deploy_memo.py

#!/usr/bin/env python3
"""
Memo Model Deployment Script
Deploy Memo to various platforms and environments
"""

import os
import sys
import subprocess
import json
from pathlib import Path

def check_requirements():
    """Check if all requirements are met for deployment"""
    print("🔍 Checking deployment requirements...")
    
    # Check if we're in the right directory
    required_files = [
        'api/main.py',
        'requirements.txt',
        'README.md',
        'config/model_tiers.py'
    ]
    
    missing_files = []
    for file in required_files:
        if not os.path.exists(file):
            missing_files.append(file)
    
    if missing_files:
        print(f"❌ Missing required files: {missing_files}")
        return False
    
    print("✅ All required files present")
    return True

def deploy_local():
    """Deploy Memo locally for testing"""
    print("🚀 Deploying Memo locally...")
    
    try:
        # Install dependencies
        print("📦 Installing dependencies...")
        subprocess.run([sys.executable, "-m", "pip", "install", "-r", "requirements.txt"], check=True)
        
        # Start the API server
        print("🌐 Starting API server on http://localhost:8000")
        subprocess.run([sys.executable, "api/main.py"])
        
    except subprocess.CalledProcessError as e:
        print(f"❌ Local deployment failed: {e}")
        return False
    
    return True

def create_dockerfile():
    """Create a Dockerfile for containerized deployment"""
    print("📦 Creating Dockerfile...")
    
    dockerfile_content = '''FROM python:3.11-slim

WORKDIR /app

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

# Copy requirements first for better caching
COPY requirements.txt .

# Install Python dependencies
RUN pip install --no-cache-dir -r requirements.txt

# Copy application code
COPY . .

# Expose the API port
EXPOSE 8000

# Health check
HEALTHCHECK --interval=30s --timeout=30s --start-period=5s --retries=3 \\
    CMD curl -f http://localhost:8000/health || exit 1

# Start the API server
CMD ["python", "api/main.py"]
'''
    
    with open('Dockerfile', 'w') as f:
        f.write(dockerfile_content)
    
    print("✅ Dockerfile created successfully")
    return True

def create_docker_compose():
    """Create docker-compose.yml for multi-service deployment"""
    print("🔧 Creating docker-compose.yml...")
    
    compose_content = '''version: '3.8'

services:
  memo-api:
    build: .
    ports:
      - "8000:8000"
    environment:
      - ENVIRONMENT=production
      - LOG_LEVEL=INFO
    volumes:
      - ./logs:/app/logs
    restart: unless-stopped
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:8000/health"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 40s
    
  redis:
    image: redis:alpine
    ports:
      - "6379:6379"
    volumes:
      - redis_data:/data
    restart: unless-stopped

volumes:
  redis_data:
'''
    
    with open('docker-compose.yml', 'w') as f:
        f.write(compose_content)
    
    print("✅ docker-compose.yml created successfully")
    return True

def deploy_huggingface_inference():
    """Instructions for Hugging Face Inference API deployment"""
    print("🌐 Hugging Face Inference API Deployment")
    print("=" * 50)
    
    instructions = """
To deploy your Memo model on Hugging Face Inference API:

1. Go to: https://huggingface.co/likhonsheikh/memo
2. Click "Ask for provider support" (as shown in your screenshot)
3. Fill out the deployment request form with:
   - Model description: "Production-grade text-to-video generation with Transformers + Safetensors"
   - Use case: "Bangla text to video content generation"
   - Expected usage: "API endpoints for video generation"
   - Performance requirements: "4-16GB memory, GPU preferred"

4. Hugging Face will review and deploy your model if approved

Benefits:
✅ Automatic scaling
✅ Global CDN
✅ Pay-per-use pricing
✅ No infrastructure management
✅ Professional SLA
"""
    
    print(instructions)
    return True

def create_cloud_deployment_scripts():
    """Create deployment scripts for various cloud platforms"""
    print("☁️ Creating cloud deployment scripts...")
    
    # AWS deployment script
    aws_script = '''#!/bin/bash
# AWS Deployment Script for Memo

echo "🚀 Deploying Memo to AWS..."

# Build and push Docker image
aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin $ACCOUNT_ID.dkr.ecr.us-east-1.amazonaws.com

docker build -t memo-api .
docker tag memo-api:latest $ACCOUNT_ID.dkr.ecr.us-east-1.amazonaws.com/memo-api:latest
docker push $ACCOUNT_ID.dkr.ecr.us-east-1.amazonaws.com/memo-api:latest

# Deploy to ECS
aws ecs create-service \\
  --cluster memo-cluster \\
  --service-name memo-api \\
  --task-definition memo-task \\
  --desired-count 1 \\
  --launch-type FARGATE \\
  --network-configuration "awsvpcConfiguration={subnets=[subnet-12345],securityGroups=[sg-12345],assignPublicIp=ENABLED}"
'''
    
    with open('deploy-aws.sh', 'w') as f:
        f.write(aws_script)
    os.chmod('deploy-aws.sh', 0o755)
    
    # Google Cloud deployment script
    gcp_script = '''#!/bin/bash
# Google Cloud Deployment Script for Memo

echo "🚀 Deploying Memo to Google Cloud..."

# Build and push Docker image
gcloud builds submit --tag gcr.io/$PROJECT_ID/memo-api

# Deploy to Cloud Run
gcloud run deploy memo-api \\
  --image gcr.io/$PROJECT_ID/memo-api \\
  --platform managed \\
  --region us-central1 \\
  --allow-unauthenticated \\
  --memory 8Gi \\
  --cpu 2 \\
  --max-instances 10 \\
  --min-instances 0
'''
    
    with open('deploy-gcp.sh', 'w') as f:
        f.write(gcp_script)
    os.chmod('deploy-gcp.sh', 0o755)
    
    print("✅ Cloud deployment scripts created")
    return True

def main():
    """Main deployment function"""
    print("🚀 Memo Model Deployment Script")
    print("=" * 50)
    
    if not check_requirements():
        print("❌ Requirements check failed. Please ensure you're in the Memo directory.")
        sys.exit(1)
    
    print("\nChoose deployment option:")
    print("1. Local deployment (for testing)")
    print("2. Create Dockerfile")
    print("3. Create docker-compose.yml")
    print("4. Hugging Face Inference API instructions")
    print("5. Create cloud deployment scripts")
    print("6. All of the above")
    
    choice = input("\nEnter your choice (1-6): ").strip()
    
    if choice == "1":
        deploy_local()
    elif choice == "2":
        create_dockerfile()
    elif choice == "3":
        create_docker_compose()
    elif choice == "4":
        deploy_huggingface_inference()
    elif choice == "5":
        create_cloud_deployment_scripts()
    elif choice == "6":
        create_dockerfile()
        create_docker_compose()
        deploy_huggingface_inference()
        create_cloud_deployment_scripts()
        print("\n✅ All deployment options prepared!")
        print("\nNext steps:")
        print("1. For local testing: python api/main.py")
        print("2. For Docker: docker-compose up")
        print("3. For cloud deployment: Use the created scripts")
        print("4. For Hugging Face: Follow the instructions above")
    else:
        print("❌ Invalid choice")
        sys.exit(1)

if __name__ == "__main__":
    main()