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app.py

@@ -43,8 +43,10 @@ def predict_sales():
     # Make a Sales prediction using the trained model
     salesPrediction = model.predict(input_data).tolist()[0]
 
+    predicted_revenue = round(float(salesPrediction), 2)
+
     # Return the actual price
-    return jsonify({'Predicted Sales Revenue Price': salesPrediction})
+    return jsonify({'Predicted Sales Revenue Price': predicted_revenue})
 
 # Batch Prediction
 # Define an endpoint to predict Sales revenue for a given batch of Product among the given store.
@@ -61,7 +63,8 @@ def predict_sales_batch():
     input_data["Product_Category"] = input_data["Product_Id"].str[:2]
 
     # Make predictions for the batch data and convert raw predictions into a readable format
-    salesPredictions = model.predict(input_data.drop("Product_Id",axis=1)).tolist()
+    salesPredictions = [round(pred, 2) for pred in model.predict(input_data.drop("Product_Id", axis=1)).tolist()]
+    ##salesPredictions = model.predict(input_data.drop("Product_Id",axis=1)).tolist()
     product_id_list = input_data.Product_Id.values.tolist()
     output_dict = dict(zip(product_id_list, salesPredictions))