A complete chatbot application built with Flutter frontend and Python FastAPI backend, powered by Ollama for AI conversations.

ollama-chatbot-fullstack/
├── README.md                 # This file
├── .gitignore               # Git ignore rules
├── frontend/                # Flutter mobile/web app
│   ├── lib/
│   ├── pubspec.yaml
│   └── ...
└── backend/                 # Python FastAPI server
    ├── api_server.py
    ├── requirements.txt
    └── ...

• Flutter SDK (latest stable)
• Python 3.8+
• Ollama installed and running
• Git

git clone <your-repo-url>
cd ollama-chatbot-fullstack

cd backend

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Start the server
python api_server.py

The backend will be available at http://localhost:8000

cd frontend

# Install Flutter dependencies
flutter pub get

# Update API URL in lib/services/api_service.dart
# Change 'YOUR_BACKEND_URL_HERE' to 'http://localhost:8000'

# Run the app
flutter run -d chrome  # For web
# or
flutter run            # For mobile (with emulator/device connected)

1. Ollama Model: Update the model in backend/api_server.py:

   runnable = build_chain(os.getenv("OLLAMA_MODEL", "llama3.1:8b"))

2. Environment Variables: Create .env file in backend/ if needed:

   OLLAMA_MODEL=llama3.1:8b
   

1. API URL: Update lib/services/api_service.dart:

   static const String baseUrl = 'http://localhost:8000';

2. Build for different platforms:

   flutter build web       # Web
   flutter build apk       # Android
   flutter build ios       # iOS

• User Authentication (registration/login)
• Real-time Chat with Ollama AI models
• Cross-platform (Web, iOS, Android)
• Clean Architecture with proper separation of concerns
• Error Handling and connection status indicators
• Modern UI with Material Design

• FastAPI with automatic docs at http://localhost:8000/docs
• LangChain integration for AI pipeline
• Simple in-memory authentication (replace with database in production)
• CORS enabled for frontend development

• Flutter with Material Design 3
• Service-based architecture
• HTTP client for API communication
• Responsive design for multiple screen sizes

• POST /auth/register - User registration
• POST /auth/login - User login
• POST /chat - Send message to AI
• GET /docs - API documentation

• Replace in-memory user storage with proper database
• Implement proper JWT token validation
• Use HTTPS in production
• Validate and sanitize all inputs

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Test thoroughly
5. Submit a pull request

[Add your license here]

• Ollama for local AI model hosting
• LangChain for AI pipeline framework
• Flutter for cross-platform UI
• FastAPI for backend framework