⚡ Streamlit x LangChain Chatbot

A modern, responsive chatbot built with Streamlit and LangChain, powered by Google's Gemini AI models. Features real-time streaming responses, multiple model selection, and a clean, professional interface.

🌟 Features

• 🤖 Multiple AI Models: Support for 40+ Google Gemini models (Gemini 1.5, 2.0, 2.5, and experimental)
• 💬 Streaming Responses: Real-time token streaming for smooth interaction
• 🔐 API Key Management: Secure per-session key entry via sidebar
• 🎨 Clean UI: Modern chat interface with avatars + dark/light theme toggle
• 📊 Chat Stats: Track messages in current session
• ⚡ Optimized: Smart caching with st.cache_resource and st.cache_data
• 📱 Mobile Friendly: Works well on different devices

🚀 Quick Start

Prerequisites

• Python 3.8 or higher
• Google Gemini API key (Get one here)

Installation

1. Clone the repository

git clone https://github.com/yourusername/streamlit-langchain-chatbot.git
cd streamlit-langchain-chatbot

2. Create virtual environment & install dependencies

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

3. Run the application

streamlit run app.py

4. Open in browser
   • The app will automatically open in your default browser
   • Or navigate to http://localhost:8501

📦 Dependencies

Create a requirements.txt file with these dependencies:

streamlit>=1.28.0
langchain-core>=0.1.0
langchain-google-genai>=1.0.0
python-dotenv>=1.0.0

🔧 Configuration

API Key Setup

You can provide your Google Gemini API key in two ways:

1. Through the UI (Recommended for security):

   • Enter your API key in the sidebar when you run the app
   • The key is stored only in your browser session

2. Environment Variable:

   • Create a .env file in the project root
   • Add: GOOGLE_API_KEY=your_api_key_here

Model Selection

Choose from 40+ available models including:

• Gemini 2.0 Flash - Latest and fastest
• Gemini 2.5 Pro - Most capable for complex tasks
• Gemini 1.5 Flash - Good balance of speed and quality
• Experimental models - Cutting-edge features

🎯 Usage

1. Start the app and enter your Google Gemini API key
2. Select a model from the dropdown in the sidebar
3. Start chatting - type your message and press Enter
4. Watch responses stream in real-time
5. Use chat controls:
   • Clear chat history
   • Switch between models mid-conversation

🏗️ Project Structure

streamlit-langchain-chatbot/
│
├── app.py                  # Main application file
├── requirements.txt        # Python dependencies
├── pyproject.toml          # Project metadata (uv/PEP 621)
├── uv.lock                 # uv lockfile (auto-generated)
├── .gitignore              # Git ignore file
├── .python-version         # Python version pin (optional)
├── LICENSE                 # MIT License
├── README.md               # Documentation
├── .env                    # Local env vars (optional, not committed)
└── .venv/                  # Local virtual env (ignored)

🚀 Deployment

Streamlit Cloud (Recommended)

1. Push your code to GitHub
2. Go to share.streamlit.io
3. Connect your GitHub repository
4. Set your GOOGLE_API_KEY in the secrets management
5. Deploy!

Other Deployment Options

• Heroku: Use the Heroku CLI with a Procfile
• Railway: Connect your GitHub repo for automatic deployment
• Replit: Import from GitHub and run directly
• Local Network: Use streamlit run app.py --server.address 0.0.0.0

🔒 Security Notes

• API keys are handled securely per session
• No conversation data is stored permanently
• Environment variables are used for sensitive configuration
• Input validation prevents malformed requests

🛠️ Development

Local Development Setup

1. Clone and setup:

git clone <your-repo>
cd streamlit-langchain-chatbot
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt

2. Run in development mode:

streamlit run app.py --server.runOnSave true

Adding New Features

The code is modular and easy to extend:

• configure_page() - UI and page setup
• handle_sidebar() - Sidebar controls and settings
• display_chat_messages() - Message rendering
• handle_user_input() - Input processing and AI interaction

🤝 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

• Streamlit for the amazing web app framework
• LangChain for AI integration tools
• Google AI for the Gemini API
• The open-source community for inspiration and support

📞 Support

• 🐛 Bug Reports: Open an issue
• 💡 Feature Requests: Start a discussion
• ❓ Questions: Check existing issues or create a new one

🔗 Links

• Live Demo
• Google Gemini API
• Streamlit Documentation
• LangChain Documentation

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Made with ❤️ by Zohaib Khan