diagnose_and_fix.py

#!/usr/bin/env python3
"""
Production Diagnostics and Quick Fix for AI Text Humanizer
This script will identify exactly what's wrong and fix it
"""

import sys
import subprocess
import importlib
import os

def test_import(module_name, component=None):
    """Test if a module/component can be imported"""
    try:
        if component:
            module = importlib.import_module(module_name)
            getattr(module, component)
            return True, "OK"
        else:
            importlib.import_module(module_name)
            return True, "OK"
    except ImportError as e:
        return False, f"ImportError: {str(e)}"
    except AttributeError as e:
        return False, f"AttributeError: {str(e)}"
    except Exception as e:
        return False, f"Error: {str(e)}"

def run_pip_command(cmd):
    """Run pip command safely"""
    try:
        result = subprocess.run(cmd, shell=True, capture_output=True, text=True, check=True)
        return True, result.stdout
    except subprocess.CalledProcessError as e:
        return False, e.stderr

def main():
    print("🔧 AI TEXT HUMANIZER - PRODUCTION DIAGNOSTICS & FIX")
    print("=" * 60)
    print("This will diagnose and fix your advanced model issues\n")
    
    # Test current imports
    print("📋 CURRENT STATUS:")
    print("-" * 20)
    
    tests = [
        ("sentence_transformers", "SentenceTransformer"),
        ("transformers", "pipeline"),
        ("torch", None),
        ("sklearn", None),
        ("nltk", None),
        ("gradio", None)
    ]
    
    results = {}
    for module, component in tests:
        success, message = test_import(module, component)
        status = "✅ WORKING" if success else "❌ FAILED"
        print(f"{module}: {status}")
        if not success:
            print(f"   Error: {message}")
        results[module] = success
    
    # Check specific model loading
    print(f"\n🤖 TESTING MODEL LOADING:")
    print("-" * 30)
    
    if results.get('sentence_transformers'):
        try:
            print("🔄 Testing sentence transformer model...")
            from sentence_transformers import SentenceTransformer
            model = SentenceTransformer('all-MiniLM-L6-v2')
            test_result = model.encode(["test"])
            print("✅ Sentence transformer: MODEL LOADED")
            results['sentence_model'] = True
        except Exception as e:
            print(f"❌ Sentence transformer: MODEL FAILED - {e}")
            results['sentence_model'] = False
    else:
        results['sentence_model'] = False
    
    if results.get('transformers'):
        try:
            print("🔄 Testing paraphrasing model...")
            from transformers import pipeline
            paraphraser = pipeline("text2text-generation", model="google/flan-t5-small")
            test_result = paraphraser("test sentence", max_length=50)
            print("✅ Paraphrasing: MODEL LOADED")
            results['paraphrase_model'] = True
        except Exception as e:
            print(f"❌ Paraphrasing: MODEL FAILED - {e}")
            results['paraphrase_model'] = False
    else:
        results['paraphrase_model'] = False
    
    # Analyze issues and provide fixes
    print(f"\n🎯 DIAGNOSIS & SOLUTIONS:")
    print("-" * 30)
    
    if not results['sentence_transformers']:
        print("🚨 ISSUE: sentence-transformers not working")
        print("💡 SOLUTION:")
        print("   pip uninstall -y sentence-transformers huggingface_hub")
        print("   pip install huggingface_hub==0.17.3")
        print("   pip install sentence-transformers==2.2.2")
        print()
        
        fix = input("🔧 Apply this fix now? (y/n): ").lower().strip()
        if fix == 'y':
            print("🔄 Applying sentence-transformers fix...")
            success1, _ = run_pip_command("pip uninstall -y sentence-transformers huggingface_hub")
            success2, _ = run_pip_command("pip install huggingface_hub==0.17.3")
            success3, _ = run_pip_command("pip install sentence-transformers==2.2.2")
            
            if success1 and success2 and success3:
                print("✅ Fix applied successfully!")
                # Test again
                success, message = test_import('sentence_transformers', 'SentenceTransformer')
                if success:
                    print("✅ sentence-transformers now working!")
                    results['sentence_transformers'] = True
                else:
                    print(f"❌ Still not working: {message}")
            else:
                print("❌ Fix failed")
    
    if not results['transformers']:
        print("🚨 ISSUE: transformers not working")
        print("💡 SOLUTION:")
        print("   pip install transformers==4.35.0 torch")
        print()
        
        fix = input("🔧 Apply this fix now? (y/n): ").lower().strip()
        if fix == 'y':
            print("🔄 Applying transformers fix...")
            success1, _ = run_pip_command("pip install transformers==4.35.0")
            success2, _ = run_pip_command("pip install torch")
            
            if success1 and success2:
                print("✅ Fix applied successfully!")
                success, message = test_import('transformers', 'pipeline')
                if success:
                    print("✅ transformers now working!")
                    results['transformers'] = True
                else:
                    print(f"❌ Still not working: {message}")
            else:
                print("❌ Fix failed")
    
    # Final test with our humanizer
    print(f"\n🧪 FINAL TEST:")
    print("-" * 15)
    
    try:
        # Try importing our production version
        if os.path.exists("text_humanizer_production.py"):
            sys.path.insert(0, ".")
            from text_humanizer_production import ProductionAITextHumanizer
            
            print("🔄 Creating production humanizer...")
            humanizer = ProductionAITextHumanizer()
            
            print("🔄 Testing humanization...")
            result = humanizer.humanize_text_production(
                "Furthermore, it is important to note that these systems demonstrate significant capabilities.",
                style="conversational",
                intensity=0.8
            )
            
            print("✅ PRODUCTION TEST SUCCESSFUL!")
            print(f"Original: Furthermore, it is important to note that...")
            print(f"Humanized: {result['humanized_text']}")
            print(f"Quality Score: {result['quality_score']:.3f}")
            
            # Check what features are working
            working_features = sum([
                results.get('sentence_model', False),
                results.get('paraphrase_model', False),
                True,  # Basic features always work
            ])
            
            if working_features >= 2:
                print("🎉 PRODUCTION READY!")
            else:
                print("⚠️ Limited features - but still functional")
                
        else:
            print("❌ text_humanizer_production.py not found")
    
    except Exception as e:
        print(f"❌ Final test failed: {e}")
    
    # Summary and next steps
    print(f"\n📊 SUMMARY:")
    print("-" * 12)
    
    working_count = sum([
        results.get('sentence_transformers', False),
        results.get('transformers', False),
        results.get('sentence_model', False),
        results.get('paraphrase_model', False)
    ])
    
    if working_count >= 3:
        print("🎉 ALL ADVANCED FEATURES WORKING!")
        print("✅ Your AI Text Humanizer is production-ready")
        print("\n🚀 Next steps:")
        print("   python text_humanizer_production.py  # Test it")
        print("   python fastapi_server.py             # Run API")
        print("   python gradio_app.py                 # Run web UI")
        
    elif working_count >= 1:
        print("⚠️ SOME FEATURES WORKING")
        print("✅ Your humanizer will work with reduced functionality")
        print("\n🚀 To enable all features, run the fixes above")
        
    else:
        print("❌ CRITICAL ISSUES DETECTED")
        print("💡 Run this command for a fresh start:")
        print("   python install_production.py")
    
    print(f"\n📞 Need help? Check:")
    print("   - README.md for detailed setup")
    print("   - DEPENDENCY_FIX.md for troubleshooting")
    print("   - Run: python install_production.py")

if __name__ == "__main__":
    main()