install_production.py

#!/usr/bin/env python3
"""
Production Installation Script for AI Text Humanizer
Ensures all advanced features are properly installed and working
"""

import subprocess
import sys
import os
import time

def run_command(cmd, description, critical=True):
    """Run a command and handle errors"""
    print(f"🔄 {description}...")
    try:
        result = subprocess.run(cmd, shell=True, check=True, capture_output=True, text=True)
        print(f"✅ {description} - SUCCESS")
        if result.stdout.strip():
            print(f"   Output: {result.stdout.strip()}")
        return True
    except subprocess.CalledProcessError as e:
        print(f"❌ {description} - FAILED")
        print(f"   Error: {e.stderr.strip()}")
        if critical:
            return False
        return True

def check_gpu_availability():
    """Check if CUDA/GPU is available for better performance"""
    try:
        result = subprocess.run(["nvidia-smi"], capture_output=True, text=True)
        if result.returncode == 0:
            print("🚀 NVIDIA GPU detected - will install CUDA support")
            return True
    except FileNotFoundError:
        pass
    
    print("💻 No NVIDIA GPU detected - using CPU versions")
    return False

def production_install():
    """Install production-grade AI Text Humanizer with all features"""
    print("🏭 AI TEXT HUMANIZER - PRODUCTION INSTALLATION")
    print("=" * 55)
    print("📋 This will install ALL advanced features:")
    print("   ✨ Advanced semantic similarity (sentence-transformers)")
    print("   🧠 AI paraphrasing capabilities (transformers)")
    print("   🚀 GPU acceleration (if available)")
    print("   📊 Full API and web interfaces")
    print("")
    
    # Check system
    has_gpu = check_gpu_availability()
    
    print("🔧 Starting production installation...")
    print("-" * 40)
    
    # Step 1: Clean existing installation
    print("\n📦 STEP 1: Cleaning existing installation")
    cleanup_commands = [
        "pip uninstall -y sentence-transformers transformers huggingface_hub torch torchvision torchaudio",
        "pip cache purge"
    ]
    
    for cmd in cleanup_commands:
        run_command(cmd, "Cleaning previous installation", critical=False)
    
    # Step 2: Upgrade pip and install build tools
    print("\n🔨 STEP 2: Installing build tools")
    build_commands = [
        "pip install --upgrade pip setuptools wheel",
        "pip install --upgrade packaging"
    ]
    
    for cmd in build_commands:
        if not run_command(cmd, "Installing build tools"):
            return False
    
    # Step 3: Install PyTorch (choose CPU or GPU version)
    print("\n🧠 STEP 3: Installing PyTorch")
    if has_gpu:
        torch_cmd = "pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121"
    else:
        torch_cmd = "pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu"
    
    if not run_command(torch_cmd, "Installing PyTorch with proper backend"):
        print("⚠️ PyTorch installation failed, trying alternative...")
        if not run_command("pip install torch==2.1.0", "Installing PyTorch (fallback)"):
            return False
    
    # Step 4: Install HuggingFace ecosystem with compatible versions
    print("\n🤗 STEP 4: Installing HuggingFace ecosystem")
    hf_commands = [
        "pip install huggingface_hub==0.17.3",
        "pip install tokenizers==0.14.1", 
        "pip install transformers==4.35.0",
        "pip install accelerate==0.24.1"
    ]
    
    for cmd in hf_commands:
        if not run_command(cmd, f"Installing {cmd.split()[1]}"):
            return False
    
    # Step 5: Install sentence transformers
    print("\n🔤 STEP 5: Installing Sentence Transformers")
    if not run_command("pip install sentence-transformers==2.2.2", "Installing Sentence Transformers"):
        print("⚠️ Trying alternative installation...")
        if not run_command("pip install sentence-transformers==2.1.0", "Installing Sentence Transformers (fallback)"):
            return False
    
    # Step 6: Install additional ML libraries
    print("\n📊 STEP 6: Installing ML libraries")
    ml_commands = [
        "pip install scikit-learn==1.3.2",
        "pip install numpy==1.25.2", 
        "pip install pandas==2.1.3",
        "pip install nltk==3.8.1"
    ]
    
    for cmd in ml_commands:
        if not run_command(cmd, f"Installing {cmd.split()[1]}"):
            return False
    
    # Step 7: Install web frameworks
    print("\n🌐 STEP 7: Installing web frameworks")
    web_commands = [
        "pip install fastapi==0.104.1",
        "pip install uvicorn[standard]==0.24.0",
        "pip install gradio==4.7.1",
        "pip install python-multipart==0.0.6",
        "pip install aiofiles==23.2.1",
        "pip install requests==2.31.0"
    ]
    
    for cmd in web_commands:
        if not run_command(cmd, f"Installing {cmd.split()[1]}"):
            return False
    
    # Step 8: Install optional production libraries
    print("\n⚡ STEP 8: Installing production libraries")
    prod_commands = [
        "pip install redis==5.0.1",
        "pip install psutil",
        "pip install python-dotenv"
    ]
    
    for cmd in prod_commands:
        run_command(cmd, f"Installing {cmd.split()[1]}", critical=False)
    
    # Step 9: Download NLTK data
    print("\n📚 STEP 9: Downloading NLTK data")
    nltk_downloads = [
        "python -c \"import nltk; nltk.download('punkt', quiet=True)\"",
        "python -c \"import nltk; nltk.download('wordnet', quiet=True)\"",
        "python -c \"import nltk; nltk.download('omw-1.4', quiet=True)\"",
        "python -c \"import nltk; nltk.download('stopwords', quiet=True)\""
    ]
    
    for cmd in nltk_downloads:
        run_command(cmd, "Downloading NLTK data", critical=False)
    
    # Step 10: Pre-download models
    print("\n🤖 STEP 10: Pre-downloading models")
    model_downloads = [
        "python -c \"from sentence_transformers import SentenceTransformer; SentenceTransformer('all-MiniLM-L6-v2')\"",
        "python -c \"from transformers import pipeline; pipeline('text2text-generation', model='google/flan-t5-small')\""
    ]
    
    for cmd in model_downloads:
        run_command(cmd, "Pre-downloading models", critical=False)
    
    print(f"\n🎉 INSTALLATION COMPLETED!")
    return True

def test_installation():
    """Test if all components are working"""
    print(f"\n🧪 TESTING INSTALLATION")
    print("=" * 30)
    
    test_results = {}
    
    # Test imports
    imports_to_test = [
        ("sentence_transformers", "SentenceTransformer"),
        ("transformers", "pipeline"), 
        ("torch", None),
        ("sklearn", None),
        ("nltk", None),
        ("gradio", None),
        ("fastapi", None)
    ]
    
    for module, component in imports_to_test:
        try:
            if component:
                exec(f"from {module} import {component}")
            else:
                exec(f"import {module}")
            print(f"✅ {module}: Import successful")
            test_results[module] = True
        except Exception as e:
            print(f"❌ {module}: Import failed - {e}")
            test_results[module] = False
    
    # Test model loading
    print(f"\n🤖 Testing model loading...")
    
    try:
        from sentence_transformers import SentenceTransformer
        model = SentenceTransformer('all-MiniLM-L6-v2')
        print("✅ Sentence transformer: Model loaded successfully")
        test_results['sentence_model'] = True
    except Exception as e:
        print(f"❌ Sentence transformer: Model loading failed - {e}")
        test_results['sentence_model'] = False
    
    try:
        from transformers import pipeline
        paraphraser = pipeline("text2text-generation", model="google/flan-t5-small")
        print("✅ Paraphrasing model: Model loaded successfully")  
        test_results['paraphrase_model'] = True
    except Exception as e:
        print(f"❌ Paraphrasing model: Model loading failed - {e}")
        test_results['paraphrase_model'] = False
    
    # Test GPU availability
    try:
        import torch
        if torch.cuda.is_available():
            print(f"✅ CUDA: {torch.cuda.device_count()} GPU(s) available")
            test_results['gpu'] = True
        else:
            print("💻 CUDA: Not available (using CPU)")
            test_results['gpu'] = False
    except:
        test_results['gpu'] = False
    
    return test_results

def create_production_requirements():
    """Create production requirements file"""
    requirements = """# AI Text Humanizer - Production Requirements
# All features enabled with compatible versions

# Core ML frameworks
torch>=2.1.0
transformers==4.35.0
sentence-transformers==2.2.2
huggingface_hub==0.17.3
accelerate==0.24.1

# NLP libraries  
nltk==3.8.1
scikit-learn==1.3.2
numpy==1.25.2
pandas==2.1.3

# Web frameworks
fastapi==0.104.1
uvicorn[standard]==0.24.0
gradio==4.7.1
python-multipart==0.0.6
aiofiles==23.2.1
requests==2.31.0

# Production libraries
redis==5.0.1
psutil
python-dotenv

# Build tools
setuptools
wheel
packaging
"""
    
    with open("requirements-production.txt", "w") as f:
        f.write(requirements)
    
    print("✅ Created requirements-production.txt")

def main():
    """Main installation process"""
    print("🚀 AI TEXT HUMANIZER - PRODUCTION SETUP")
    print("======================================")
    
    # Check Python version
    if sys.version_info < (3, 7):
        print("❌ Python 3.7+ required")
        return False
    
    print(f"🐍 Python {sys.version_info.major}.{sys.version_info.minor}.{sys.version_info.micro} detected")
    
    # Check virtual environment
    in_venv = hasattr(sys, 'real_prefix') or (hasattr(sys, 'base_prefix') and sys.base_prefix != sys.prefix)
    if not in_venv:
        print("⚠️ Warning: Not in virtual environment")
        response = input("Continue? (y/n): ").lower().strip()
        if response != 'y':
            print("👋 Please create a virtual environment first")
            return False
    else:
        print("✅ Virtual environment detected")
    
    # Start installation
    if not production_install():
        print("\n❌ Installation failed!")
        return False
    
    # Test installation
    test_results = test_installation()
    
    # Create requirements file
    create_production_requirements()
    
    # Summary
    print(f"\n📊 INSTALLATION SUMMARY")
    print("=" * 30)
    
    success_count = sum(1 for v in test_results.values() if v)
    total_count = len(test_results)
    
    print(f"✅ {success_count}/{total_count} components working")
    
    if test_results.get('sentence_model') and test_results.get('paraphrase_model'):
        print("🎉 ALL ADVANCED FEATURES ENABLED!")
        print("   • Advanced semantic similarity ✅")
        print("   • AI paraphrasing capabilities ✅")
        print("   • Production-ready performance ✅")
    elif test_results.get('sentence_model'):
        print("⚠️ Advanced similarity enabled, paraphrasing needs attention")
    elif test_results.get('paraphrase_model'):
        print("⚠️ Paraphrasing enabled, similarity needs attention")
    else:
        print("❌ Advanced features need troubleshooting")
    
    print(f"\n🎯 NEXT STEPS:")
    print("1. Test: python text_humanizer_robust.py")
    print("2. Run API: python fastapi_server.py") 
    print("3. Run web UI: python gradio_app.py")
    
    return success_count >= total_count - 2  # Allow 2 optional failures

if __name__ == "__main__":
    try:
        success = main()
        if success:
            print(f"\n🎉 Production installation successful!")
        else:
            print(f"\n❌ Production installation needs attention")
    except KeyboardInterrupt:
        print(f"\n👋 Installation cancelled")
    except Exception as e:
        print(f"\n❌ Unexpected error: {e}")