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app.py

@@ -38,9 +38,9 @@ def predict_sales():
     Returns predicted sales value.
     """
     data = request.get_json()
-
-    # Extract features for prediction (replace keys with your exact feature names)
-    features = {
+    try:
+      # Extract features for prediction (replace keys with your exact feature names)
+      features = {
         'Store_Establishment_Year': data['Store_Establishment_Year'],
         'Product_MRP': data['Product_MRP'],
         'Product_Weight': data['Product_Weight'],
@@ -50,21 +50,21 @@ def predict_sales():
         'Store_Location_City_Type': data['Store_Location_City_Type'],
         'Store_Size': data['Store_Size'],
         'Product_Allocated_Area': data['Product_Allocated_Area'],
-        'Product_id': data['Product_id'],
+        'Product_Id': data['Product_Id'],
         'Store_Type': data['Store_Type'],
         # Add or remove features per your model input
-    }
-
-    # Convert to DataFrame for model input
-    input_df = pd.DataFrame([features])
-
-    # Predict sales
-    predicted_sales = model.predict(input_df)[0]
-
-    # Convert to float and round for JSON serialization
-    predicted_sales = round(float(predicted_sales), 2)
-
-    return jsonify({'Predicted_Sales': predicted_sales})
+      }
+      # Convert to DataFrame for model input
+      input_df = pd.DataFrame([features])
+      # Predict sales
+      predicted_sales = model.predict(input_df)[0]
+      # Convert to float and round for JSON serialization
+      predicted_sales = round(float(predicted_sales), 2)
+      
+      return jsonify({'Predicted_Sales': predicted_sales})
+    except Exception as e:
+        return jsonify({'error': str(e)}), 400
+    
 
 
 # Batch prediction endpoint (POST)