v0.0.31

2026-11-08 17:31:00 -10:00 · 2024-06-19 22:27:47 +01:00
1 parent 340b3b467f
commit 618db97e7f
@@ -1,6 +1,6 @@
 FROM python:3.11-slim
 WORKDIR /app
 COPY . .
-RUN pip install flask praisonai==0.0.30 gunicorn markdown
+RUN pip install flask praisonai==0.0.31 gunicorn markdown
 EXPOSE 8080
 CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]
@@ -84,7 +84,7 @@ class CloudDeployer:
            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==0.0.30 gunicorn markdown\n&#34;)
+            file.write(&#34;RUN pip install flask praisonai==0.0.31 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;)
            
@@ -250,7 +250,7 @@ Loads environment variables from .env file or system and sets them.</p>
            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==0.0.30 gunicorn markdown\n&#34;)
+            file.write(&#34;RUN pip install flask praisonai==0.0.31 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;)
            
@@ -416,7 +416,7 @@ expose port 8080, and run the application using Gunicorn.</p></div>
        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==0.0.30 gunicorn markdown\n&#34;)
+        file.write(&#34;RUN pip install flask praisonai==0.0.31 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;)</code></pre>
 </details>
@@ -1,60 +0,0 @@
-# Create Custom Tools
-## 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
-```
@@ -0,0 +1,135 @@
+# 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
+```
@@ -0,0 +1,42 @@
+# Firecrawl PraisonAI Integration
+
+## Firecrawl running in Localhost:3002
+
+```
+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
+```
@@ -0,0 +1,69 @@
+# Langchain Tools
+
+## Integrate Langchain Direct Tools
+
+```
+pip install youtube_search praisonai langchain_community langchain
+```
+
+```
+# tools.py
+from langchain_community.tools import YouTubeSearchTool
+```
+
+```
+# 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
+
+```
+pip install wikipedia langchain_community
+```
+
+```
+# 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
+```
+
+```
+# 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'
+```
@@ -0,0 +1,32 @@
+# Reddit PraisonAI Integration
+
+```
+export REDDIT_USER_AGENT=[USER]
+export REDDIT_CLIENT_SECRET=xxxxxx
+export REDDIT_CLIENT_ID=xxxxxx
+```
+
+tools.py
+
+```
+from langchain_community.tools.reddit_search.tool import RedditSearchRun
+```
+
+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:
+    - 'RedditSearchRun'
+```
@@ -0,0 +1,15 @@
+# Tavily PraisonAI Integration
+
+````
+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
+```
@@ -0,0 +1,43 @@
+# 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
+
+```
+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
+```
@@ -0,0 +1,38 @@
+# Wikipedia PraisonAI Integration
+
+```
+pip install wikipedia langchain_community
+```
+
+```
+# 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
+```
+
+```
+# 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'
+```
@@ -0,0 +1,36 @@
+# You.com PraisonAI Integration
+
+```
+export YDC_API_KEY=xxxxxxxxxxxx
+```
+tools.py
+```
+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
+
+```
+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'
+```
@@ -0,0 +1,28 @@
+# YouTube Search PraisonAI Integration
+
+```
+pip install youtube_search praisonai langchain_community langchain
+```
+
+```
+# tools.py
+from langchain_community.tools import YouTubeSearchTool
+```
+
+```
+# 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'
+```
@@ -8,20 +8,38 @@ repo_name: MervinPraison/PraisonAI
 repo_url: https://github.com/MervinPraison/PraisonAI

 nav:
-  - Home: index.md
+- 🏠 Home: 'index.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
-  - User Interface: ui.md
-  - Create Custom Tools: create_custom_tools.md
+
+- ⚙️ Tools:
+  - Tools: tools.md
+  - Create Custom Tools: custom_tools.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
-  - Other Models: other_models.md
-  - Contributing: contributing.md
-  - API Reference: api.md
+
+- 🤗 Contributing:
+  - Home: contributing.md
+
+- 🛠️ API reference:
+  - API: api.md
  - API Code: api/praisonai/index.html

 theme:
@@ -356,7 +356,7 @@ files = [
 name = "blinker"
 version = "1.8.2"
 description = "Fast, simple object-to-object and broadcast signaling"
-optional = false
+optional = true
 python-versions = ">=3.8"
 files = [
    {file = "blinker-1.8.2-py3-none-any.whl", hash = "sha256:1779309f71bf239144b9399d06ae925637cf6634cf6bd131104184531bf67c01"},
@@ -365,17 +365,17 @@ files = [

 [[package]]
 name = "boto3"
-version = "1.34.127"
+version = "1.34.129"
 description = "The AWS SDK for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "boto3-1.34.127-py3-none-any.whl", hash = "sha256:d370befe4fb7aea5bc383057d7dad18dda5d0cf3cd3295915bcc8c8c4191905c"},
-    {file = "boto3-1.34.127.tar.gz", hash = "sha256:58ccdeae3a96811ecc9d5d866d8226faadbd0ee1891756e4a04d5186e9a57a64"},
+    {file = "boto3-1.34.129-py3-none-any.whl", hash = "sha256:cc73de1c9d953b1f9da6ee2404af717e93d888f790f3e0291b22d1b8489eb401"},
+    {file = "boto3-1.34.129.tar.gz", hash = "sha256:a7a696fd3e7f5f43a81450b441f3eb6c5a89d28efe867cd97d8fc73ea5d8c139"},
 ]

 [package.dependencies]
-botocore = ">=1.34.127,<1.35.0"
+botocore = ">=1.34.129,<1.35.0"
 jmespath = ">=0.7.1,<2.0.0"
 s3transfer = ">=0.10.0,<0.11.0"

@@ -384,13 +384,13 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]

 [[package]]
 name = "botocore"
-version = "1.34.127"
+version = "1.34.129"
 description = "Low-level, data-driven core of boto 3."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "botocore-1.34.127-py3-none-any.whl", hash = "sha256:e14fa28c8bb141de965e700f88b196d17c67a703c7f0f5c7e14f7dd1cf636011"},
-    {file = "botocore-1.34.127.tar.gz", hash = "sha256:a377871742c40603d559103f19acb7bc93cfaf285e68f21b81637ec396099877"},
+    {file = "botocore-1.34.129-py3-none-any.whl", hash = "sha256:86d3dd30996aa459e9c3321edac12aebe47c73cb4acc7556941f9b4c39726088"},
+    {file = "botocore-1.34.129.tar.gz", hash = "sha256:7c56e25af6112d69c5d14a15b42f76ba7687687abc463a96ac5edca19c0a9c2d"},
 ]

 [package.dependencies]
@@ -656,13 +656,13 @@ files = [

 [[package]]
 name = "chainlit"
-version = "1.1.301"
+version = "1.1.302"
 description = "Build Conversational AI."
 optional = true
 python-versions = "<4.0.0,>=3.8.1"
 files = [
-    {file = "chainlit-1.1.301-py3-none-any.whl", hash = "sha256:d404974c42caf4216a290b82aab4dddfb5c535164c5974e0a207352196fd7cde"},
-    {file = "chainlit-1.1.301.tar.gz", hash = "sha256:c577d405ab00cffb4b4d32976c13cf16095fb89e0bb9e8059c2d3d070b1a20d9"},
+    {file = "chainlit-1.1.302-py3-none-any.whl", hash = "sha256:20fbf5d7e70933552d4a4ebdd2ceefbee179138f798f4dc9160a249842f7d5fd"},
+    {file = "chainlit-1.1.302.tar.gz", hash = "sha256:513e4c126a1db31ea717adf5a7a89a46a42b82eddcdf1d4b6e4e33a9963435ce"},
 ]

 [package.dependencies]
@@ -894,13 +894,13 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""}

 [[package]]
 name = "cohere"
-version = "5.5.3"
+version = "5.5.8"
 description = ""
 optional = false
 python-versions = "<4.0,>=3.8"
 files = [
-    {file = "cohere-5.5.3-py3-none-any.whl", hash = "sha256:99d20129713a6dae052368b4839773a214592a76bee345b94a4846d00f702da3"},
-    {file = "cohere-5.5.3.tar.gz", hash = "sha256:8c7ebe2f5bf83fee8e55a24a0acdd4b0e94de274fd0ef32b285978289a03e930"},
+    {file = "cohere-5.5.8-py3-none-any.whl", hash = "sha256:e1ed84b90eadd13c6a68ee28e378a0bb955f8945eadc6eb7ee126b3399cafd54"},
+    {file = "cohere-5.5.8.tar.gz", hash = "sha256:84ce7666ff8fbdf4f41fb5f6ca452ab2639a514bc88967a2854a9b1b820d6ea0"},
 ]

 [package.dependencies]
@@ -908,9 +908,10 @@ boto3 = ">=1.34.0,<2.0.0"
 fastavro = ">=1.9.4,<2.0.0"
 httpx = ">=0.21.2"
 httpx-sse = ">=0.4.0,<0.5.0"
+parameterized = ">=0.9.0,<0.10.0"
 pydantic = ">=1.9.2"
 requests = ">=2.0.0,<3.0.0"
-tokenizers = ">=0.19,<0.20"
+tokenizers = ">=0.15,<1"
 types-requests = ">=2.0.0,<3.0.0"
 typing_extensions = ">=4.0.0"

@@ -1482,7 +1483,7 @@ vw = ["scikit-learn", "vowpalwabbit (>=8.10.0,<9.0.0)"]
 name = "flask"
 version = "3.0.3"
 description = "A simple framework for building complex web applications."
-optional = false
+optional = true
 python-versions = ">=3.8"
 files = [
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@@ -1789,13 +1790,13 @@ requests = ["requests (>=2.20.0,<3.0.0.dev0)"]

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-version = "1.55.0"
+version = "1.56.0"
 description = "Vertex AI API client library"
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 python-versions = ">=3.8"
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 description = "Safely pass data to untrusted environments and back."
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@@ -2545,7 +2546,7 @@ files = [
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 ]

 [package.dependencies]
-greenlet = {version = "!=0.4.17", markers = "platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\""}
+greenlet = {version = "!=0.4.17", markers = "python_version < \"3.13\" and (platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\")"}
 typing-extensions = ">=4.6.0"

 [package.extras]
@@ -6251,7 +6266,7 @@ files = [
 name = "werkzeug"
 version = "3.0.3"
 description = "The comprehensive WSGI web application library."
-optional = false
+optional = true
 python-versions = ">=3.8"
 files = [
    {file = "werkzeug-3.0.3-py3-none-any.whl", hash = "sha256:fc9645dc43e03e4d630d23143a04a7f947a9a3b5727cd535fdfe155a17cc48c8"},
@@ -6511,10 +6526,11 @@ doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linke
 test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]

 [extras]
+api = ["flask"]
 gradio = ["gradio"]
 ui = ["chainlit"]

 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<3.13"
-content-hash = "2ba6072efc0fe2fb868bf1b7a5c3947f488cf1637e1a585acfae961561f6bde0"
+content-hash = "ed013174f31333cb688d1c84764578d8a0df409e1f70ddb544fdaa44a29e7cc1"
@@ -56,7 +56,7 @@ class CloudDeployer:
            file.write("FROM python:3.11-slim\n")
            file.write("WORKDIR /app\n")
            file.write("COPY . .\n")
-            file.write("RUN pip install flask praisonai==0.0.30 gunicorn markdown\n")
+            file.write("RUN pip install flask praisonai==0.0.31 gunicorn markdown\n")
            file.write("EXPOSE 8080\n")
            file.write('CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]\n')
            
@@ -166,7 +166,11 @@ 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)
-            return tool_instance.run()
+            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)

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "PraisonAI"
-version = "0.0.30"
+version = "0.0.31"
 description = "PraisonAI application combines AutoGen and CrewAI or similar frameworks into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customization, and efficient human-agent collaboration."
 authors = ["Mervin Praison"]
 license = ""
@@ -21,10 +21,9 @@ crewai = ">=0.30.4"
 markdown = ">=3.5"
 crewai-tools = "^0.2.6"
 praisonai-tools = ">=0.0.4"
-blinker = ">=1.8.2"
-Flask = ">=3.0.0"
 chainlit = {version = "^1.1.301", optional = true}
 gradio = {version = ">=4.26.0", optional = true}
+flask = {version = ">=3.0.0", optional = true}

 [tool.poetry.dev-dependencies]
 pytest = "^8.0.0"
@@ -39,4 +38,5 @@ praisonai = "praisonai.__main__:main"

 [tool.poetry.extras]
 ui = ["chainlit"]
-gradio = ["gradio"]
+gradio = ["gradio"]
+api = ["flask"]