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app.py

@@ -29,6 +29,7 @@ def predict_revenue():
             'Product_MRP': product_store_data['Product_MRP'],
             'Store_Id': product_store_data['Store_Id'],
             'Store_Establishment_Year': product_store_data['Store_Establishment_Year'],
+            'Store_Size': product_store_data['Store_Size'],
             'Store_Location_City_Type': product_store_data['Store_Location_City_Type'],
             'Store_Type': product_store_data['Store_Type']
         }
@@ -44,26 +45,5 @@ def predict_revenue():
     except Exception as e:
         return jsonify({"error": str(e)}), 500
 
-
-# -------- Batch prediction ----------
-@sales_revenue_predictor_api.post('/v1/revenuebatch')
-def predict_revenue_batch():
-    try:
-        file = request.files['file']
-        input_data = pd.read_csv(file)
-
-        predicted_logs = model.predict(input_data).tolist()
-        predicted_revenues = [round(float(np.exp(log)), 2) for log in predicted_logs]
-
-        # Fix: create proper IDs
-        ids = (input_data['Product_Id'].astype(str) + "_" + input_data['Store_Id'].astype(str)).tolist()
-        output_dict = dict(zip(ids, predicted_revenues))
-
-        return jsonify(output_dict)
-
-    except Exception as e:
-        return jsonify({"error": str(e)}), 500
-
-
 if __name__ == '__main__':
     sales_revenue_predictor_api.run(host="0.0.0.0", port=8000, debug=True)