🤖 PDF Analysis with CrewAI - Streamlit Application

A powerful Streamlit application that uses AI agents to analyze PDF documents, generate insights, and provide comprehensive project analysis using CrewAI.

🚀 Features

• PDF Upload & Analysis: Upload any PDF and get AI-powered analysis
• Multi-Agent System: Three specialized AI agents working together
  • Project Analyst: Identifies risks, strengths, and opportunities
  • Resource Search Specialist: Discovers relevant tools and resources
  • Code Architect: Designs system architecture and generates code
• Real-time Results: See analysis progress and results in real-time
• File Generation: Automatically generates markdown reports and documentation
• Download Capability: Download all generated files for offline use

📋 Prerequisites

Before running this application, make sure you have:

• Python 3.8+ installed on your system
• Git for version control
• GitHub account for hosting the repository
• API Keys for the required services (see Setup section)

🛠️ Installation & Setup

1. Clone the Repository

# Clone the repository to your local machine
git clone https://github.com/yourusername/pdf-analysis-crewai.git

# Navigate to the project directory
cd pdf-analysis-crewai

2. Create Virtual Environment (Recommended)

# Create a virtual environment
python -m venv venv

# Activate the virtual environment
# On Windows:
venv\Scripts\activate

# On macOS/Linux:
source venv/bin/activate

3. Install Dependencies

# Install all required packages
pip install -r requirements.txt

4. Environment Configuration

Create a .env file in the root directory with your API keys:

# Required: Your AIML API key for GPT-5 access
AIML_API_KEY=your_aiml_api_key_here

# Optional: GitHub token for enhanced repository search
GITHUB_TOKEN=your_github_token_here

# Optional: Linkup API key for additional search capabilities
LINKUP_API_KEY=your_linkup_api_key_here

# Optional: EXA API key for scientific research
EXA_API_KEY=your_exa_api_key_here

Note: Only AIML_API_KEY is required. Other keys are optional but enhance functionality.

🚀 Running the Application

Option 1: Direct Streamlit Run

# Make sure your virtual environment is activated
streamlit run streamlit_app.py

Option 2: Using Python Module

# Alternative way to run
python -m streamlit run streamlit_app.py

Option 3: Development Mode

# Run with auto-reload for development
streamlit run streamlit_app.py --server.runOnSave true

🌐 Accessing the Application

Once running, the application will be available at:

• Local: http://localhost:8501
• Network: http://your-ip-address:8501

The app will automatically open in your default web browser.

📱 How to Use

1. Upload PDF: Use the file uploader to select a PDF document
2. Start Analysis: Click the "🚀 Start AI Analysis" button
3. Wait for Results: Monitor the progress as AI agents analyze your document
4. View Results: See the analysis results and generated files
5. Check Files: Click "🔄 Check Generated Files" to refresh and see new content
6. Download: Download any generated markdown files for offline use

📁 Generated Output

The application creates three main output directories:

• project_analysis_output/: Project analysis reports and insights
• resource_output/: Resource discovery findings and recommendations
• code_output/: System architecture and code generation outputs

🔧 Troubleshooting

Common Issues

1. "No generated files found"

   • Click "🔄 Check Generated Files" button
   • Wait a few minutes for agents to complete processing
   • Check console for any error messages

2. API Key Errors

   • Verify your .env file is in the root directory
   • Ensure AIML_API_KEY is correctly set
   • Check that the API key is valid and has sufficient credits

3. PDF Reading Errors

   • Ensure the PDF is not password-protected
   • Try with a different PDF file
   • Check if the PDF contains extractable text (not just images)

4. Port Already in Use

   • Streamlit will automatically use the next available port
   • Check the terminal output for the actual port number

Getting Help

• Check the terminal/console for detailed error messages
• Verify all dependencies are installed correctly
• Ensure your virtual environment is activated

📤 Uploading to GitHub

1. Initialize Git Repository (if not already done)

# Initialize git repository
git init

# Add all files to git
git add .

# Make initial commit
git commit -m "Initial commit: PDF Analysis with CrewAI Streamlit app"

2. Create GitHub Repository

1. Go to GitHub and sign in
2. Click the "+" icon in the top right corner
3. Select "New repository"
4. Name your repository (e.g., pdf-analysis-crewai)
5. Add a description
6. Choose public or private
7. DO NOT initialize with README, .gitignore, or license (we already have these)
8. Click "Create repository"

3. Connect and Push to GitHub

# Add the remote origin (replace with your repository URL)
git remote add origin https://github.com/yourusername/pdf-analysis-crewai.git

# Push to GitHub
git push -u origin main

# If your default branch is 'master' instead of 'main':
git push -u origin master

4. Verify Upload

• Go to your GitHub repository
• Verify all files are uploaded correctly
• Check that the README.md is properly displayed

🔄 Updating the Repository

When you make changes to your code:

# Add all changes
git add .

# Commit changes with a descriptive message
git commit -m "Update: Improved file generation and error handling"

# Push to GitHub
git push origin main

📚 Project Structure

pdf-analysis-crewai/
├── streamlit_app.py          # Main Streamlit application
├── requirements.txt          # Python dependencies
├── README.md                # This file
├── .env                     # Environment variables (create this)
├── .gitignore              # Git ignore file
├── project_analysis_output/ # Generated project analysis files
├── resource_output/         # Generated resource discovery files
└── code_output/            # Generated architecture and code files

🌟 Customization

Adding New Agents

To add new AI agents:

1. Create a new agent in the create_agents() function
2. Define corresponding tasks in create_tasks()
3. Update the crew configuration
4. Add new output folders if needed

Modifying Output Formats

• Edit the task descriptions to change output requirements
• Modify the file reading logic in safe_read_file()
• Update the UI display logic in the main function

Styling Changes

• Modify the CSS in the st.markdown() sections
• Update page configuration in st.set_page_config()
• Customize the sidebar and main content layout

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• CrewAI: For the powerful multi-agent framework
• Streamlit: For the excellent web application framework
• OpenAI: For the GPT-5 language model
• Community: For contributions and feedback

📞 Support

If you encounter any issues or have questions:

1. Check the troubleshooting section above
2. Search existing GitHub issues
3. Create a new issue with detailed information
4. Include error messages and steps to reproduce

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Happy PDF Analyzing! 🚀📚🤖