🚀 AgentSynapz - Vision Language Model

Agentic Image Personality is an AI-powered FastAPI application that analyzes uploaded images and generates personalized, culturally-aware responses. The project leverages Google Gemini AI models for vision analysis and creates engaging interactions based on dynamic AI personality profiles.

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✨ Features

• 🔍 Advanced Image Analysis: Uses Google Gemini vision models to describe and interpret uploaded images with cultural awareness
• 🎨 Dynamic AI Personalities: Multiple customizable AI agents with unique voices and cultural perspectives
• 🌍 Cultural Intelligence: AI recognizes and comments on cultural elements, adapting tone and language accordingly
• 📡 Production-Ready API: FastAPI backend with comprehensive endpoints and automatic documentation
• 📓 Interactive Development: Jupyter Notebook for experimentation and testing
• ☁️ Cloud Deployment: Automated Google Cloud Run deployment via GitHub Actions
• 🔒 Secure Configuration: Environment-based API key management

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🎯 How It Works

1. Upload an image (e.g., selfie or portrait)
2. AI analyzes the image using Google Gemini vision models
3. Cultural context recognition identifies elements and background
4. Personalized response generation creates engaging, witty responses
5. Culturally-aware delivery adapts tone and language appropriately

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🚀 Quick Start

Prerequisites

• Python 3.9+
• Google Generative AI API key (Gemini)
• (Optional) Jupyter Notebook for interactive use

1. Clone & Setup

git clone https://github.com/yourusername/Agentic-ImagePersonality-3.git
cd Agentic-ImagePersonality-3
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt

2. Configure Environment

Create a .env file:

GOOGLE_API_KEY=your_gemini_api_key_here

3. Run the API

python main.py

Visit http://localhost:8000/docs for the interactive API documentation.

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📋 API Endpoints

Core Endpoints

Method	Endpoint	Description
GET	/	API information and available personalities
GET	/bots	List all available AI agents
POST	/analyze_image_with_file	Upload image file for analysis
POST	/analyze_image_with_base64	Send base64 image for analysis
GET	/health	Health check and system status

Example Usage

Upload Image File

curl -X POST "http://localhost:8000/analyze_image_with_file" \
     -H "Content-Type: multipart/form-data" \
     -F "image=@your_image.jpg" \
     -F "bot_id=mentor_male"

Send Base64 Image

curl -X POST "http://localhost:8000/analyze_image_with_base64" \
     -H "Content-Type: application/json" \
     -d '{
       "image_base64": "iVBORw0KGgoAAAANSUhEUgAA...",
       "bot_id": "cultural_advisor"
     }'

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🏗️ Project Structure

Agentic-ImagePersonality-3/
├── main.py                 # FastAPI application
├── bot_persona.py          # AI personality configurations
├── agenticImagePersonality1.ipynb  # Jupyter notebook for testing
├── requirements.txt        # Python dependencies
├── .env                    # Environment variables (create this)
├── .gitignore             # Git ignore rules
├── .github/
│   └── workflows/
│       └── deploy.yml     # GitHub Actions deployment
└── README.md              # This file

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🔧 Development

Interactive Development

Use the Jupyter notebook for experimentation:

jupyter notebook agenticImagePersonality1.ipynb

Adding New AI Personalities

1. Edit bot_persona.py
2. Add your personality configuration
3. Test with the API endpoints

Local Testing

# Start the server
python main.py

# Test health endpoint
curl http://localhost:8000/health

# Get available personalities
curl http://localhost:8000/bots

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☁️ Deployment

Google Cloud Run (Automated)

The project includes GitHub Actions for automatic deployment:

1. Set up secrets in your GitHub repository:

   • GCP_CREDENTIALS: Your Google Cloud service account JSON
   • GOOGLE_API_KEY: Your Gemini API key

2. Push to main branch:

   git push origin main

3. Monitor deployment: Check the Actions tab in your GitHub repository

Manual Deployment

# Deploy to Cloud Run
gcloud run deploy fastapi-image-personality \
  --source . \
  --region us-central1 \
  --set-env-vars GOOGLE_API_KEY=$GOOGLE_API_KEY

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📦 Dependencies

• FastAPI - Modern web framework for APIs
• Google Generative AI - Gemini vision and text models
• Pillow - Image processing
• Uvicorn - ASGI server
• Pydantic - Data validation

See requirements.txt for complete list.

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🔒 Security & Privacy

• API keys are managed through environment variables
• Images are temporarily processed and can be stored as base64
• No persistent storage of user images by default
• CORS middleware configured for frontend integration

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🤝 Contributing

1. Fork the repository
2. Create a feature branch: git checkout -b feature-name
3. Make your changes and add tests
4. Commit your changes: git commit -m 'Add feature'
5. Push to the branch: git push origin feature-name
6. Submit a pull request

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🆘 Support

• Issues: Report bugs or request features via GitHub Issues
• Documentation: Visit /docs endpoint when running locally
• API Reference: Interactive docs at /docs and /redoc

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