AI Image Chatbot with Gemini API

📂Project Structure

aadi/  # Project root
│
├── app.py                # Main Flask application
├── requirements.txt      # Python dependencies
├── .env                  # Environment configuration
├── README.md             # This documentation
│
├── static/               # All static assets
│   │   └── uploads/          # Image uploads directory
│   └───────style.css     # Main stylesheet
│       ├── demo-animation.gif
│       ├── flask-logo.png
│       ├── gemini-logo.png
│       ├── landing-hero.png
│       ├── sqlite-logo.png
│       ├── user1.jpg
│       ├── user2.jpg
│       └── user3.jpg
│
├── templates/            # HTML templates
│   ├── base.html         # Base template
│   ├── index.html        # Main chat interface
│   ├── landing.html      # Landing page
│   ├── login.html        # Login page
│   ├── profile.html      # User profile
│   └── signup.html       # Registration page
│
└── instance/             # Auto-generated
    └── database.db       # SQLite database file

AI-Powered Image Chatbot

A conversational Flask web application that uses Google's Gemini API to analyze images through natural language interactions.

✨ Features

• Image Analysis: Upload images and get AI-generated descriptions
• Conversational Interface: Ask follow-up questions about your images
• User System: Login, registration, and profile management
• Responsive Design: Works on desktop and mobile devices

🛠️ Tech Stack

• Backend: Python Flask
• AI Engine: Gemini 1.5 Flash API
• Database: SQLite with SQLAlchemy
• Frontend: HTML5, CSS3, Vanilla JavaScript

🚀 Quick Start

1. Clone the repository

   git clone https://github.com/Supriyadasari04/AI-Powered-Image-Chatbot.git
   cd AI-Powered-Image-Chatbot

2. Set up environment

   python -m venv venv
   # Windows:
   venv\Scripts\activate
   # Mac/Linux:
   source venv/bin/activate

3. Install dependencies

   pip install -r requirements.txt

4. Configure environment

   echo "GEMINI_API_KEY=your_api_key_here" > .env
   echo "SECRET_KEY=your_secret_key_here" >> .env

5. Run the application

   python app.py

6. Ctrl + C Notice - a new instance folder to store database is created

7. Re-run the application

   python app.py

Visit http://localhost:5000 in your browser.

📚 Documentation

File Structure

• app.py: Main application logic
• static/: All CSS, JS, and uploaded images
• templates/: HTML pages with Jinja2 templating
• instance/: Database storage

API Endpoints

• POST /upload: Handle image uploads
• POST /chat: Process chat messages
• GET /history: Retrieve conversation history

🌈 Future Roadmap

• Image editing capabilities
• Multi-language support
• Advanced user analytics
• Docker deployment

🤝 Contributing

Pull requests are welcome! For major changes, please open an issue first.

📜 License

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