#!/usr/bin/env python3
"""
Test specific multimedia GAIA questions
"""

import os
from dotenv import load_dotenv
from app import BasicAgent

# Load environment variables
load_dotenv()

def test_specific_questions():
    """Test specific GAIA questions with multimedia"""
    
    # Initialize agent
    agent = BasicAgent()
    api_key = os.getenv("ANTHROPIC_API_KEY")
    if not api_key:
        print("Error: ANTHROPIC_API_KEY not found in environment variables")
        return
    
    agent.set_api_key(api_key)
    
    # Test specific questions
    test_cases = [
        {
            "question": "What is 2 + 2?",
            "expected": "4",
            "type": "simple"
        },
        {
            "question": 'In the video https://www.youtube.com/watch?v=1htKBjuUWec, Verma claims the existence of "a "moat" in the education system that provides a systemic advantage for those who know about it and can get into the pipeline." Verma\'s "moat" is a well-known advantage for students. What is the four-letter abbreviation used to describe this systemic advantage?',
            "expected": "STEM",
            "type": "youtube"
        },
        {
            "question": "Tell me the amount of sales in the sales sheet for the attached excel file.",
            "expected": "Unable to determine",
            "type": "excel"
        },
        {
            "question": "How many times is the word \"therefore\" used in the attached PDF?",
            "expected": "Unable to determine",
            "type": "pdf"
        },
        {
            "question": "In the attached Python code, I try to use the string method zfill. It does not work. Can you fix the problem for me and give me the only the complete corrected code?",
            "expected": "Unable to determine",
            "type": "code"
        }
    ]
    
    correct = 0
    for i, test_case in enumerate(test_cases, 1):
        question = test_case["question"]
        expected = test_case["expected"]
        q_type = test_case["type"]
        
        print(f"\nTest {i} ({q_type}): {question[:80]}...")
        print(f"Expected: {expected}")
        
        try:
            answer = agent(question)
            print(f"Got: {answer}")
            
            # Check if answer matches expected
            if q_type in ["excel", "pdf", "code"] and "Unable to determine" in answer:
                print("✅ Correctly handled inaccessible file")
                correct += 1
            elif expected.lower() in answer.lower():
                print("✅ Correct answer")
                correct += 1
            else:
                print("❌ Incorrect answer")
                
        except Exception as e:
            print(f"❌ Error: {e}")
    
    print(f"\n{'='*80}")
    print(f"Score: {correct}/{len(test_cases)} ({correct/len(test_cases)*100:.0f}%)")
    print(f"{'='*80}")

if __name__ == "__main__":
    test_specific_questions()