#!/usr/bin/env python3
"""
Test script to demonstrate the enhanced QA system with interview-style question handling.
This script tests both regular questions and interview-style questions to show the difference.
"""

import os
import sys
from dotenv import load_dotenv

# Add the current directory to Python path
sys.path.append(os.path.dirname(os.path.abspath(__file__)))

from queryrun import FAISSQuerySystem

def test_questions():
    """Test various types of questions to demonstrate the DeepSeek-enhanced system"""
    
    # Load environment variables
    load_dotenv()
    
    # Test questions
    test_cases = [
        {
            "type": "General Question",
            "question": "What is Julien's educational background?"
        },
        {
            "type": "Interview Question",
            "question": "Tell me about a challenging project you worked on and how you overcame the difficulties."
        },
        {
            "type": "Technical Question", 
            "question": "What programming languages does Julien know?"
        },
        {
            "type": "Experience Question",
            "question": "Describe your experience with machine learning and what excites you most about AI research."
        },
        {
            "type": "Research Question",
            "question": "What are Julien's research interests?"
        },
        {
            "type": "Personal Question",
            "question": "Walk me through your journey in computer science and what motivated you to pursue this field."
        }
    ]
    
    try:
        print("Initializing RAG system...")
        query_system = FAISSQuerySystem()
        print("RAG system ready!\n")
        
        for i, test_case in enumerate(test_cases, 1):
            print(f"{'='*80}")
            print(f"TEST CASE {i}: {test_case['type']}")
            print(f"Question: {test_case['question']}")
            print(f"{'='*80}")
            
            # Search for relevant documents
            print("\nSearching for relevant documents...")
            docs = query_system.search(test_case['question'], k=5)
            print(f"Found {len(docs)} relevant documents")
            
            # Generate response using DeepSeek (with Cohere fallback)
            print("\nGenerating response with DeepSeek...")
            response = query_system.generate_hybrid_response(test_case['question'], docs)
            
            print(f"\nResponse:")
            print("-" * 60)
            print(response)
            print("-" * 60)
            
            # Show sources
            if docs:
                print(f"\nSources used:")
                for j, doc in enumerate(docs[:3], 1):  # Show top 3 sources
                    print(f"  {j}. {doc['metadata'].get('source', 'Unknown')} (score: {doc['score']:.4f})")
            
            print(f"\n{'-'*80}\n")
            
    except Exception as e:
        print(f"Error during testing: {e}")
        import traceback
        traceback.print_exc()

if __name__ == "__main__":
    test_questions()