"""Test script to verify CSV integration into RAG system."""

import sys
from pathlib import Path

# Add parent directory to path to allow importing src modules
sys.path.insert(0, str(Path(__file__).parent.parent))

import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)


def test_csv_document_generation():
    """Test CSV document generation."""
    print("=" * 60)
    print("TEST 1: CSV Document Generation")
    print("=" * 60)
    
    try:
        from src.rag.csv_document_generator import CSVDocumentGenerator
        
        csv_path = Path("data/fraudTrain.csv")
        generator = CSVDocumentGenerator(csv_path, sample_size=1050000)
        
        print(f"\n✓ Created CSVDocumentGenerator")
        print(f"  CSV Path: {csv_path}")
        print(f"  Sample Size: 1,050,000 rows")
        
        # Generate all documents
        documents = generator.generate_all_documents()
        
        print(f"\n✓ Generated {len(documents)} documents from CSV")
        
        # Show sample document
        if documents:
            print(f"\n--- Sample Document ---")
            print(f"Type: {documents[0].metadata.get('type', 'N/A')}")
            print(f"Source: {documents[0].metadata.get('source', 'N/A')}")
            print(f"\nContent Preview:")
            print(documents[0].page_content[:400])
            print("...")
        
        return True
        
    except Exception as e:
        print(f"\n❌ Error: {str(e)}")
        import traceback
        traceback.print_exc()
        return False


def test_vector_store_integration():
    """Test vector store integration with CSV documents."""
    print("\n" + "=" * 60)
    print("TEST 2: Vector Store Integration")
    print("=" * 60)
    
    try:
        from src.rag.document_loader import DocumentLoader
        from src.rag.vector_store import VectorStore
        from src.config.config import settings
        
        document_loader = DocumentLoader()
        
        # Load CSV insights
        csv_path = settings.data_dir / "fraudTrain.csv"
        print(f"\n✓ Loading CSV insights from {csv_path}")
        
        csv_documents = document_loader.load_csv_insights(csv_path, sample_size=1050000)
        print(f"✓ Loaded {len(csv_documents)} CSV documents")
        
        # Create vector store and add documents
        print(f"\n✓ Creating vector store...")
        vector_store = VectorStore()
        vector_store.add_documents(csv_documents)
        print(f"✓ Added {len(csv_documents)} documents to vector store")
        
        # Test similarity search
        print(f"\n✓ Testing similarity search...")
        query = "What are fraud patterns in grocery stores?"
        results = vector_store.similarity_search(query, k=3)
        
        print(f"\n✓ Found {len(results)} relevant documents for query:")
        print(f"  '{query}'")
        
        for i, doc in enumerate(results, 1):
            print(f"\n--- Result {i} ---")
            print(f"Type: {doc.metadata.get('type', 'N/A')}")
            print(f"Category: {doc.metadata.get('category', 'N/A')}")
            print(f"Content: {doc.page_content[:200]}...")
        
        return True
        
    except Exception as e:
        print(f"\n❌ Error: {str(e)}")
        import traceback
        traceback.print_exc()
        return False


def test_full_rag_integration():
    """Test full RAG integration with both PDF and CSV."""
    print("\n" + "=" * 60)
    print("TEST 3: Full RAG Integration (PDF + CSV)")
    print("=" * 60)
    
    try:
        from src.rag.document_loader import DocumentLoader
        from src.rag.vector_store import VectorStore
        from src.config.config import settings
        
        document_loader = DocumentLoader(
            chunk_size=settings.chunk_size,
            chunk_overlap=settings.chunk_overlap,
        )
        
        all_documents = []
        
        # Load PDF documents
        print(f"\n✓ Loading PDF documents...")
        pdf_documents = document_loader.load_pdfs_from_directory(settings.pdf_dir)
        if pdf_documents:
            all_documents.extend(pdf_documents)
            print(f"✓ Loaded {len(pdf_documents)} PDF documents")
        
        # Load CSV insights
        print(f"\n✓ Loading CSV insights...")
        csv_path = settings.data_dir / "fraudTrain.csv"
        if csv_path.exists():
            csv_documents = document_loader.load_csv_insights(csv_path, sample_size=1050000)
            all_documents.extend(csv_documents)
            print(f"✓ Loaded {len(csv_documents)} CSV documents")
        
        # Create vector store
        print(f"\n✓ Creating unified vector store...")
        vector_store = VectorStore()
        vector_store.add_documents(all_documents)
        print(f"✓ Total documents in RAG: {len(all_documents)}")
        print(f"  - PDF documents: {len(pdf_documents)}")
        print(f"  - CSV documents: {len(csv_documents)}")
        
        # Test queries
        test_queries = [
            "What are common fraud patterns?",
            "Fraud rate in grocery transactions",
            "High risk merchants",
        ]
        
        print(f"\n✓ Testing queries with unified RAG...")
        for query in test_queries:
            results = vector_store.similarity_search(query, k=2)
            print(f"\nQuery: '{query}'")
            print(f"  Found {len(results)} results")
            for doc in results:
                doc_type = doc.metadata.get('type', 'pdf')
                source = doc.metadata.get('source', 'N/A')
                print(f"  - Source: {source} (Type: {doc_type})")
        
        return True
        
    except Exception as e:
        print(f"\n❌ Error: {str(e)}")
        import traceback
        traceback.print_exc()
        return False


if __name__ == "__main__":
    print("\n" + "=" * 60)
    print("CSV RAG INTEGRATION VERIFICATION")
    print("=" * 60)
    
    results = []
    
    # Run tests
    results.append(("CSV Document Generation", test_csv_document_generation()))
    results.append(("Vector Store Integration", test_vector_store_integration()))
    results.append(("Full RAG Integration", test_full_rag_integration()))
    
    # Summary
    print("\n" + "=" * 60)
    print("TEST SUMMARY")
    print("=" * 60)
    
    for test_name, passed in results:
        status = "✅ PASSED" if passed else "❌ FAILED"
        print(f"{status} - {test_name}")
    
    all_passed = all(result[1] for result in results)
    
    if all_passed:
        print("\n🎉 All tests passed! CSV integration is working correctly.")
    else:
        print("\n⚠️ Some tests failed. Please check the errors above.")
    
    print("=" * 60)