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app.py

@@ -47,13 +47,10 @@ def predict_sales():
     input_data = pd.DataFrame([sample])
 
     # Make prediction (get log_sales)
-    predicted_price = model.predict(input_data)[0]
+    predictions = model.predict(input_data)[0]
 
-    # Calculate actual sales
-    # predicted_sales = np.exp(predicted_log_price)
-
-    # Convert predicted_sales to Python float
-    predicted_sales = round(float(predicted_price), 2)
+    # Round predictions
+    predicted_sales = [round(float(x), 2) for x in predictions]
     # 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