#!/usr/bin/env python3
"""
Test Conversational Voice Bot Features
Demonstrates how the bot now asks for clarification like your friend's bot
"""

import asyncio
import logging

# Setup logging
logging.basicConfig(level=logging.INFO, format='%(levelname)s: %(message)s')
logger = logging.getLogger(__name__)

async def test_conversational_features():
    """Test the conversational clarity detection"""
    
    print("🎯 Testing Conversational Voice Bot Features")
    print("=" * 60)
    print("This demonstrates how your bot now asks for clarification")
    print("just like your friend's superior voice bot!")
    print()
    
    try:
        from conversational_service import conversational_service
        
        # Test queries that should trigger clarification
        test_cases = [
            {
                "query": "pension",
                "description": "Vague pension query",
                "expected": "Should ask for specific pension topic"
            },
            {
                "query": "what",
                "description": "Too generic query", 
                "expected": "Should ask what topic user wants help with"
            },
            {
                "query": "help",
                "description": "General help request",
                "expected": "Should provide topic options"
            },
            {
                "query": "tender",
                "description": "Vague procurement query",
                "expected": "Should ask about specific procurement aspect"
            },
            {
                "query": "What are the pension rules?",
                "description": "Clear specific query",
                "expected": "Should proceed with search (no clarification needed)"
            }
        ]
        
        for i, test_case in enumerate(test_cases, 1):
            print(f"🔍 Test {i}: {test_case['description']}")
            print(f"Query: \"{test_case['query']}\"")
            print(f"Expected: {test_case['expected']}")
            print("-" * 50)
            
            # Analyze query clarity
            clarity_analysis = conversational_service.analyze_query_clarity(
                test_case['query'], 
                []  # Empty search results for testing
            )
            
            if clarity_analysis['clarification_needed']:
                print("✅ RESULT: Bot will ask for clarification")
                print("🤖 Bot response:")
                print()
                for question in clarity_analysis['clarification_questions']:
                    print(f"   {question}")
                print()
                
                if 'suggested_responses' in clarity_analysis:
                    for suggestion in clarity_analysis['suggested_responses']:
                        print(f"   {suggestion}")
                        
            else:
                print("✅ RESULT: Query is clear, will proceed with search")
                print(f"🎯 Confidence: {clarity_analysis['confidence']:.2f}")
            
            print()
            print("=" * 60)
            print()
    
    except ImportError as e:
        print(f"❌ Import error: {e}")
        print("💡 Make sure you're running from the PensionBot directory")
    except Exception as e:
        print(f"❌ Test error: {e}")

def demonstrate_conversation_flow():
    """Demonstrate a full conversation flow"""
    
    print("💬 Conversation Flow Demonstration")
    print("=" * 40)
    print("Here's how a conversation now works:")
    print()
    
    conversation = [
        {"speaker": "👤 User", "message": "pension"},
        {"speaker": "🤖 Bot", "message": """I can help with pension-related questions! To give you the most accurate information, could you specify:

📋 What specifically about pension?
• How to calculate pension amount?
• Pension eligibility criteria?
• How to apply for pension?
• Family pension benefits?
• Commutation of pension?

Please let me know which aspect interests you most!"""},
        {"speaker": "👤 User", "message": "calculation"},
        {"speaker": "🤖 Bot", "message": """Here's the pension calculation formula:

**Formula**: (Last drawn basic pay + DA) × service years ÷ 70
**Minimum pension**: ₹9,000 per month
**Maximum pension**: No upper limit

**Example calculation**:
- Last basic pay: ₹50,000
- DA: ₹25,000 (50% of basic)
- Service years: 30
- Pension = (50,000 + 25,000) × 30 ÷ 70 = ₹32,143 per month

Would you like me to help with a specific pension calculation for your situation?"""}
    ]
    
    for exchange in conversation:
        print(f"{exchange['speaker']}: {exchange['message']}")
        print()
    
    print("🎯 Key Improvements:")
    print("✅ Bot asks for clarification instead of guessing")
    print("✅ Provides structured options for user to choose")  
    print("✅ Follows up with specific, detailed information")
    print("✅ Encourages further conversation")
    print()
    print("This is exactly how your friend's bot works! 🎉")

if __name__ == "__main__":
    print("🎯 Conversational Voice Bot Test")
    print("Testing enhanced conversational features")
    print()
    
    # Run the tests
    asyncio.run(test_conversational_features())
    
    print("\n" + "="*60)
    demonstrate_conversation_flow()