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app.py

@@ -1,8 +1,8 @@
 # Import necessary libraries
 import numpy as np
-import joblib  # For loading the serialized model
-import pandas as pd  # For data manipulation
-from flask import Flask, request, jsonify  # For creating the Flask API
+import joblib
+import pandas as pd
+from flask import Flask, request, jsonify
 
 # Initialize the Flask application
 Superkart_sales_predictor_api = Flask("SuperKart Store Sales Prediction")
@@ -10,81 +10,50 @@ Superkart_sales_predictor_api = Flask("SuperKart Store Sales Prediction")
 # Load the trained machine learning model
 model = joblib.load("best_random_forest_model.joblib")
 
-# Define a route for the home page (GET request)
+# Home route
 @Superkart_sales_predictor_api.get('/')
 def home():
-    """
-    This function handles GET requests to the root URL ('/') of the API.
-    It returns a simple welcome message.
-    """
     return "Welcome to the SuperKart Store Sales Prediction API!"
 
-# Define an endpoint for single property prediction (POST request)
+# Single prediction endpoint
 @Superkart_sales_predictor_api.post('/v1/sales')
 def predict_sales_price():
-    """
-    This function handles POST requests to the '/v1/sales' endpoint.
-    It expects a JSON payload containing store details and returns
-    the predicted sales price as a JSON response.
-    """
-    # Get the JSON data from the request body
-    store_data = request.get_json()
-    df = pd.DataFrame([store_data])
-
-    # Extract relevant features from the JSON data
-    sample = {
-        'Product_Weight': store_data['Product_Weight'],
-        'Product_Sugar_Content': store_data['Product_Sugar_Content'],
-        'Product_Allocated_Area': store_data['Product_Allocated_Area'],
-        'Product_Type': store_data['Product_Type'],
-        'Product_MRP': store_data['Product_MRP'],
-        'Store_Id': store_data['Store_Id'],
-        'Store_Establishment_Year':store_data['Store_Establishment_Year'],
-        'Store_Size': store_data['Store_Size'],
-        'Store_Type': store_data['Store_Type'],
-        'Store_Location_City_Type': store_data['Store_Location_City_Type']
-    }
-
-    # Convert the extracted data into a Pandas DataFrame
-    input_data = pd.DataFrame([sample])
-
-    # Make prediction (get log_price)
-    predicted_sale = model.predict(input_data)[0]
-
-    # Convert predicted_price to Python float
-    predicted_sale = round(float(predicted_sale), 2)
-    
-    # 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
-
-    # Return the actual price
-    return jsonify({'Predicted Sales (in dollars)': predicted_sale})
-
-
-# Define an endpoint for batch prediction (POST request)
+    try:
+        store_data = request.get_json()
+        sample = {
+            'Product_Weight': store_data['Product_Weight'],
+            'Product_Sugar_Content': store_data['Product_Sugar_Content'],
+            'Product_Allocated_Area': store_data['Product_Allocated_Area'],
+            'Product_Type': store_data['Product_Type'],
+            'Product_MRP': store_data['Product_MRP'],
+            'Store_Id': store_data['Store_Id'],
+            'Store_Establishment_Year': store_data['Store_Establishment_Year'],
+            'Store_Size': store_data['Store_Size'],
+            'Store_Type': store_data['Store_Type'],
+            'Store_Location_City_Type': store_data['Store_Location_City_Type']
+        }
+        input_data = pd.DataFrame([sample])
+        predicted_sale = model.predict(input_data)[0]
+        predicted_sale = round(float(predicted_sale), 2)
+        return jsonify({'Predicted Sales (in dollars)': predicted_sale})
+    except Exception as e:
+        print("ERROR in /v1/sales:", str(e))
+        return jsonify({'error': str(e)}), 500
+
+# Batch prediction endpoint
 @Superkart_sales_predictor_api.post('/v1/salesbatch')
 def predict_sales_price_batch():
-    """
-    This function handles POST requests to the '/v1/salesbatch' endpoint.
-    It expects a CSV file containing store details for multiple properties
-    and returns the predicted store sales as a dictionary in the JSON response.
-    """
-    # Get the uploaded CSV file from the request
-    file = request.files['file']
-
-    # Read the CSV file into a Pandas DataFrame
-    input_data = pd.read_csv(file)
-
-    # Make predictions for all properties in the DataFrame (get log_prices)
-    predicted_sale = model.predict(input_data).tolist()
-
-    # Create a dictionary of predictions with store IDs as keys
-    store_ids = input_data['Store_Id'].tolist() 
-    output_dict = dict(zip(store_ids, predicted_sale))  # Use actual sales
-
-    # Return the predictions dictionary as a JSON response
-    return output_dict
-
-# Run the Flask application in debug mode if this script is executed directly
+    try:
+        file = request.files['file']
+        input_data = pd.read_csv(file)
+        predicted_sale = model.predict(input_data).tolist()
+        store_ids = input_data['Store_Id'].tolist()
+        output_dict = dict(zip(store_ids, predicted_sale))
+        return jsonify(output_dict)
+    except Exception as e:
+        print("ERROR in /v1/salesbatch:", str(e))
+        return jsonify({'error': str(e)}), 500
+
+# Run the app
 if __name__ == '__main__':
     Superkart_sales_predictor_api.run(debug=True)