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app.py

@@ -33,7 +33,7 @@ def predict_sales():
     """
     # Get the JSON data from the request body
     input_data = request.get_json()
-    
+
     # Extract relevant features from the JSON data
     sample = {
         'Product_Weight': input_data['Product_Weight'],
@@ -46,32 +46,32 @@ def predict_sales():
         'Store_Location_City_Type': input_data['Store_Location_City_Type'],
         'Store_Type': input_data['Store_Type']
     }
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+
     # Convert the extracted data into a Pandas DataFrame
     features_df = pd.DataFrame([sample])
-    
+
     # Apply one-hot encoding for nominal columns (matching training)
     features_df = pd.get_dummies(features_df, columns=['Product_Type', 'Store_Type'], drop_first=True)
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+
     # Apply ordinal encoding (based on provided orders)
     sugar_mapping = {'No Sugar': 0, 'Low Sugar': 1, 'Regular': 2}
     size_mapping = {'Small': 0, 'Medium': 1, 'High': 2}
     city_mapping = {'Tier 3': 0, 'Tier 2': 1, 'Tier 1': 2}
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+
     features_df['Product_Sugar_Content'] = features_df['Product_Sugar_Content'].map(sugar_mapping)
     features_df['Store_Size'] = features_df['Store_Size'].map(size_mapping)
     features_df['Store_Location_City_Type'] = features_df['Store_Location_City_Type'].map(city_mapping)
-    
+
     # Make prediction (assuming direct sales prediction; adjust if log-transformed)
     predicted_sales = model.predict(features_df)[0]
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+
     # If your model predicts log(sales), uncomment and use this instead:
     # predicted_log_sales = model.predict(features_df)
     # predicted_sales = np.exp(predicted_log_sales)
-    
+
     # Convert to Python float and round to 2 decimals
     predicted_sales = round(float(predicted_sales), 2)
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+
     # Return the predicted sales total
     return jsonify({'Predicted Sales Total (in dollars)': predicted_sales})