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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() |