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app.py

@@ -65,14 +65,17 @@ def predict_sales_forecast_batch():
 
     # Read the CSV file into a Pandas DataFrame
     input_data = pd.read_csv(file)
-    print(input_data.loc[0])
+    #print(input_data.loc[0])
 
-    # Make predictions for all properties in the DataFrame (get log_prices)
+    # Make predictions for all products in the DataFrame (get sales prices)
     predicted_sales = model.predict(input_data).tolist()
 
-    print(predicted_sales)
+    # Convert predicted_price to Python float
+    predicted_sales = [round(float(price), 2) for price in predicted_sales]
+
+    #print(predicted_sales)
     # Create a dictionary of predictions with property IDs as keys
-    product_ids = input_data['Product_Id'].tolist()  # Assuming 'Product_Id' is the property ID column
+    product_ids = input_data['Product_Id'].tolist()  # Assuming 'Product_Id' is the product_id ID column
     output_dict = dict(zip(product_ids, predicted_sales))  # Use actual prices
 
     # Return the predictions dictionary as a JSON response