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import torch
import torch.nn as nn
from flask import Flask, request, jsonify
# --- 1. Define the exact same Model Architecture ---
# This is necessary to load the saved state_dict
class FraudClassifier(nn.Module):
def __init__(self):
super(FraudClassifier, self).__init__()
self.layer1 = nn.Linear(2, 16)
self.layer2 = nn.Linear(16, 8)
self.layer3 = nn.Linear(8, 1)
self.relu = nn.ReLU()
self.sigmoid = nn.Sigmoid()
def forward(self, x):
x = self.relu(self.layer1(x))
x = self.relu(self.layer2(x))
x = self.sigmoid(self.layer3(x))
return x
# --- 2. Initialize App and Load Assets ---
app = Flask(__name__)
model = None
scaler_params = None
try:
# Load scaler parameters
scaler_params = torch.load("scaler_params.pt")
mean = scaler_params['mean']
std = scaler_params['std']
print("✅ PyTorch scaler parameters loaded.")
# Load the trained model
model = FraudClassifier()
model.load_state_dict(torch.load("realistic_fraud_model.pth"))
model.eval() # Set to evaluation mode
print("✅ PyTorch model loaded successfully.")
except Exception as e:
print(f"❌ Error loading assets: {e}")
# --- 3. Define API Endpoints ---
@app.route('/', methods=['GET'])
def home():
"""Root endpoint to check API status."""
return jsonify({
"status": "API is running",
"model_loaded": model is not None,
"scaler_loaded": scaler_params is not None
})
@app.route('/predict', methods=['POST'])
def predict():
"""Receives feature data and returns a fraud prediction."""
if not model or not scaler_params:
return jsonify({"error": "Model or scaler not loaded."}), 500
try:
data = request.get_json(force=True)
# Expected input: {"features": [value_ether, fee_ether]}
features = data['features']
# Convert to a PyTorch tensor
features_tensor = torch.tensor(features, dtype=torch.float32)
# Apply the scaling transformation
scaled_tensor = (features_tensor - mean) / std
# Get model prediction
with torch.no_grad():
probability = model(scaled_tensor)
prob_fraud = probability.item()
prediction = 1 if prob_fraud > 0.5 else 0
# Return the result
return jsonify({
'prediction': prediction,
'is_fraud': bool(prediction == 1),
'prediction_probability': {
'not_fraud': 1.0 - prob_fraud,
'fraud': prob_fraud
}
})
except Exception as e:
return jsonify({"error": f"An error occurred during prediction: {str(e)}"}), 400
if __name__ == '__main__':
port = int(os.environ.get("PORT", 8080))
app.run(host='0.0.0.0', port=port) |