import requests
import json
from typing import List, Dict, Any

# Endpoint URL (assuming the FastAPI server is running locally on port 7860)
BASE_URL = "http://localhost:7860"

def create_sample_transactions(num_transactions: int = 3) -> List[Dict[str, Any]]:
    """
    Generate sample transaction data for testing the /llm-analyse endpoint.
    Includes all 22 required fields: fraud_score, STATUS, cc_num, merchant, category,
    amt, gender, state, zip, lat, long, city_pop, job, unix_time, merch_lat,
    merch_long, is_fraud, age, trans_hour, trans_day, trans_month, trans_weekday, distance.
    """
    samples = []
    for i in range(num_transactions):
        transaction = {
            "fraud_score": round(10 + (i * 20), 2),  # Vary fraud_score: 10, 30, 50 for example
            "STATUS": "approved" if i < 2 else "declined",  # Mix statuses
            "cc_num": 4532015112830366 + i,  # Fake CC numbers
            "merchant": f"merchant_{i+1}",
            "category": ["gas", "grocery", "entertainment"][i % 3],
            "amt": round(50 + (i * 100), 2),  # Increasing amounts: 50, 150, 250
            "gender": "F" if i % 2 == 0 else "M",
            "state": ["NY", "CA", "TX"][i % 3],
            "zip": 10001 + i * 100,
            "lat": 40.7128 + (i * 0.1),
            "long": -74.0060 + (i * 0.1),
            "city_pop": 8000000 - (i * 1000000),
            "job": ["Lawyer", "Doctor", "Engineer"][i % 3],
            "unix_time": 1640995200 + (i * 3600),  # Sequential hours
            "merch_lat": 40.7589 + (i * 0.05),
            "merch_long": -73.9851 + (i * 0.05),
            "is_fraud": 0 if i < 2 else 1,
            "age": 30 + i * 5,
            "trans_hour": (12 + i) % 24,
            "trans_day": i + 1,
            "trans_month": 12,
            "trans_weekday": (i % 7) + 1,
            "distance": round(5 + (i * 10), 2)  # Increasing distance
        }
        samples.append(transaction)
    return samples

def test_llm_analyse():
    """
    Test the /llm-analyse endpoint by sending sample transactions and printing the response.
    """
    endpoint = f"{BASE_URL}/llm-analyse"
    
    # Prepare payload
    payload = {
        "transactions": create_sample_transactions(3)
    }
    
    print("📤 Sending request to /llm-analyse...")
    print(json.dumps(payload, indent=2))
    print("-" * 50)
    
    try:
        response = requests.post(endpoint, json=payload)
        response.raise_for_status()  # Raise an HTTPError for bad responses
        
        result = response.json()
        print("✅ Response received:")
        print(json.dumps(result, indent=2))
        
        # Additional checks
        if "fraud_score" in result and "explanation" in result:
            fraud_score = result["fraud_score"]
            explanation = result["explanation"]
            print(f"\n📊 Overall Fraud Score: {fraud_score} ({fraud_score * 100:.1f}%)")
            print(f"💡 Explanation: {explanation}")
            
            # Simple categorization
            if fraud_score < 0.5:
                print("🟢 Assessment: Good (Low Risk)")
            elif 0.5 <= fraud_score <= 0.6:
                print("🟡 Assessment: Uncertain")
            else:
                print("🔴 Assessment: Suspicious/Critical")
        else:
            print("⚠️ Unexpected response format.")
            
    except requests.exceptions.RequestException as e:
        print(f"❌ Request failed: {e}")
        if hasattr(e.response, 'text'):
            print(f"Server response: {e.response.text}")
    except json.JSONDecodeError as e:
        print(f"❌ Failed to parse JSON response: {e}")
        print(f"Raw response: {response.text}")

if __name__ == "__main__":
    # Run the test
    test_llm_analyse()