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app.py

@@ -51,7 +51,7 @@ def predict_sales_price():
     predicted_sales = model.predict(input_data)[0]
 
     # Convert predicted_price to Python float
-    predicted_sales = round(float(predicted_sales), 2)
+    # predicted_sales = round(float(predicted_sales), 2)
 
     # Return the actual price
     return jsonify({'Predicted Price (in dollars)': predicted_sales})
@@ -75,7 +75,7 @@ def predict_superkart_sales_batch():
     predicted_sales = model.predict(input_data).tolist()
 
     # Calculate actual prices
-    predicted_sales = [round(float(sales), 2) for sales in predicted_sales]
+    # predicted_sales = [round(float(sales), 2) for sales in predicted_sales]
 
     # Create a dictionary of predictions with product IDs as keys
     product_ids = input_data['id'].tolist()  # Assuming 'id' is the product ID column