assistant, user_proxy):
-    def register_pdf_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
-        def tool_func(pdf: str, search_query: str) -> Any:
-            tool_instance = tool_class(pdf=pdf, search_query=search_query)
-            return tool_instance.run()
-        register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
-    register_pdf_search_tool(PDFSearchTool, "pdf_search_tool", "Search a PDF's content(search_query: 'string', pdf: 'string') - A tool that can be used to semantic search a query from a PDF's content.", assistant, user_proxy)
-
-def autogen_RagTool(assistant, user_proxy):
-    def register_rag_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
-        def tool_func(query: str, data: Any) -> Any:
-            tool_instance = tool_class(query=query, data=data)
-            return tool_instance.run()
-        register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
-    register_rag_tool(RagTool, "rag_tool", "Knowledge base(query: 'string', data: Any) - A knowledge base that can be used to answer questions.", assistant, user_proxy)
-
-def autogen_ScrapeElementFromWebsiteTool(assistant, user_proxy):
-    def register_scrape_element_from_website_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
-        def tool_func(website_url: str, element_query: str) -> Any:
-            tool_instance = tool_class(website_url=website_url, element_query=element_query)
-            return tool_instance.run()
-        register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
-    register_scrape_element_from_website_tool(ScrapeElementFromWebsiteTool, "scrape_element_from_website_tool", "Scrape an element from a website(element_query: 'string', website_url: 'string') - A tool that can be used to scrape an element from a website.", assistant, user_proxy)
-
-def autogen_ScrapeWebsiteTool(assistant, user_proxy):
-    def register_scrape_website_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
-        def tool_func(website_url: str) -> Any:
-            tool_instance = tool_class(website_url=website_url)
-            content = tool_instance.run()
-            # Ensure content is properly decoded as UTF-8 if it's a bytes object
-            if isinstance(content, bytes):
-                content = content.decode('utf-8') 
-            return content
-        register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
-    register_scrape_website_tool(ScrapeWebsiteTool, "scrape_website_tool", "Read website content(website_url: 'string') - A tool that can be used to read content from a specified website.", assistant, user_proxy)
-
-def autogen_WebsiteSearchTool(assistant, user_proxy):
-    def register_website_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
-        def tool_func(website: str, search_query: str) -> Any:
-            tool_instance = tool_class(website=website, search_query=search_query)
-            return tool_instance.run()
-        register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
-    register_website_search_tool(WebsiteSearchTool, "website_search_tool", "Search in a specific website(search_query: 'string', website: 'string') - A tool that can be used to semantic search a query from a specific URL content.", assistant, user_proxy)
-
-def autogen_XMLSearchTool(assistant, user_proxy):
-    def register_xml_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
-        def tool_func(xml: str, search_query: str) -> Any:
-            tool_instance = tool_class(xml=xml, search_query=search_query)
-            return tool_instance.run()
-        register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
-    register_xml_search_tool(XMLSearchTool, "xml_search_tool", "Search a XML's content(search_query: 'string', xml: 'string') - A tool that can be used to semantic search a query from a XML's content.", assistant, user_proxy)
-
-def autogen_YoutubeChannelSearchTool(assistant, user_proxy):
-    def register_youtube_channel_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
-        def tool_func(youtube_channel_handle: str, search_query: str) -> Any:
-            tool_instance = tool_class(youtube_channel_handle=youtube_channel_handle, search_query=search_query)
-            return tool_instance.run()
-        register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
-    register_youtube_channel_search_tool(YoutubeChannelSearchTool, "youtube_channel_search_tool", "Search a Youtube Channels content(search_query: 'string', youtube_channel_handle: 'string') - A tool that can be used to semantic search a query from a Youtube Channels content.", assistant, user_proxy)
-
-def autogen_YoutubeVideoSearchTool(assistant, user_proxy):
-    def register_youtube_video_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
-        def tool_func(youtube_video_url: str, search_query: str) -> Any:
-            tool_instance = tool_class(youtube_video_url=youtube_video_url, search_query=search_query)
-            return tool_instance.run()
-        register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
-    register_youtube_video_search_tool(YoutubeVideoSearchTool, "youtube_video_search_tool", "Search a Youtube Video content(search_query: 'string', youtube_video_url: 'string') - A tool that can be used to semantic search a query from a Youtube Video content.", assistant, user_proxy)

praisonai/public/fantasy.svg

@@ -1,3 +0,0 @@
-<?xml version="1.0" encoding="utf-8"?>
-<!-- Uploaded to: SVG Repo, www.svgrepo.com, Generator: SVG Repo Mixer Tools -->
-<svg width="800px" height="800px" viewBox="0 0 1024 1024" class="icon"  version="1.1" xmlns="http://www.w3.org/2000/svg"><path d="M840.5 798.2L662.3 599.5l-151 173.7-173.7-173.7-167.7 201c-21 30.4 0.9 71.8 37.9 71.6l594.7-3.3c36.2-0.1 57.8-40.3 38-70.6z" fill="#FFB89A" /><path d="M741.6 647.3l-52.3-47.7c-12.2-11.2-31.2-10.3-42.4 1.9s-10.3 31.2 1.9 42.4l52.3 47.7c5.8 5.3 13 7.8 20.2 7.8 8.1 0 16.2-3.3 22.2-9.8 11.2-12.1 10.3-31.1-1.9-42.3zM631.2 546.5c-12.4-11-31.4-9.8-42.3 2.6l-98.8 111.7-171-165.7L87.9 724.7c-11.8 11.7-11.8 30.7-0.1 42.4 5.9 5.9 13.6 8.9 21.3 8.9 7.6 0 15.3-2.9 21.1-8.7l189.4-188.1 173.8 168.5L633.8 589c11-12.5 9.8-31.5-2.6-42.5z" fill="#33CC99" /><path d="M721.3 342.8m-35.1 0a35.1 35.1 0 1 0 70.2 0 35.1 35.1 0 1 0-70.2 0Z" fill="#33CC99" /><path d="M743.2 175.1H191.6c-70.6 0-128.3 57.7-128.3 128.3v499.2c0 70.6 57.7 128.3 128.3 128.3h551.5c70.6 0 128.3-57.7 128.3-128.3V303.5c0.1-70.6-57.7-128.4-128.2-128.4z m68.3 627.6c0 18.1-7.1 35.2-20.1 48.2-13 13-30.1 20.1-48.2 20.1H191.6c-18.1 0-35.2-7.1-48.2-20.1-13-13-20.1-30.1-20.1-48.2V303.5c0-18.1 7.1-35.2 20.1-48.2 13-13 30.1-20.1 48.2-20.1h551.5c18.1 0 35.2 7.1 48.2 20.1 13 13 20.1 30.1 20.1 48.2v499.2z" fill="#45484C" /><path d="M799.7 90.9H237.2c-16.6 0-30 13.4-30 30s13.4 30 30 30h562.4c26.1 0 50.8 10.3 69.4 28.9 18.6 18.6 28.9 43.3 28.9 69.4v482.4c0 16.6 13.4 30 30 30s30-13.4 30-30V249.2C958 161.9 887 90.9 799.7 90.9z" fill="#45484C" /></svg>

praisonai/public/game.svg

@@ -1,3 +0,0 @@
-<?xml version="1.0" encoding="utf-8"?>
-<!-- Uploaded to: SVG Repo, www.svgrepo.com, Generator: SVG Repo Mixer Tools -->
-<svg width="800px" height="800px" viewBox="0 0 1024 1024" class="icon"  version="1.1" xmlns="http://www.w3.org/2000/svg"><path d="M570.2 842c-50.6 0-278.7-180-278.7-401.9 0-58.8-2.9-133.1-1-183.9-50.8 3.2-91.4 45.7-91.4 97.3v272.1c37.4 194.7 137.5 334 255.2 334 69.5 0 132.9-48.6 180.9-128.5-20.8 7.1-42.6 10.9-65 10.9z" fill="#FFB89A" /><path d="M926.1 191.8C900.5 74.1 817.9 62.1 704.9 62.1c-29.1 0-60.3 0.8-93 0.8-36 0-70.5-1.1-102.5-1.1-109.7 0-189.8 12.5-201.3 123.7-20.4 198.3 30 617.1 306.1 617.1S939 414.3 926.1 191.8z m-76.9 268.5c-9.5 47.9-22.3 90.8-38.1 127.7-16.8 39.2-37 71.4-60 95.8-37.3 39.5-82.1 58.7-137 58.7-53.4 0-97.6-20.1-134.9-61.6-45.5-50.5-79.8-131.5-99-234.2-15.6-83.5-20.3-178.9-12.4-255.2 1.8-17.3 5.7-30.7 11.6-39.8 4.4-6.8 10.1-11.7 18.7-15.8 25.8-12.5 70.8-14.2 111.4-14.2 15 0 30.7 0.2 47.3 0.5 17.8 0.3 36.2 0.6 55.2 0.6 17.2 0 33.9-0.2 50-0.4 15.1-0.2 29.3-0.4 43.1-0.4 44.5 0 89.5 1.8 118 15.1 15.9 7.4 33.4 20.8 43.6 63 2.6 53.3 3.6 153.5-17.5 260.2z" fill="#4E5155" /><path d="M532 841.7c-32.5 22.3-70.6 33.7-113.2 33.7-29.7 0-57.3-6-82.1-17.7-23.2-11-44.7-27.4-63.9-48.7-46-50.9-80.3-131.3-99.2-232.4-15.1-80.6-19.6-172.9-12-246.8 3-29.5 12-50.2 27.5-63.2 14.2-12 35.1-19.2 65.8-22.9 16.5-2 28.2-16.9 26.3-33.3-2-16.5-16.9-28.2-33.3-26.3-42.9 5.1-73.8 16.7-97.4 36.5-27.9 23.5-43.8 57.2-48.5 103-8.2 79.3-3.4 178.1 12.7 264 9.7 51.9 23.4 99.4 40.6 141.2 19.8 48.1 44.4 88.6 73 120.4 51.6 57.2 115.7 86.2 190.6 86.2 55 0 104.5-14.9 147.2-44.2 13.7-9.4 17.1-28.1 7.7-41.7-9.4-13.7-28.1-17.2-41.8-7.8z" fill="#4E5155" /><path d="M519.7 248.5c-16.6 0-30 13.4-30 30v91.3c0 16.6 13.4 30 30 30s30-13.4 30-30v-91.3c0-16.6-13.5-30-30-30zM299.5 385.5c0-16.6-13.4-30-30-30s-30 13.4-30 30v91.3c0 16.6 13.4 30 30 30s30-13.4 30-30v-91.3zM754.6 248.5c-16.6 0-30 13.4-30 30v91.3c0 16.6 13.4 30 30 30s30-13.4 30-30v-91.3c0-16.6-13.4-30-30-30zM716.7 554.5c0-16.6-13.4-30-30-30H551v30c0 58.5 38.1 123.7 92.8 123.7 22.9 0 45-11.9 62.2-33.6 10.3-13 8.1-31.9-4.9-42.1-13-10.3-31.9-8.1-42.1 4.9-5.3 6.7-11.1 10.9-15.1 10.9-4.3 0-11.9-5.1-19.1-16.4-3.3-5.3-6.2-11.2-8.4-17.4h70.4c16.4 0 29.9-13.4 29.9-30zM401.6 704c-25.4 0-46.1-24.2-46.1-53.9 0-16.6-13.4-30-30-30s-30 13.4-30 30c0 62.8 47.6 113.9 106.1 113.9 16.6 0 30-13.4 30-30s-13.5-30-30-30z" fill="#33CC99" /></svg>

praisonai/public/movie.svg

@@ -1,3 +0,0 @@
-<?xml version="1.0" encoding="utf-8"?>
-<!-- Uploaded to: SVG Repo, www.svgrepo.com, Generator: SVG Repo Mixer Tools -->
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praisonai/public/thriller.svg

@@ -1,3 +0,0 @@
-<?xml version="1.0" encoding="utf-8"?>
-<!-- Uploaded to: SVG Repo, www.svgrepo.com, Generator: SVG Repo Mixer Tools -->
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praisonai/test.py

@@ -1,105 +0,0 @@
-import yaml
-import os
-from rich import print
-from dotenv import load_dotenv
-from crewai import Agent, Task, Crew
-load_dotenv()
-import autogen
-config_list = [
-    {
-        'model': os.environ.get("OPENAI_MODEL_NAME", "gpt-3.5-turbo"),
-        'base_url': os.environ.get("OPENAI_API_BASE", "https://api.openai.com/v1"),
-        'api_key': os.environ.get("OPENAI_API_KEY")
-    }
-]
-
-def generate_crew_and_kickoff(agent_file):
-    """
-    This function generates a crew of agents and kicks off tasks based on the configuration provided in a YAML file.
-
-    Parameters:
-    agent_file (str): The path to the YAML file containing the configuration for the agents and tasks.
-
-    Returns:
-    str: The result of the last task executed by the crew.
-    """
-
-    with open(agent_file, 'r') as f:
-        config = yaml.safe_load(f)
-
-    topic = config['topic']  
-    framework = config['framework']
-
-    agents = {}
-    tasks = []
-    if framework == "autogen":
-        # Load the LLM configuration dynamically
-        print(config_list)
-        llm_config = {"config_list": config_list}
-        
-        for role, details in config['roles'].items():
-            agent_name = details['role'].format(topic=topic).replace("{topic}", topic)
-            agent_goal = details['goal'].format(topic=topic)
-            # Creating an AssistantAgent for each role dynamically
-            agents[role] = autogen.AssistantAgent(
-                name=agent_name,
-                llm_config=llm_config,
-                system_message=details['backstory'].format(topic=topic)+". Reply \"TERMINATE\" in the end when everything is done.",
-            )
-
-            # Preparing tasks for initiate_chats
-            for task_name, task_details in details.get('tasks', {}).items():
-                description_filled = task_details['description'].format(topic=topic)
-                expected_output_filled = task_details['expected_output'].format(topic=topic)
-                
-                chat_task = {
-                    "recipient": agents[role],
-                    "message": description_filled,
-                    "summary_method": "last_msg",  # Customize as needed
-                    # Additional fields like carryover can be added based on dependencies
-                }
-                tasks.append(chat_task)
-
-        # Assuming the user proxy agent is set up as per your requirements
-        user = autogen.UserProxyAgent(
-            name="User",
-            human_input_mode="NEVER",
-            is_termination_msg=lambda x: (x.get("content") or "").rstrip().endswith("TERMINATE"),
-            code_execution_config={
-                "work_dir": "coding",
-                "use_docker": False,
-            },
-            # additional setup for the user proxy agent
-        )
-        response = user.initiate_chats(tasks)
-        result = "### Output ###\n"+response[-1].summary if hasattr(response[-1], 'summary') else ""
-    else:
-        for role, details in config['roles'].items():
-            role_filled = details['role'].format(topic=topic)
-            goal_filled = details['goal'].format(topic=topic)
-            backstory_filled = details['backstory'].format(topic=topic)
-            
-            # Assume tools are loaded and handled here as per your requirements
-            agent = Agent(role=role_filled, goal=goal_filled, backstory=backstory_filled)
-            agents[role] = agent
-
-            for task_name, task_details in details.get('tasks', {}).items():
-                description_filled = task_details['description'].format(topic=topic)
-                expected_output_filled = task_details['expected_output'].format(topic=topic)
-
-                task = Task(description=description_filled, expected_output=expected_output_filled, agent=agent)
-                tasks.append(task)
-
-        crew = Crew(
-            agents=list(agents.values()),
-            tasks=tasks,
-            verbose=2
-        )
-
-        result = crew.kickoff()
-    return result
-
-if __name__ == "__main__":
-    agent_file = "agents.yaml"
-    result = generate_crew_and_kickoff(agent_file)
-    print(result)

praisonai/version.py

@@ -1 +0,0 @@
-__version__ = "0.0.1"

public/fantasy.svg

@@ -1,3 +0,0 @@
-<?xml version="1.0" encoding="utf-8"?>
-<!-- Uploaded to: SVG Repo, www.svgrepo.com, Generator: SVG Repo Mixer Tools -->
-<svg width="800px" height="800px" viewBox="0 0 1024 1024" class="icon"  version="1.1" xmlns="http://www.w3.org/2000/svg"><path d="M840.5 798.2L662.3 599.5l-151 173.7-173.7-173.7-167.7 201c-21 30.4 0.9 71.8 37.9 71.6l594.7-3.3c36.2-0.1 57.8-40.3 38-70.6z" fill="#FFB89A" /><path d="M741.6 647.3l-52.3-47.7c-12.2-11.2-31.2-10.3-42.4 1.9s-10.3 31.2 1.9 42.4l52.3 47.7c5.8 5.3 13 7.8 20.2 7.8 8.1 0 16.2-3.3 22.2-9.8 11.2-12.1 10.3-31.1-1.9-42.3zM631.2 546.5c-12.4-11-31.4-9.8-42.3 2.6l-98.8 111.7-171-165.7L87.9 724.7c-11.8 11.7-11.8 30.7-0.1 42.4 5.9 5.9 13.6 8.9 21.3 8.9 7.6 0 15.3-2.9 21.1-8.7l189.4-188.1 173.8 168.5L633.8 589c11-12.5 9.8-31.5-2.6-42.5z" fill="#33CC99" /><path d="M721.3 342.8m-35.1 0a35.1 35.1 0 1 0 70.2 0 35.1 35.1 0 1 0-70.2 0Z" fill="#33CC99" /><path d="M743.2 175.1H191.6c-70.6 0-128.3 57.7-128.3 128.3v499.2c0 70.6 57.7 128.3 128.3 128.3h551.5c70.6 0 128.3-57.7 128.3-128.3V303.5c0.1-70.6-57.7-128.4-128.2-128.4z m68.3 627.6c0 18.1-7.1 35.2-20.1 48.2-13 13-30.1 20.1-48.2 20.1H191.6c-18.1 0-35.2-7.1-48.2-20.1-13-13-20.1-30.1-20.1-48.2V303.5c0-18.1 7.1-35.2 20.1-48.2 13-13 30.1-20.1 48.2-20.1h551.5c18.1 0 35.2 7.1 48.2 20.1 13 13 20.1 30.1 20.1 48.2v499.2z" fill="#45484C" /><path d="M799.7 90.9H237.2c-16.6 0-30 13.4-30 30s13.4 30 30 30h562.4c26.1 0 50.8 10.3 69.4 28.9 18.6 18.6 28.9 43.3 28.9 69.4v482.4c0 16.6 13.4 30 30 30s30-13.4 30-30V249.2C958 161.9 887 90.9 799.7 90.9z" fill="#45484C" /></svg>

public/game.svg

@@ -1,3 +0,0 @@
-<?xml version="1.0" encoding="utf-8"?>
-<!-- Uploaded to: SVG Repo, www.svgrepo.com, Generator: SVG Repo Mixer Tools -->
-<svg width="800px" height="800px" viewBox="0 0 1024 1024" class="icon"  version="1.1" xmlns="http://www.w3.org/2000/svg"><path d="M570.2 842c-50.6 0-278.7-180-278.7-401.9 0-58.8-2.9-133.1-1-183.9-50.8 3.2-91.4 45.7-91.4 97.3v272.1c37.4 194.7 137.5 334 255.2 334 69.5 0 132.9-48.6 180.9-128.5-20.8 7.1-42.6 10.9-65 10.9z" fill="#FFB89A" /><path d="M926.1 191.8C900.5 74.1 817.9 62.1 704.9 62.1c-29.1 0-60.3 0.8-93 0.8-36 0-70.5-1.1-102.5-1.1-109.7 0-189.8 12.5-201.3 123.7-20.4 198.3 30 617.1 306.1 617.1S939 414.3 926.1 191.8z m-76.9 268.5c-9.5 47.9-22.3 90.8-38.1 127.7-16.8 39.2-37 71.4-60 95.8-37.3 39.5-82.1 58.7-137 58.7-53.4 0-97.6-20.1-134.9-61.6-45.5-50.5-79.8-131.5-99-234.2-15.6-83.5-20.3-178.9-12.4-255.2 1.8-17.3 5.7-30.7 11.6-39.8 4.4-6.8 10.1-11.7 18.7-15.8 25.8-12.5 70.8-14.2 111.4-14.2 15 0 30.7 0.2 47.3 0.5 17.8 0.3 36.2 0.6 55.2 0.6 17.2 0 33.9-0.2 50-0.4 15.1-0.2 29.3-0.4 43.1-0.4 44.5 0 89.5 1.8 118 15.1 15.9 7.4 33.4 20.8 43.6 63 2.6 53.3 3.6 153.5-17.5 260.2z" fill="#4E5155" /><path d="M532 841.7c-32.5 22.3-70.6 33.7-113.2 33.7-29.7 0-57.3-6-82.1-17.7-23.2-11-44.7-27.4-63.9-48.7-46-50.9-80.3-131.3-99.2-232.4-15.1-80.6-19.6-172.9-12-246.8 3-29.5 12-50.2 27.5-63.2 14.2-12 35.1-19.2 65.8-22.9 16.5-2 28.2-16.9 26.3-33.3-2-16.5-16.9-28.2-33.3-26.3-42.9 5.1-73.8 16.7-97.4 36.5-27.9 23.5-43.8 57.2-48.5 103-8.2 79.3-3.4 178.1 12.7 264 9.7 51.9 23.4 99.4 40.6 141.2 19.8 48.1 44.4 88.6 73 120.4 51.6 57.2 115.7 86.2 190.6 86.2 55 0 104.5-14.9 147.2-44.2 13.7-9.4 17.1-28.1 7.7-41.7-9.4-13.7-28.1-17.2-41.8-7.8z" fill="#4E5155" /><path d="M519.7 248.5c-16.6 0-30 13.4-30 30v91.3c0 16.6 13.4 30 30 30s30-13.4 30-30v-91.3c0-16.6-13.5-30-30-30zM299.5 385.5c0-16.6-13.4-30-30-30s-30 13.4-30 30v91.3c0 16.6 13.4 30 30 30s30-13.4 30-30v-91.3zM754.6 248.5c-16.6 0-30 13.4-30 30v91.3c0 16.6 13.4 30 30 30s30-13.4 30-30v-91.3c0-16.6-13.4-30-30-30zM716.7 554.5c0-16.6-13.4-30-30-30H551v30c0 58.5 38.1 123.7 92.8 123.7 22.9 0 45-11.9 62.2-33.6 10.3-13 8.1-31.9-4.9-42.1-13-10.3-31.9-8.1-42.1 4.9-5.3 6.7-11.1 10.9-15.1 10.9-4.3 0-11.9-5.1-19.1-16.4-3.3-5.3-6.2-11.2-8.4-17.4h70.4c16.4 0 29.9-13.4 29.9-30zM401.6 704c-25.4 0-46.1-24.2-46.1-53.9 0-16.6-13.4-30-30-30s-30 13.4-30 30c0 62.8 47.6 113.9 106.1 113.9 16.6 0 30-13.4 30-30s-13.5-30-30-30z" fill="#33CC99" /></svg>

public/index.html

@@ -0,0 +1,43 @@
+<!DOCTYPE html>
+<html lang="en">
+  <head>
+    <meta charset="utf-8" />
+    <link rel="icon" href="%PUBLIC_URL%/favicon.ico" />
+    <meta name="viewport" content="width=device-width, initial-scale=1" />
+    <meta name="theme-color" content="#000000" />
+    <meta
+      name="description"
+      content="Web site created using create-react-app"
+    />
+    <link rel="apple-touch-icon" href="%PUBLIC_URL%/logo192.png" />
+    <!--
+      manifest.json provides metadata used when your web app is installed on a
+      user's mobile device or desktop. See https://developers.google.com/web/fundamentals/web-app-manifest/
+    -->
+    <link rel="manifest" href="%PUBLIC_URL%/manifest.json" />
+    <!--
+      Notice the use of %PUBLIC_URL% in the tags above.
+      It will be replaced with the URL of the `public` folder during the build.
+      Only files inside the `public` folder can be referenced from the HTML.
+
+      Unlike "/favicon.ico" or "favicon.ico", "%PUBLIC_URL%/favicon.ico" will
+      work correctly both with client-side routing and a non-root public URL.
+      Learn how to configure a non-root public URL by running `npm run build`.
+    -->
+    <title>React App</title>
+  </head>
+  <body>
+    <noscript>You need to enable JavaScript to run this app.</noscript>
+    <div id="root"></div>
+    <!--
+      This HTML file is a template.
+      If you open it directly in the browser, you will see an empty page.
+
+      You can add webfonts, meta tags, or analytics to this file.
+      The build step will place the bundled scripts into the <body> tag.
+
+      To begin the development, run `npm start` or `yarn start`.
+      To create a production bundle, use `npm run build` or `yarn build`.
+    -->
+  </body>
+</html>

public/manifest.json

@@ -0,0 +1,25 @@
+{
+  "short_name": "React App",
+  "name": "Create React App Sample",
+  "icons": [
+    {
+      "src": "favicon.ico",
+      "sizes": "64x64 32x32 24x24 16x16",
+      "type": "image/x-icon"
+    },
+    {
+      "src": "logo192.png",
+      "type": "image/png",
+      "sizes": "192x192"
+    },
+    {
+      "src": "logo512.png",
+      "type": "image/png",
+      "sizes": "512x512"
+    }
+  ],
+  "start_url": ".",
+  "display": "standalone",
+  "theme_color": "#000000",
+  "background_color": "#ffffff"
+}