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app.py

@@ -48,12 +48,13 @@ def predict_product_sales():
     # Make prediction (get Product_Store_Sales_Total)
     predicted_Product_Store_Sales_Total = model.predict(input_data)[0]
     print(f"Predicted Product_Store_Sales_Total: {predicted_Product_Store_Sales_Total}")
-
-    # Calculate actual price
-    predicted_price = np.exp(predicted_Product_Store_Sales_Total)
+    
+    # Calculate actual price safely (avoid overflow)
+    predicted_price = np.exp(np.clip(predicted_Product_Store_Sales_Total, -100, 100))
 
     # Convert predicted_price to Python float
     predicted_price = round(float(predicted_price), 2)
+
     # The conversion above is needed as we convert the model prediction (log price) to actual price using np.exp, which returns predictions as NumPy float32 values.
     # When we send this value directly within a JSON response, Flask's jsonify function encounters a datatype error