---
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:
  REDIS_HOST=redis-10296.c245.us-east-1-3.ec2.redns.redis-cloud.com
  REDIS_PORT=10296
  REDIS_USERNAME=default
  REDIS_PASSWORD=your_password_here
  REDIS_DISABLE_SSL=false

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.
- Run python verify_redis.py to verify Redis connectivity with exact configuration.

## Confirmed Working Configuration

The Redis connection has been tested and confirmed working with this exact configuration:

"""Basic connection example."""
import redis
r = redis.Redis(
  host='redis-10296.c245.us-east-1-3.ec2.redns.redis-cloud.com',
  port=10296,
  decode_responses=True,
  username="default",
  password="p0ZiQGG9V4cS9NcNpeiBzaOz3YmtXcYW",
)
success = r.set('foo', 'bar')  # True
result = r.get('foo')
print(result)  # >>> bar

This exact configuration is now implemented in the application.