README.md

---
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)

Note: Redis configuration is now hardcoded for reliability.

## 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:

The Redis configuration is now hardcoded for maximum reliability
If issues persist, check network connectivity to Redis Cloud
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 test_hardcoded_redis.py to verify Redis connectivity with hardcoded configuration.
New Redis Database Configuration
The Redis connection now uses the new database credentials:


import redis
r = redis.Redis(
  host='redis-16717.c85.us-east-1-2.ec2.redns.redis-cloud.com',
  port=16717,
  username="default",
  password="bNQGmfkB2fRo4KrT3UXwhAUEUmgDClx7",
  decode_responses=True,
  socket_connect_timeout=15,
  socket_timeout=15,
  ssl=True,
  health_check_interval=30,
  retry_on_timeout=True
)
This configuration has been tested and confirmed working.