docs/DEPLOYMENT.md

Deployment Guide

This guide provides detailed instructions for deploying LifeUnity AI — Cognitive Twin System to various cloud platforms.

Table of Contents

• Streamlit Cloud Deployment
• Render Deployment
• HuggingFace Spaces Deployment
• Docker Deployment

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Streamlit Cloud Deployment

Streamlit Cloud is the easiest way to deploy this application.

Prerequisites

• GitHub account
• Streamlit Cloud account (free at https://share.streamlit.io)

Steps

1. Fork or push this repository to your GitHub account

2. Go to Streamlit Cloud

3. Click "New app"

4. Configure the app:

   • Repository: your-username/lifeunity-ai-cognitive-twin
   • Branch: main
   • Main file path: app/main.py

5. Advanced settings (optional):

   • Python version: 3.9 or higher
   • Environment variables: None required

6. Click "Deploy"

7. Wait for deployment (may take 5-10 minutes for first deployment)

8. Access your app at https://[your-app-name].streamlit.app

Troubleshooting

• If deployment fails, check the logs in Streamlit Cloud
• Ensure all dependencies in requirements.txt are compatible
• Large models (like transformers) may take time to download on first run

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Render Deployment

Render provides free web service hosting with automatic deployments.

Prerequisites

• GitHub account
• Render account (free at https://render.com)

Steps

1. Push this repository to GitHub

2. Go to Render Dashboard

3. Click "New +" and select "Web Service"

4. Connect your GitHub repository

5. Configure the service:

   • Name: lifeunity-ai-cognitive-twin
   • Environment: Python 3
   • Build Command: pip install -r requirements.txt
   • Start Command: streamlit run app/main.py --server.port=$PORT --server.address=0.0.0.0

6. Select the free plan

7. Advanced settings:

   • Auto-Deploy: Yes (recommended)
   • Environment variables: None required

8. Click "Create Web Service"

9. Wait for deployment (first deployment may take 10-15 minutes)

10. Access your app at the provided Render URL

Using render.yaml (Alternative)

The repository includes a render.yaml file. You can:

1. Go to Render Dashboard
2. Click "New +" → "Blueprint"
3. Connect your repository
4. Render will automatically use render.yaml configuration

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HuggingFace Spaces Deployment

HuggingFace Spaces provides free hosting with GPU support options.

Prerequisites

• HuggingFace account (free at https://huggingface.co)

Steps

1. Go to HuggingFace Spaces

2. Click "Create new Space"

3. Configure the Space:

   • Owner: Your username
   • Space name: lifeunity-ai-cognitive-twin
   • License: MIT
   • Select SDK: Streamlit
   • Space hardware: CPU basic (free) or GPU (paid)
   • Visibility: Public or Private

4. Click "Create Space"

5. Clone the Space repository:

   git clone https://huggingface.co/spaces/[your-username]/lifeunity-ai-cognitive-twin
   

6. Copy files to the Space:

   cp -r app requirements.txt HF_README.md [space-directory]/
   

7. Rename HF_README.md to README.md:

   cd [space-directory]
   mv HF_README.md README.md
   

8. Push to HuggingFace:

   git add .
   git commit -m "Initial deployment"
   git push
   

9. Wait for deployment (automatic)

10. Access your Space at https://huggingface.co/spaces/[your-username]/lifeunity-ai-cognitive-twin

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Docker Deployment

For self-hosting or custom cloud deployments.

Create Dockerfile

FROM python:3.9-slim

WORKDIR /app

# Install system dependencies
RUN apt-get update && apt-get install -y \
    build-essential \
    curl \
    software-properties-common \
    git \
    && rm -rf /var/lib/apt/lists/*

# Copy requirements and install
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Copy application
COPY app/ ./app/
COPY README.md .

# Expose Streamlit port
EXPOSE 8501

# Health check
HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health

# Run application
ENTRYPOINT ["streamlit", "run", "app/main.py", "--server.port=8501", "--server.address=0.0.0.0"]

Build and Run

# Build image
docker build -t lifeunity-ai .

# Run container
docker run -p 8501:8501 lifeunity-ai

Docker Compose (Optional)

version: '3.8'

services:
  app:
    build: .
    ports:
      - "8501:8501"
    volumes:
      - ./data:/app/data
      - ./logs:/app/logs
    restart: unless-stopped

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Post-Deployment

Testing Your Deployment

1. Open the application URL
2. Navigate through all pages:
   • Dashboard
   • Mood Detection
   • Cognitive Memory
   • AI Insights
3. Test core features:
   • Upload an image for mood detection
   • Add a memory note
   • Generate a daily report
4. Check for errors in logs

Monitoring

• Streamlit Cloud: Use built-in logs and metrics
• Render: Check logs in the Render dashboard
• HuggingFace: Use the Spaces logs tab
• Docker: Use docker logs [container-id]

Updating Your Deployment

Streamlit Cloud & Render:

• Push changes to your GitHub repository
• Deployment updates automatically

HuggingFace Spaces:

• Push changes to the Space repository
• Rebuild happens automatically

Docker:

• Rebuild image: docker build -t lifeunity-ai .
• Restart container

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Troubleshooting

Common Issues

Out of Memory:

• Use smaller models in requirements.txt
• Reduce batch sizes
• Upgrade to paid tier with more RAM

Slow First Load:

• Models download on first run (expected)
• Consider caching models in Docker image

Port Issues:

• Ensure using correct port for platform
• Streamlit Cloud: Auto-configured
• Render: Use $PORT environment variable
• HuggingFace: Port 7860 (default for Streamlit)

Dependency Conflicts:

• Check Python version (3.9+ recommended)
• Update requirements.txt versions
• Use virtual environment for testing

Getting Help

• Check platform-specific documentation
• Open an issue on GitHub
• Contact platform support
• Review application logs

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Security Best Practices

1. Environment Variables:

   • Store secrets in platform-specific secrets management
   • Never commit sensitive data

2. Data Privacy:

   • User data stays in deployment
   • Consider adding authentication for production

3. Updates:

   • Keep dependencies updated
   • Monitor security advisories

4. Backups:

   • Regularly backup data directories
   • Use version control

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For more information, see the main README.md