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Runtime error
Runtime error
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3c58b5f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 | import os
import joblib
import pandas as pd
from flask import Flask, request, jsonify
from flask_cors import CORS
# Initialize the Flask application
app = Flask(__name__)
CORS(app) # Enable Cross-Origin Resource Sharing
# --- Model Loading ---
# The model is loaded once when the application starts.
try:
# In Hugging Face Spaces, your files are all in the same root directory.
model_path = 'realistic_fraud_model.pkl'
model = joblib.load(model_path)
print("✅ Model loaded successfully.")
except FileNotFoundError:
print(f"❌ Error: Model file not found at '{model_path}'")
model = None
except Exception as e:
print(f"❌ An error occurred while loading the model: {e}")
model = None
# --- End of Model Loading ---
# --- API Endpoints ---
# Root endpoint to check if the API is running
@app.route('/', methods=['GET'])
def home():
return jsonify({"status": "API is running", "model_loaded": model is not None})
# Prediction endpoint
@app.route('/predict', methods=['POST'])
def predict():
"""
Receives feature data in JSON format and returns a fraud prediction.
"""
if model is None:
return jsonify({"error": "Model is not loaded."}), 500
try:
# Get data from the POST request
data = request.get_json(force=True)
# IMPORTANT: You must know the order of features your model expects.
# The input JSON should be an object with a key like "features"
# which is a list of values in the correct order.
# Example: {"features": [0.1, -0.5, 1.2, ...]}
features = data['features']
# Convert to a Pandas DataFrame for prediction
# The feature names here are placeholders, they don't affect a
# scikit-learn model prediction if the order is correct.
feature_df = pd.DataFrame([features])
# Make prediction
prediction = model.predict(feature_df)
prediction_proba = model.predict_proba(feature_df)
# Return the result as JSON
return jsonify({
'prediction': int(prediction[0]),
'is_fraud': bool(prediction[0] == 1),
'prediction_probability': {
'not_fraud': prediction_proba[0][0],
'fraud': prediction_proba[0][1]
}
})
except Exception as e:
return jsonify({"error": f"An error occurred during prediction: {str(e)}"}), 400
# --- End of API Endpoints ---
if __name__ == '__main__':
# The 'port' is set by Hugging Face Spaces, default to 8080 for local testing
port = int(os.environ.get("PORT", 8080))
app.run(host='0.0.0.0', port=port)
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