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app.py

@@ -56,14 +56,14 @@ def predict_sales_forecast_batch():
     # Read the CSV file into a Pandas DataFrame
     input_data = pd.read_csv(file)
 
-    # Make predictions for all properties in the DataFrame (get log_prices)
-    predicted_log_prices = model.predict(input_data).tolist()
+    # Make predictions for all products in the DataFrame (get log_prices)
+    predicted_sales = model.predict(input_data).tolist()
 
     # Calculate actual prices
-    predicted_sales = [round(float(np.exp(log_price)), 2) for log_price in predicted_log_prices]
+    predicted_sales = [round(float(Product_Store_Sales_Total, 2)) for Product_Store_Sales_Total in predicted_sales]
 
-    # Create a dictionary of predictions with property IDs as keys
-    product_ids = input_data['Product_ID'].tolist()  # Assuming 'id' is the property ID column
+    # Create a dictionary of predictions with Product IDs as keys
+    product_ids = input_data['Product_ID'].tolist()  # Assuming 'id' is the Product ID column
     output_dict = dict(zip(product_ids, predicted_sales))  # Use actual prices
 
     # Return the predictions dictionary as a JSON response