Upload folder using huggingface_hub

app.py

@@ -65,11 +65,12 @@ def predict_sales_forecast_batch():
 
     # Read the CSV file into a Pandas DataFrame
     input_data = pd.read_csv(file)
+    print(batch_data.loc[0])
 
     # Make predictions for all properties in the DataFrame (get log_prices)
-    predicted_sales = model.predict(input_data.drop('Product_Id', axis=1)).tolist()
-
+    predicted_sales = model.predict(input_data).tolist()
 
+    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
     output_dict = dict(zip(product_ids, predicted_sales))  # Use actual prices
@@ -79,4 +80,4 @@ def predict_sales_forecast_batch():
 
 # Run the Flask app in debug mode
 if __name__ == '__main__':
-    app.run(debug=True)
+    superkart_product_sales_prediction_api.run(debug=True)