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app.py

@@ -1,5 +1,4 @@
 
-import os
 from flask import Flask, request, jsonify
 import pandas as pd
 import joblib
@@ -11,8 +10,6 @@ import json
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
-#os.makedirs('backend_deployment', exist_ok=True)
-
 # Initialize Flask app
 app = Flask(__name__)
 
@@ -149,17 +146,26 @@ def batch_predict():
         
         for i, item in enumerate(data['predictions']):
             try:
+                # Calculate derived features
+                current_year = 2025
+                store_age = current_year - int(item['Store_Establishment_Year'])
+                price_efficiency = float(item['Product_MRP']) * 0.1
+                
                 # Create DataFrame for prediction
                 input_data = pd.DataFrame([{
+                    'Product_Id': f'FD{i+1:03d}',  # Unique product ID for batch
                     'Product_Weight': float(item['Product_Weight']),
                     'Product_Sugar_Content': str(item['Product_Sugar_Content']),
                     'Product_Allocated_Area': float(item['Product_Allocated_Area']),
                     'Product_Type': str(item['Product_Type']),
                     'Product_MRP': float(item['Product_MRP']),
+                    'Store_Id': f'OUT{i+1:03d}',  # Unique store ID for batch
                     'Store_Establishment_Year': int(item['Store_Establishment_Year']),
                     'Store_Size': str(item['Store_Size']),
                     'Store_Location_City_Type': str(item['Store_Location_City_Type']),
-                    'Store_Type': str(item['Store_Type'])
+                    'Store_Type': str(item['Store_Type']),
+                    'Store_Age': store_age,
+                    'Price_Efficiency': price_efficiency
                 }])
                 
                 prediction = model.predict(input_data)[0]