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app.py

@@ -16,17 +16,18 @@ def home():
 # Define an endpoint to predict churn for a single customer
 @superkart_product_sales_prediction_api.post('/v1/forecast')
 def predict_sales_forecast():
-   """
+
+  """
     This function handles POST requests to the '/v1/forecast' endpoint.
     It expects a JSON payload containing property details and returns
     the predicted rental price as a JSON response.
-    """
+  """
 
     # Get JSON data from the request
-    product_data = request.get_json()
+  product_data = request.get_json()
 
-    # Extract relevant customer features from the input data
-    sample = {
+  # Extract relevant customer features from the input data
+  sample = {
         'Product_Weight': product_data.get('Product_Weight'),
         'Product_Sugar_Content': product_data.get('Product_Sugar_Content'),
         'Product_Allocated_Area': product_data.get('Product_Allocated_Area'),
@@ -39,18 +40,18 @@ def predict_sales_forecast():
         'Store_Type': product_data.get('Store_Type')
     }
 
-    # Convert the extracted data into a DataFrame
-    input_data = pd.DataFrame([sample])
+  # Convert the extracted data into a DataFrame
+  input_data = pd.DataFrame([sample])
 
-    # Make a churn prediction using the trained model
-    predicted_sales = model.predict(input_data).tolist()[0]
+  # Make a churn prediction using the trained model
+  predicted_sales = model.predict(input_data).tolist()[0]
 
-    # Convert predicted_price to Python float
-    predicted_sales = round(float(predicted_sales), 2)
+  # Convert predicted_price to Python float
+  predicted_sales = round(float(predicted_sales), 2)
 
 
-    # Return the prediction as a JSON response
-    return jsonify({'Prediction': predicted_sales})
+  # Return the prediction as a JSON response
+  return jsonify({'Prediction': predicted_sales})
 
 @superkart_product_sales_prediction_api.post('/v1/forecastbatch')
 def predict_sales_forecast_batch():