README.md

metadata

title: AI Life Coach
emoji: 🧘
colorFrom: purple
colorTo: blue
sdk: streamlit
sdk_version: 1.24.0
app_file: app.py
pinned: false

AI Life Coach 🧘

Your personal AI-powered life coaching assistant.

Features

• Personalized life coaching conversations
• Redis-based conversation memory
• Multiple LLM provider support (Ollama, Hugging Face, OpenAI)
• Dynamic model selection
• Remote Ollama integration via ngrok

How to Use

1. Select a user from the sidebar
2. Configure your Ollama connection (if using remote Ollama)
3. Choose your preferred model
4. Start chatting with your AI Life Coach!

Requirements

All requirements are specified in requirements.txt. The app automatically handles:

• Streamlit UI
• FastAPI backend (for future expansion)
• Redis connection for persistent memory
• Multiple LLM integrations

Environment Variables

Configure these in your Hugging Face Space secrets or local .env file:

• OLLAMA_HOST: Your Ollama server URL (default: ngrok URL)
• LOCAL_MODEL_NAME: Default model name (default: mistral)
• HF_TOKEN: Hugging Face API token (for Hugging Face models)
• HF_API_ENDPOINT_URL: Hugging Face inference API endpoint
• USE_FALLBACK: Whether to use fallback providers (true/false)
• REDIS_HOST: Redis server hostname (default: localhost)
• REDIS_PORT: Redis server port (default: 6379)
• REDIS_USERNAME: Redis username (optional)
• REDIS_PASSWORD: Redis password (optional)

Architecture

This application consists of:

• Streamlit frontend (app.py)
• Core LLM abstraction (core/llm.py)
• Memory management (core/memory.py)
• Configuration management (utils/config.py)
• API endpoints (in api/ directory for future expansion)

Built with Python, Streamlit, FastAPI, and Redis.