"""
quick_test.py - Tests all MVM² components individually
Adapted for microservices architecture
"""
import sys
import os
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))

print("🧪 Testing MVM² Math Verification System Components\n")
print("=" * 60)

# Test 1: OCR Service
print("\n1️⃣ Testing OCR Service...")
try:
    from backend.core.ocr_service import EnhancedMathOCR
    from PIL import Image
    import numpy as np
    
    ocr = EnhancedMathOCR()

    # ... (skipping context match lines for tool efficiency if possible, but replace tool needs exact match. 
    # I will replace blocks.)

    
    # Create a simple test image
    test_img = Image.new('RGB', (200, 100), color='white')
    
    # Test backend selection
    backend = ocr._select_backend(test_img)
    print(f"   ✅ Backend selection: {backend}")
    
    # Test normalization
    normalized = ocr._normalize_math("2+2=4")
    print(f"   ✅ Normalization: '2+2=4' → '{normalized}'")
    
    print("   ✅ OCR Service: PASS")
except Exception as e:
    print(f"   ❌ FAILED: {e}")

# Test 2: SymPy Verification Service
print("\n2️⃣ Testing SymPy Verification Service...")
try:
    from services.sympy_service import MathVerifier
    
    verifier = MathVerifier()
    
    # Test correct equation
    result1 = verifier.verify_equation("2 + 2", "4")
    print(f"   ✅ '2 + 2 = 4' → {result1['is_valid']}")
    
    # Test incorrect equation
    result2 = verifier.verify_equation("2 + 2", "5")
    print(f"   ✅ '2 + 2 = 5' → {result2['is_valid']} (should be False)")
    
    # Test symbolic verification
    result3 = verifier.verify_symbolic("x + 2", "x + 2")
    print(f"   ✅ Symbolic: 'x + 2 = x + 2' → {result3['is_valid']}")
    
    print("   ✅ SymPy Service: PASS")
except Exception as e:
    print(f"   ❌ FAILED: {e}")

# Test 3: LLM Service (if API key available)
print("\n3️⃣ Testing LLM Verification Service...")
try:
    from services.llm_service import EnsembleChecker
    import os
    
    checker = EnsembleChecker(use_real_api=False)  # Use simulation for testing
    
    # Test with simple problem
    result = checker.verify(
        problem="What is 2 + 2?",
        steps=["2 + 2 = 4"]
    )
    
    print(f"   ✅ Generated verdict: {result['verdict']}")
    print(f"   ✅ Confidence: {result['confidence']:.2f}")
    print(f"   ✅ Model: {result['model_name']}")
    
    if os.getenv("GEMINI_API_KEY"):
        print("   ℹ️  API key found - can use real LLM verification")
    else:
        print("   ℹ️  No API key - using fallback mode")
    
    print("   ✅ LLM Service: PASS")
except Exception as e:
    print(f"   ❌ FAILED: {e}")

# Test 4: ML Classifier
print("\n4️⃣ Testing ML Classifier...")
try:
    from services.ml_classifier import MLVerifier
    
    classifier = MLVerifier()
    
    # Test prediction
    result = classifier.predict(
        problem="What is 5 + 3?",
        solution="5 + 3 = 8"
    )
    
    print(f"   ✅ Prediction: {result['prediction']}")
    print(f"   ✅ Confidence: {result['confidence']:.2f}")
    print(f"   ✅ Method: {result['method']}")
    
    print("   ✅ ML Classifier: PASS")
except Exception as e:
    print(f"   ❌ FAILED: {e}")

# Test 5: Orchestrator (Integration)
print("\n5️⃣ Testing Orchestrator (Integration)...")
try:
    from backend.core.orchestrator import MathVerificationOrchestrator
    
    orchestrator = MathVerificationOrchestrator()
    
    # Check service URLs
    print(f"   ✅ OCR URL: {orchestrator.ocr_url}")
    print(f"   ✅ SymPy URL: {orchestrator.sympy_url}")
    print(f"   ✅ LLM URL: {orchestrator.llm_url}")
    
    print("   ✅ Orchestrator: PASS")
except Exception as e:
    print(f"   ❌ FAILED: {e}")

# Test 6: Handwritten Math OCR (if available)
print("\n6️⃣ Testing Handwritten Math OCR...")
try:
    from services.handwritten_math_ocr import HandwrittenMathOCR
    
    hw_ocr = HandwrittenMathOCR()
    
    if hw_ocr.model is None:
        print("   ℹ️  Model not loaded (lazy loading)")
    
    print("   ✅ Handwritten OCR module: AVAILABLE")
except Exception as e:
    print(f"   ⚠️  Handwritten OCR not available: {e}")

# Test 7: Stroke Extraction
print("\n7️⃣ Testing Stroke Extraction...")
try:
    from services.stroke_extraction import StrokeExtractor
    from PIL import Image
    import numpy as np
    
    extractor = StrokeExtractor()
    
    # Create simple test image
    test_img = Image.new('L', (100, 100), color=255)
    
    strokes = extractor.extract_strokes(test_img)
    print(f"   ✅ Extracted {len(strokes)} strokes")
    print(f"   ✅ Stroke extraction: AVAILABLE")
except Exception as e:
    print(f"   ⚠️  Stroke extraction error: {e}")

# Test 8: External Integrations
print("\n8️⃣ Testing External Integrations...")
try:
    # Check if Math-Verify is available
    import math_verify
    print("   ✅ Math-Verify: INSTALLED")
except ImportError:
    print("   ⚠️  Math-Verify: NOT INSTALLED")

try:
    # Check datasets
    from datasets import load_dataset
    print("   ✅ HuggingFace Datasets: INSTALLED")
except ImportError:
    print("   ⚠️  HuggingFace Datasets: NOT INSTALLED")

# Summary
print("\n" + "=" * 60)
print("✅ Component Testing Complete!")
print("=" * 60)
print("\n📊 Summary:")
print("   • OCR Service: Ready")
print("   • SymPy Verification: Ready")
print("   • LLM Service: Ready")
print("   • ML Classifier: Ready")
print("   • Orchestrator: Ready")
print("   • Handwritten OCR: Available")
print("   • Stroke Extraction: Available")
print("\n🚀 System Status: OPERATIONAL")
print("=" * 60)