---
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
- Automatic fallback between providers

## 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)

## Provider Details

### Ollama (Primary Local Provider)

**Setup:**
1. Install Ollama: https://ollama.com/download
2. Pull a model: `ollama pull mistral`
3. Start server: `ollama serve`
4. Configure ngrok: `ngrok http 11434`
5. Set `OLLAMA_HOST` to your ngrok URL

**Advantages:**
- No cost for inference
- Full control over models
- Fast response times
- Privacy - all processing local

### Hugging Face Inference API (Fallback)

**Current Endpoint:** `https://zxzbfrlg3ssrk7d9.us-east-1.aws.endpoints.huggingface.cloud`

**Important Scaling Behavior:**
- ⚠️ **Scale-to-Zero**: Endpoint automatically scales to zero after 15 minutes of inactivity
- ⏱️ **Cold Start**: Takes approximately 4 minutes to initialize when first requested
- 🔄 **Automatic Wake-up**: Sending any request will automatically start the endpoint
- 💰 **Cost**: \$0.536/hour while running (not billed when scaled to zero)
- 📍 **Location**: AWS us-east-1 (Intel Sapphire Rapids, 16vCPUs, 32GB RAM)

**Handling 503 Errors:**
When using the Hugging Face fallback, you may encounter 503 errors initially. This indicates the endpoint is initializing. Simply retry your request after 30-60 seconds, or wait for the initialization to complete (typically 4 minutes).

**Model:** OpenAI GPT OSS 20B (Uncensored variant)

### OpenAI (Alternative Fallback)

Configure with `OPENAI_API_KEY` environment variable.

## Switching Between Providers

### For Local Development (Windows/Ollama):

1. **Install Ollama:**
   ```bash
   # Download from https://ollama.com/download/OllamaSetup.exe
Pull and run models:

ollama pull mistral
ollama pull llama3
ollama serve
Start ngrok tunnel:

ngrok http 11434
Update environment variables:

OLLAMA_HOST=https://your-ngrok-url.ngrok-free.app
LOCAL_MODEL_NAME=mistral
USE_FALLBACK=false
For Production Deployment:
The application automatically handles provider fallback:

Primary: Ollama (via ngrok)
Secondary: Hugging Face Inference API
Tertiary: OpenAI (if configured)
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.

Troubleshooting Common Issues:
503 Errors with Hugging Face Fallback:
Wait 4 minutes for cold start initialization
Retry request after endpoint warms up
Ollama Connection Issues:
Verify ollama serve is running locally
Check ngrok tunnel status
Confirm ngrok URL matches OLLAMA_HOST
Test with test_ollama_connection.py
Redis Connection Problems:
Set USE_FALLBACK=true to disable Redis requirement
Or configure proper Redis credentials
Model Not Found:
Pull required model: ollama pull <model-name>
Check available models: ollama list
Diagnostic Scripts:
Run python test_ollama_connection.py to verify Ollama connectivity.
Run python diagnose_ollama.py for detailed connection diagnostics.