diff --git a/.env.template b/.env.template
new file mode 100644
index 0000000..3da3625
--- /dev/null
+++ b/.env.template
@@ -0,0 +1,21 @@
+# LlamaCloud API Configuration
+# Get these values from https://cloud.llamaindex.ai/
+
+# Your LlamaCloud API key (required)
+LLAMA_CLOUD_API_KEY=your_api_key_here
+
+# Your LlamaCloud project ID (required)
+LLAMA_CLOUD_PROJECT_ID=your_project_id_here
+
+# Your LlamaCloud organization ID (required)
+LLAMA_CLOUD_ORGANIZATION_ID=your_organization_id_here
+
+# Name for your extraction agent (optional - defaults to "invoice_extraction_agent")
+LLAMA_CLOUD_AGENT_NAME=invoice_extraction_agent
+
+# Instructions:
+# 1. Copy this file to .env: cp .env.template .env
+# 2. Replace the placeholder values with your actual LlamaCloud credentials
+# 3. Save the .env file
+# 4. Run: python create_agent.py
+# 5. Run: python sample.py
\ No newline at end of file
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..d3c1754
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,83 @@
+# Environment variables
+.env
+.env.local
+.env.production
+
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# Virtual environments
+.venv/
+venv/
+ENV/
+env/
+.env/
+
+# IDEs
+.vscode/
+.idea/
+*.swp
+*.swo
+*~
+
+# OS
+.DS_Store
+.DS_Store?
+._*
+.Spotlight-V100
+.Trashes
+ehthumbs.db
+Thumbs.db
+
+# Streamlit
+.streamlit/
+
+# Temporary files and development folders
+tmp_*/
+temp/
+*.tmp
+*.log
+
+# Node modules (for any JS tooling)
+node_modules/
+*.log
+npm-debug.log*
+
+# Next.js (excluding from repo)
+nextjs_app/
+nextjs_app2/
+
+# Coverage reports
+htmlcov/
+.coverage
+.coverage.*
+coverage.xml
+*.cover
+
+# Pytest
+.pytest_cache/
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
\ No newline at end of file
diff --git a/README.md b/README.md
index e66d3f3..d02476c 100644
--- a/README.md
+++ b/README.md
@@ -1,62 +1,177 @@
-# Invoice Extraction Streamlit App
+# Invoice Processing App - Vibe Coding Example
 
-A simple Streamlit application that allows users to upload invoice images and extract structured data using LlamaCloud's extraction agent.
+This repository demonstrates how to "vibe code" a full-stack invoice processing application using Cursor/Claude Code from a simple starting file. This showcases building a complete Streamlit application with LlamaCloud's document extraction capabilities.
 
-## Features
+## 🚀 Quick Start - Vibe Coding Your Own App
 
-- 📄 Upload invoice images (JPG, JPEG, PNG, BMP, TIFF)
-- 🔍 Automatic data extraction using LlamaCloud's kaggle_invoice_agent
-- 📊 Display extracted data in both JSON and formatted views
-- 🎨 Clean, modern UI with progress indicators
+This repository demonstrates **vibe coding** - rapidly building applications through iterative AI-assisted development. The goal is to transform a simple script into a full-featured application using Cursor/Claude Code.
 
-## Setup
+### Prerequisites
+- Python 3.11+
+- LlamaCloud account and API key ([Get one here](https://cloud.llamaindex.ai/))
 
-1. **Install dependencies:**
-   ```bash
-   pip install -r requirements.txt
-   ```
+### Setup Process
 
-2. **Set up environment variables:**
-   Create a `.env` file in the project root with your LlamaCloud API credentials:
-   ```
-   LLAMA_CLOUD_API_KEY=your_api_key_here
-   ```
-
-3. **Run the app:**
-   ```bash
-   streamlit run app.py
-   ```
-
-4. **Open your browser:**
-   The app will be available at `http://localhost:8501`
-
-## Usage
-
-1. Upload an invoice image using the file uploader
-2. Click "Extract Data" to run the extraction
-3. View the extracted data in both JSON and formatted formats
-4. The sidebar shows configuration details and app information
-
-## Configuration
-
-The app uses the following configuration (from `sample.py`):
-- **Project ID:** `2fef999e-1073-40e6-aeb3-1f3c0e64d99b`
-- **Organization ID:** `43b88c8f-e488-46f6-9013-698e3d2e374a`
-- **Agent:** `kaggle_invoice_agent`
-
-## File Structure
-
-```
-jerry_invoice_streamlit/
-├── app.py              # Main Streamlit application
-├── sample.py           # Original sample code
-├── requirements.txt    # Python dependencies
-├── README.md          # This file
-└── .env               # Environment variables (create this)
+#### 1. **Clone and Install**
+```bash
+git clone <your-repo-url>
+cd invoice-extraction-vibe-coding
+pip install -r requirements.txt
 ```
 
-## Troubleshooting
+#### 2. **Set Up LlamaCloud**
+1. Go to [LlamaCloud](https://cloud.llamaindex.ai/) and log in
+2. Create a new project and note your `project_id` and `organization_id`
+3. Get your API key from your account settings
 
-- **API Key Issues:** Make sure your `.env` file contains the correct `LLAMA_CLOUD_API_KEY`
-- **Import Errors:** Ensure all dependencies are installed with `pip install -r requirements.txt`
-- **File Upload Issues:** Check that your image file is in a supported format 
\ No newline at end of file
+#### 3. **Configure Environment**
+Copy the template and add your credentials:
+```bash
+cp .env.template .env
+```
+Then edit `.env` with your LlamaCloud credentials:
+```env
+LLAMA_CLOUD_API_KEY=your_actual_api_key
+LLAMA_CLOUD_PROJECT_ID=your_actual_project_id
+LLAMA_CLOUD_ORGANIZATION_ID=your_actual_organization_id
+LLAMA_CLOUD_AGENT_NAME=invoice_extraction_agent
+```
+
+#### 4. **Create Extraction Agent**
+```bash
+python create_agent.py
+```
+This automatically creates an extraction agent in LlamaCloud using the invoice schema from `sample_data/sample_schema.py`.
+
+#### 5. **Test Your Setup**
+```bash
+python sample.py
+```
+This validates your configuration and runs extraction on the sample invoice. You should see structured JSON output.
+
+#### 6. **Start Vibe Coding!**
+Now comes the fun part - use the `cursor_prompt.md` template to transform `sample.py` into a full application:
+
+1. Open your project in Cursor or Claude Code
+2. Copy the prompt from `cursor_prompt.md`
+3. Start iterating: "Transform this simple script into a professional Streamlit app with..."
+4. Build features incrementally through natural language prompts
+
+#### 7. **Compare Your Result**
+When you're done vibe coding, compare your creation with our `app.py` to see different approaches!
+
+## 📁 Project Structure
+
+```
+invoice-extraction-vibe-coding/
+├── app.py                      # Full-featured Streamlit application (generated)
+├── sample.py                   # Starting point - simple extraction script
+├── create_agent.py            # Script to create LlamaCloud extraction agent
+├── .env.template              # Environment variables template
+├── requirements.txt           # Python dependencies
+├── cursor_prompt.md           # Cursor prompt template for vibe coding
+└── sample_data/               # Sample invoice data
+    ├── batch1-0274.jpg       # Sample invoice image
+    └── sample_schema.py      # Pydantic models for data structure
+```
+
+## 🎯 What This Demonstrates
+
+### From Simple Script to Full App
+- **Starting Point**: `sample.py` - A basic 20-line script that calls LlamaCloud extraction
+- **End Result**: `app.py` - A fully-featured Streamlit application with:
+  - Professional UI with custom CSS
+  - File upload functionality
+  - Real-time data extraction
+  - Structured data display
+  - Invoice history tracking
+  - Status indicators and metrics
+
+### LlamaCloud Integration
+- Document parsing and extraction using LlamaExtract
+- Structured data output using predefined schemas
+- Integration with kaggle_invoice_agent
+
+## 🛠 Vibe Coding with the Prompt Template
+
+This project demonstrates **vibe coding** - rapidly building applications through conversational AI development.
+
+### Start with `cursor_prompt.md`
+The `cursor_prompt.md` file contains a comprehensive prompt template that can potentially **one-shot** the entire application transformation:
+
+1. **Copy the main prompt** from `cursor_prompt.md`
+2. **Paste it into Cursor/Claude Code** with your `sample.py` file open
+3. **Watch the magic happen** - the AI may build the entire Streamlit app in one go!
+
+### The Vibe Coding Process:
+- **Start Simple**: `sample.py` - a working 20-line script
+- **Use Natural Language**: Describe what you want, not how to build it
+- **Iterate if Needed**: Add features through follow-up prompts
+- **Build Incrementally**: Test and refine each addition
+
+### Why the Template Works:
+- **Comprehensive Scope**: Covers UI, functionality, and user experience
+- **Clear Context**: References your actual files and sample data
+- **Specific Examples**: Shows exactly what features to build
+- **Production-Ready**: Asks for professional-quality output
+
+Try the full prompt first - you might be surprised how much gets built in a single interaction!
+
+## 🔧 Features
+
+### Current Application (`app.py`)
+- 📄 **Multi-format Support**: JPG, JPEG, PNG, BMP, TIFF
+- 🎨 **Professional UI**: Custom CSS styling and responsive design
+- 📊 **Data Visualization**: Structured invoice data display
+- 📈 **Analytics**: Processing metrics and status tracking
+- 🔄 **Real-time Processing**: Live extraction with progress indicators
+- 💾 **Session Storage**: Invoice history within session
+
+### Data Schema
+The application extracts structured invoice data including:
+- Invoice metadata (number, date)
+- Seller and client information
+- Line items with pricing details
+- VAT calculations and summaries
+- Total amounts
+
+See `sample_data/sample_schema.py` for the complete Pydantic model definitions.
+
+## 📝 Usage
+
+1. **Upload Invoice**: Drag and drop or select an invoice image
+2. **Extract Data**: Click the extraction button to process with LlamaCloud
+3. **View Results**: See structured data in formatted tables and JSON
+4. **Track History**: View processed invoices in the history tab
+
+## 🔑 Configuration
+
+Update these values in your application for your own LlamaCloud setup:
+```python
+project_id = "your-project-id"
+organization_id = "your-organization-id"
+agent_name = "your-agent-name"
+```
+
+## 🐛 Troubleshooting
+
+### Common Issues
+- **API Key**: Ensure your LlamaCloud API key is valid and in `.env`
+- **Dependencies**: Run `pip install -r requirements.txt`
+- **File Formats**: Only image formats are supported
+- **File Size**: Keep images under 10MB for best performance
+
+## 🤝 Contributing
+
+This is an educational example demonstrating the vibe coding approach. Feel free to:
+- Fork and modify for your use case
+- Share improvements and variations
+- Use as a starting point for your own projects
+
+## 📄 License
+
+Open source - use freely for learning and development.
+
+---
+
+**Built with ❤️ using LlamaIndex, LlamaCloud, and the power of vibe coding with Cursor/Claude Code!** 
\ No newline at end of file
diff --git a/create_agent.py b/create_agent.py
new file mode 100644
index 0000000..ac1ad5f
--- /dev/null
+++ b/create_agent.py
@@ -0,0 +1,126 @@
+"""
+Create LlamaCloud extraction agent for invoice processing.
+
+Run this script to automatically create an extraction agent with the invoice schema
+before testing with sample.py or building your Streamlit app.
+"""
+
+import os
+import sys
+from dotenv import load_dotenv
+from llama_cloud_services import LlamaExtract
+from llama_cloud.core.api_error import ApiError
+from sample_data.sample_schema import Invoice
+
+# Load environment variables
+load_dotenv()
+
+def create_invoice_agent():
+    """Create or update the invoice extraction agent."""
+    
+    # Get configuration
+    project_id = os.getenv("LLAMA_CLOUD_PROJECT_ID")
+    organization_id = os.getenv("LLAMA_CLOUD_ORGANIZATION_ID")
+    agent_name = os.getenv("LLAMA_CLOUD_AGENT_NAME", "invoice_extraction_agent")
+    
+    # Validate configuration
+    if not project_id or project_id == "your-project-id-here":
+        print("❌ Error: LLAMA_CLOUD_PROJECT_ID not configured!")
+        print("Please set your project ID in the .env file")
+        return False
+        
+    if not organization_id or organization_id == "your-organization-id-here":
+        print("❌ Error: LLAMA_CLOUD_ORGANIZATION_ID not configured!")
+        print("Please set your organization ID in the .env file")
+        return False
+    
+    try:
+        print("🚀 Initializing LlamaCloud extraction service...")
+        
+        # Initialize LlamaExtract
+        extract = LlamaExtract(
+            show_progress=True,
+            check_interval=5,
+            project_id=project_id,
+            organization_id=organization_id
+        )
+        
+        print(f"✅ Connected to LlamaCloud")
+        print(f"📋 Project ID: {project_id}")
+        print(f"🏢 Organization ID: {organization_id}")
+        print(f"🤖 Agent name: {agent_name}")
+        
+        # Check if agent already exists
+        try:
+            existing_agent = extract.get_agent(name=agent_name)
+            if existing_agent:
+                print(f"✅ Agent '{agent_name}' already exists!")
+                print(f"🤖 Agent ID: {existing_agent.id}")
+                print("📝 You can use this existing agent for extraction.")
+                return True
+        except ApiError as e:
+            if e.status_code == 404:
+                print(f"📝 Agent '{agent_name}' does not exist, creating new one...")
+            else:
+                raise
+        
+        # Create new agent with invoice schema
+        print("🔧 Creating new extraction agent...")
+        print("📊 Using invoice schema from sample_data/sample_schema.py")
+        
+        agent = extract.create_agent(
+            name=agent_name, 
+            data_schema=Invoice
+        )
+        
+        print("🎉 Success! Extraction agent created successfully!")
+        print(f"🤖 Agent ID: {agent.id}")
+        print(f"📝 Agent Name: {agent.name}")
+        
+        print("\n📋 Schema Summary:")
+        print("  • Invoice metadata (number, date)")
+        print("  • Seller and client information")
+        print("  • Line items with pricing details")
+        print("  • VAT calculations and summaries")
+        print("  • Total amounts")
+        
+        print(f"\n✅ Setup complete! Your agent '{agent_name}' is ready to use.")
+        print("\n🚀 Next steps:")
+        print("1. Run 'python sample.py' to test extraction")
+        print("2. Use 'cursor_prompt.md' to build your Streamlit app")
+        
+        return True
+        
+    except Exception as e:
+        print(f"❌ Error creating agent: {e}")
+        print("\n🔧 Troubleshooting:")
+        print("1. Check your .env file has correct credentials")
+        print("2. Verify your LlamaCloud account has necessary permissions")
+        print("3. Ensure you have a valid project and organization setup")
+        return False
+
+def main():
+    """Main function."""
+    print("🔨 LlamaCloud Invoice Extraction Agent Setup")
+    print("=" * 50)
+    
+    # Check if .env file exists
+    if not os.path.exists(".env"):
+        print("❌ No .env file found!")
+        print("\nPlease create a .env file with:")
+        print("LLAMA_CLOUD_API_KEY=your_api_key_here")
+        print("LLAMA_CLOUD_PROJECT_ID=your_project_id_here")
+        print("LLAMA_CLOUD_ORGANIZATION_ID=your_organization_id_here")
+        print("LLAMA_CLOUD_AGENT_NAME=invoice_extraction_agent")
+        sys.exit(1)
+    
+    # Create agent
+    success = create_invoice_agent()
+    
+    if success:
+        sys.exit(0)
+    else:
+        sys.exit(1)
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/cursor_prompt.md b/cursor_prompt.md
new file mode 100644
index 0000000..265dcea
--- /dev/null
+++ b/cursor_prompt.md
@@ -0,0 +1,111 @@
+# Cursor Prompt for Invoice Processing App
+
+Use this prompt to transform the simple `sample.py` script into a full-featured Streamlit invoice processing application.
+
+## Main Prompt
+
+```
+I have a simple piece of sample code (sample.py) that initializes a LlamaCloud extraction agent and runs extraction over a sample invoice image. I want to transform this into a professional Streamlit web application.
+
+Here's what I'm starting with:
+- sample.py: Basic extraction script using LlamaExtract with kaggle_invoice_agent
+- sample_data/batch1-0274.jpg: Sample invoice image for testing
+- sample_data/sample_schema.py: Pydantic models defining the expected invoice data structure
+
+Transform this into a comprehensive Streamlit app with:
+
+## Core Features
+1. **File Upload Interface**
+   - Support multiple document formats:
+     - PDF files (.pdf)
+     - Word documents (.docx)  
+     - Image files (.jpg, .jpeg, .png, .bmp, .tiff)
+     - And other common document formats supported by LlamaCloud
+   - Drag-and-drop functionality
+   - Document preview before processing
+   - File validation and size limits
+
+2. **Data Extraction & Display**
+   - Use the same LlamaExtract agent from sample.py
+   - Process uploaded invoices in real-time
+   - Display extracted data using the schema structure from sample_schema.py
+   - Show both formatted view and raw JSON
+
+3. **Professional UI Design**
+   - Modern, clean interface with custom CSS
+   - Responsive layout with proper spacing
+   - Professional color scheme and typography
+   - Loading states and progress indicators
+
+## Data Presentation
+Based on the sample_schema.py structure, display:
+- Invoice overview (number, date, status)
+- Seller and client information in organized cards
+- Line items in a structured table
+- VAT summary with calculations
+- Total amounts prominently displayed
+
+## Additional Features
+- Session-based invoice history
+- Processing status and metrics in sidebar
+- Error handling with user-friendly messages
+- System status indicators
+- Usage statistics and tips
+
+## Technical Requirements
+- Use the same project_id and organization_id from sample.py
+- Maintain the kaggle_invoice_agent configuration
+- Include proper environment variable handling for API keys
+- Add comprehensive error handling for API failures
+
+Make it production-ready with clean code structure, proper documentation, and professional presentation. The app should feel like a commercial invoice processing tool while maintaining the simplicity of the original extraction logic.
+
+Test with the provided sample_data/batch1-0274.jpg to ensure everything works correctly.
+```
+
+## Follow-up Enhancement Prompts
+
+### UI Polish
+```
+Enhance the visual design with:
+- Custom CSS for a more professional look
+- Better color scheme (consider invoice/business themes)
+- Improved typography and spacing
+- Status badges and progress indicators
+- Responsive design for different screen sizes
+```
+
+### Feature Expansion
+```
+Add these capabilities:
+- Export results to JSON/CSV
+- Batch processing of multiple invoices
+- Invoice data validation against schema
+- Search and filter processed invoices
+- Comparison between different invoices
+```
+
+### User Experience
+```
+Improve the user experience with:
+- Better error messages and recovery options
+- Helpful tooltips and guidance
+- Keyboard shortcuts and accessibility
+- Auto-save of processing results
+- Undo/redo functionality where applicable
+```
+
+## Key Context Files
+
+When using this prompt, make sure to include:
+- `sample.py` - The starting extraction script
+- `sample_data/sample_schema.py` - Pydantic models for data structure
+- `sample_data/batch1-0274.jpg` - Test invoice image
+- Current `requirements.txt` - Dependencies list
+
+## Expected Transformation
+
+**From:** 20-line extraction script
+**To:** Full-featured web application with professional UI, comprehensive functionality, file handling, data visualization, and production-ready code quality.
+
+The result should demonstrate the power of "vibe coding" - rapidly building complex applications through iterative AI-assisted development.
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
index 5202220..5e8ebda 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,4 @@
 streamlit>=1.28.0
-python-dotenv>=1.0.0
-llama-cloud-services>=0.1.0
-llama-cloud>=0.1.0 
\ No newline at end of file
+python-dotenv>=1.1.1
+llama-cloud-services==0.6.49
+llama-cloud==0.1.34
\ No newline at end of file
diff --git a/sample.py b/sample.py
index 82d97bb..362a33c 100644
--- a/sample.py
+++ b/sample.py
@@ -1,22 +1,83 @@
+"""
+Simple invoice extraction script using LlamaCloud.
+
+This is the starting point for vibe coding a full Streamlit application.
+Configure your LlamaCloud credentials and run this script to test extraction
+before building the full web application.
+"""
+
+import os
+import json
 from dotenv import load_dotenv
 from llama_cloud_services import LlamaExtract
 from llama_cloud.core.api_error import ApiError
 
+# Load environment variables
+load_dotenv()
 
-project_id = "2fef999e-1073-40e6-aeb3-1f3c0e64d99b"
-organization_id = "43b88c8f-e488-46f6-9013-698e3d2e374a"
+# Configuration - Update these with your LlamaCloud details
+PROJECT_ID = os.getenv("LLAMA_CLOUD_PROJECT_ID", "your-project-id-here")
+ORGANIZATION_ID = os.getenv("LLAMA_CLOUD_ORGANIZATION_ID", "your-organization-id-here")
+AGENT_NAME = os.getenv("LLAMA_CLOUD_AGENT_NAME", "your-agent-name-here")
 
-# Optionally, add your project id/organization id
-extract = LlamaExtract(
-    show_progress=False, 
-    check_interval=5,
-    project_id=project_id,
-    organization_id=organization_id
-)
+# Sample image path
+SAMPLE_IMAGE = "sample_data/batch1-0274.jpg"
 
+def main():
+    """Run invoice extraction on sample image."""
+    
+    # Validate configuration
+    if PROJECT_ID == "your-project-id-here" or ORGANIZATION_ID == "your-organization-id-here":
+        print("❌ Please configure your LlamaCloud credentials!")
+        print("Update your .env file with:")
+        print("LLAMA_CLOUD_PROJECT_ID=your-actual-project-id")
+        print("LLAMA_CLOUD_ORGANIZATION_ID=your-actual-organization-id")
+        print("LLAMA_CLOUD_AGENT_NAME=your-actual-agent-name")
+        return
+    
+    try:
+        print("🚀 Initializing LlamaCloud extraction agent...")
+        
+        # Initialize the extraction service
+        extract = LlamaExtract(
+            show_progress=True,  # Show progress for better UX
+            check_interval=5,
+            project_id=PROJECT_ID,
+            organization_id=ORGANIZATION_ID
+        )
+        
+        # Get the configured agent
+        agent = extract.get_agent(name=AGENT_NAME)
+        print(f"✅ Successfully connected to agent: {AGENT_NAME}")
+        
+        # Run extraction on sample image
+        print(f"📄 Processing sample invoice: {SAMPLE_IMAGE}")
+        result = agent.extract(SAMPLE_IMAGE)
+        
+        # Display results
+        print("✅ Extraction completed successfully!")
+        print("\n📊 Extracted Data:")
+        print("=" * 50)
+        print(json.dumps(result.data, indent=2))
+        
+        # Save results for reference
+        output_file = "sample_output.json"
+        with open(output_file, "w") as f:
+            json.dump(result.data, f, indent=2)
+        print(f"\n💾 Results saved to: {output_file}")
+        
+        print("\n🎉 Success! You're ready to start vibe coding the Streamlit app!")
+        print("Use cursor_prompt.md to transform this script into a full application.")
+        
+    except ApiError as e:
+        print(f"❌ API Error: {e}")
+        print("Check your API key and agent configuration.")
+    except FileNotFoundError:
+        print(f"❌ Sample image not found: {SAMPLE_IMAGE}")
+        print("Make sure the sample_data directory exists with the sample image.")
+    except Exception as e:
+        print(f"❌ Unexpected error: {e}")
 
-agent = extract.get_agent(name="kaggle_invoice_agent")
-
-result = agent.extract("batch1-0274.jpg")
-result.data # outputs json of the extracted output according to the schema defined in the agent
+if __name__ == "__main__":
+    main()