Update app.py

app.py

@@ -45,9 +45,7 @@ def predict_product_sales():
     input_data = pd.DataFrame([sample])
 
     # Make prediction (get product_store_sales_price)
-    predicted_sales_price = model.predict(input_data)[0]
-
-    predicted_sales_price = round(float(predicted_sales_price), 2)
+    predicted_sales_price = round(float(model.predict(input_data)[0]),2)
 
     return jsonify({'Predicted Product Sales Price':predicted_sales_price})
 
@@ -65,12 +63,13 @@ def predict_product_sales_batch():
     input_data = pd.read_csv(file)
     # Make predictions for all properties in the DataFrame (get prodict_sales)
     predicted_product_sales = model.predict(input_data).tolist()
+    predicted_product_sales = [round(float(p),2) for p in predicted_product_sales]
     # Create a dictionary of predictions with product IDs as keys
     product_ids=input_data['Product_Id'].tolist()
     output_dict=dict(zip(product_ids,predicted_product_sales))
 
     # Return the predictions dictionary as a JSON response
-    return output_dict
+    return jsonify(output_dict)
 
 # Run the Flask application in debug mode if this script is executed directly
 if __name__ == "__main__":