#!/usr/bin/env python3
"""
Simple test script to verify vectorization functionality.
Run this to check if sentence-transformers is working correctly.
"""

import os
import sys

def test_vectorization():
    """Test the vectorization functionality."""
    print("🧪 Testing vectorization functionality...")
    
    # Test 1: Import dependencies
    print("\n1. Testing imports...")
    try:
        import numpy as np
        print("✅ numpy imported successfully")
    except ImportError as e:
        print(f"❌ numpy import failed: {e}")
        return False
    
    try:
        from sentence_transformers import SentenceTransformer
        print("✅ sentence-transformers imported successfully")
    except ImportError as e:
        print(f"❌ sentence-transformers import failed: {e}")
        print("Install with: pip install sentence-transformers")
        return False
    
    # Test 2: Load model
    print("\n2. Testing model loading...")
    try:
        model = SentenceTransformer('all-MiniLM-L6-v2')
        print("✅ SentenceTransformer model loaded successfully")
    except Exception as e:
        print(f"❌ Model loading failed: {e}")
        return False
    
    # Test 3: Create embeddings
    print("\n3. Testing embedding creation...")
    try:
        test_texts = [
            "This is a Python function for machine learning",
            "Here's a repository configuration file",
            "Installation instructions for the project"
        ]
        embeddings = model.encode(test_texts)
        print(f"✅ Created embeddings with shape: {embeddings.shape}")
    except Exception as e:
        print(f"❌ Embedding creation failed: {e}")
        return False
    
    # Test 4: Test similarity calculation
    print("\n4. Testing similarity calculation...")
    try:
        query_embedding = model.encode(["Python code example"])
        similarities = []
        for embedding in embeddings:
            similarity = np.dot(query_embedding[0], embedding) / (
                np.linalg.norm(query_embedding[0]) * np.linalg.norm(embedding)
            )
            similarities.append(similarity)
        print(f"✅ Similarity scores: {[f'{s:.3f}' for s in similarities]}")
    except Exception as e:
        print(f"❌ Similarity calculation failed: {e}")
        return False
    
    # Test 5: Test repo_explorer integration
    print("\n5. Testing repo_explorer integration...")
    try:
        from repo_explorer import SimpleVectorStore, vectorize_repository_content
        
        # Create test repository content
        test_repo_content = """# Test Repository
import numpy as np
import pandas as pd

def main():
    print("Hello, world!")
    
class DataProcessor:
    def __init__(self):
        self.data = []
    
    def process(self, data):
        return data.upper()

if __name__ == "__main__":
    main()
"""
        
        # Test vectorization
        success = vectorize_repository_content(test_repo_content, "test/repo")
        if success:
            print("✅ Repository vectorization successful")
            
            # Test vector store
            from repo_explorer import vector_store
            stats = vector_store.get_stats()
            print(f"✅ Vector store stats: {stats}")
            
            # Test search
            results = vector_store.search("Python function", top_k=2)
            if results:
                print(f"✅ Vector search returned {len(results)} results")
                for i, (chunk, similarity, metadata) in enumerate(results):
                    print(f"   Result {i+1}: similarity={similarity:.3f}")
            else:
                print("⚠️ Vector search returned no results")
        else:
            print("❌ Repository vectorization failed")
            return False
            
    except Exception as e:
        print(f"❌ repo_explorer integration test failed: {e}")
        return False
    
    print("\n🎉 All tests passed! Vectorization is working correctly.")
    return True

if __name__ == "__main__":
    print("Repository Explorer Vectorization Test")
    print("=" * 45)
    
    success = test_vectorization()
    
    if success:
        print("\n✅ Ready to use vectorization in repo explorer!")
        print("   The sentence-transformers model will be downloaded on first use.")
    else:
        print("\n❌ Vectorization setup incomplete.")
        print("   Make sure to install: pip install sentence-transformers numpy")
    
    sys.exit(0 if success else 1)