Streamlit Gemini Apps

A comprehensive collection of Google Gemini AI-powered applications built with Streamlit, featuring multiple interaction modes including text chat, Q&A, and image analysis capabilities.

🚀 Features

• Text Chat: Interactive conversational AI with chat history
• Q&A Mode: Simple question and answer interface
• Image Analysis: Upload and analyze images with AI
• Streaming Responses: Real-time response streaming for better user experience
• Session Management: Persistent chat history across interactions
• Modern UI: Clean and responsive Streamlit interface

📁 Project Structure

streamlit-gemini-apps/
├── app.py          # Basic Q&A application
├── chat.py        # Streaming chat with history
├── gpt.py          # Enhanced chat interface with styling
├── qachat.py       # Q&A with chat history
├── vision.py       # Image analysis application
├── requirements.txt # Project dependencies
├── env.example     # Environment variables template
├── Dockerfile      # Docker configuration
├── docker-compose.yml # Docker Compose setup
└── README.md       # Project documentation

🛠️ Installation

1. Clone the repository

   git clone https://github.com/yourusername/streamlit-gemini-apps.git
   cd streamlit-gemini-apps

2. Install dependencies

   pip install -r requirements.txt

3. Set up environment variables

   • Copy the example environment file:

   cp env.example .env

   • Edit the .env file and add your Google API key:

   GOOGLE_API_KEY=your_api_key_here
   

4. Get Google API Key

   • Visit Google AI Studio
   • Create a new API key
   • Copy the key to your .env file

🚀 Usage

Basic Q&A Application

streamlit run app.py

Chat with History

streamlit run chat.py

Enhanced Chat Interface

streamlit run gpt.py

Q&A with Chat History

streamlit run qachat.py

Image Analysis

streamlit run vision.py

🐳 Docker Support

Using Docker Compose (Recommended)

1. Set up environment variables

   cp env.example .env
   # Edit .env file with your Google API key

2. Run with Docker Compose

   # Run the enhanced chat interface (default)
   docker-compose up
   
   # Run a specific application
   docker-compose -f docker-compose.yml run --rm app streamlit run app.py
   docker-compose -f docker-compose.yml run --rm chat streamlit run chat.py
   docker-compose -f docker-compose.yml run --rm gpt streamlit run gpt.py
   docker-compose -f docker-compose.yml run --rm qachat streamlit run qachat.py
   docker-compose -f docker-compose.yml run --rm vision streamlit run vision.py

3. Access the application

   • Open your browser and go to http://localhost:8501

Using Docker directly

1. Build the Docker image

   docker build -t streamlit-gemini-apps .

2. Run the container

   # Run with environment variables
   docker run -p 8501:8501 --env-file .env streamlit-gemini-apps
   
   # Run a specific application
   docker run -p 8501:8501 --env-file .env streamlit-gemini-apps streamlit run gpt.py

Docker Commands

# Build the image
docker build -t streamlit-gemini-apps .

# Run the default application (gpt.py)
docker run -p 8501:8501 --env-file .env streamlit-gemini-apps

# Run specific applications
docker run -p 8501:8501 --env-file .env streamlit-gemini-apps streamlit run app.py
docker run -p 8501:8501 --env-file .env streamlit-gemini-apps streamlit run chat.py
docker run -p 8501:8501 --env-file .env streamlit-gemini-apps streamlit run qachat.py
docker run -p 8501:8501 --env-file .env streamlit-gemini-apps streamlit run vision.py

# Run in background
docker run -d -p 8501:8501 --env-file .env --name gemini-app streamlit-gemini-apps

# Stop the container
docker stop gemini-app

# Remove the container
docker rm gemini-app

Development with Docker

For development with hot reload:

# Run in development mode with file watching
docker-compose -f docker-compose.yml -f docker-compose.dev.yml up

# Run specific application in development mode
docker-compose -f docker-compose.yml -f docker-compose.dev.yml up app

Docker Compose Services

The docker-compose.yml includes multiple services for different applications:

Service	Port	Application	Description
app	8501	gpt.py	Enhanced chat interface (default)
qa	8502	app.py	Basic Q&A application
chat	8503	chat.py	Streaming chat with history
qachat	8504	qachat.py	Q&A with chat history
vision	8505	vision.py	Image analysis application

Quick Start with Docker

# 1. Clone and setup
git clone https://github.com/yourusername/streamlit-gemini-apps.git
cd streamlit-gemini-apps

# 2. Setup environment
cp env.example .env
# Edit .env with your Google API key

# 3. Run with Docker Compose
docker-compose up

# 4. Access the application
# Open http://localhost:8501 in your browser

📋 Available Applications

File	Description	Features
app.py	Basic Q&A	Simple question-answer interface
chat.py	Streaming Chat	Real-time streaming responses
gpt.py	Enhanced Chat	Styled interface with chat history
qachat.py	Q&A with History	Question-answer with conversation memory
vision.py	Image Analysis	Upload and analyze images with AI

🔧 Configuration

Environment Variables

• GOOGLE_API_KEY: Your Google Gemini API key (required)

Model Configuration

• Default model: gemini-pro
• Vision model: gemini-pro-vision
• Streaming: Enabled for real-time responses

📦 Dependencies

• streamlit - Web application framework
• google-generativeai - Google Gemini AI SDK
• python-dotenv - Environment variable management
• PIL (Pillow) - Image processing for vision features

🎯 Use Cases

• Customer Support: Automated Q&A systems
• Content Analysis: Image and text analysis
• Educational Tools: Interactive learning assistants
• Research: AI-powered research assistance
• Prototyping: Rapid AI application development

🔒 Security Notes

• Never commit your .env file to version control
• Keep your API keys secure and private
• Consider using environment variables in production

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some 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

• Google for the Gemini AI API
• Streamlit team for the amazing framework
• Open source community for inspiration

📞 Support

If you encounter any issues or have questions:

1. Check the Issues page
2. Create a new issue with detailed description
3. Contact the maintainers

---

Made with ❤️ using Streamlit and Google Gemini AI