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app.py

@@ -1,16 +1,12 @@
-# Import necessary libraries
-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_api = Flask("SuperKart Sales Forecast API")
 
-# Load the trained machine learning pipeline (preprocessor + model)
-# Make sure this file is present next to app.py in your backend folder
 model = joblib.load("sales_forecast_model_v1_0.joblib")
 
-# Expected feature names (order doesn't matter for DataFrame, kept for clarity)
 EXPECTED_FEATURES = [
     "Product_Weight",
     "Product_Allocated_Area",
@@ -31,21 +27,6 @@ def home():
     Returns a simple welcome message and the expected schema.
     """
     return "Welcome to the SuperKart Sales Forecast API!"
-    
-  #  jsonify({
-   #     "message": ,
-    #    "expected_payload": {
-     #       "Product_Weight": "float",
-      #      "Product_Allocated_Area": "float (0-1)",
-       #     "Product_MRP": "float",
-        #    "Store_Age": "int",
-         #   "Product_Sugar_Content": "str (e.g., 'Regular', 'Low Sugar', 'No Sugar')",
-          #  "Product_Type": "str (e.g., 'Snack Foods', 'Dairy', ...)",
-           # "Store_Size": "str (e.g., 'Small', 'Medium', 'High')",
-            #"Store_Location_City_Type": "str (e.g., 'Tier 1', 'Tier 2', 'Tier 3')",
-            #"Store_Type": "str (e.g., 'Supermarket Type 2', 'Departmental Store', ...)"
-        #}
-   #})
 
 # Define an endpoint for single sales prediction (POST request)
 @superkart_sales_api.post("/v1/sales")
@@ -86,7 +67,7 @@ def predict_sales():
 
         # Predict sales (model outputs actual sales; no log transform)
         pred = model.predict(input_df)[0]
-        pred = round(float(pred), 2)  # ensure JSON-serializable and nicely rounded
+        pred = round(float(pred), 2)  
 
         return jsonify({"Predicted Product_Store_Sales_Total": pred})