Update app.py

app.py

@@ -163,7 +163,6 @@ def full_forecast_pipeline(file_obj, time_col, target_col, forecast_horizon, fin
     """
     try:
         data = load_data(file_obj)
-        print("Column Names:", data.columns)
         if not isinstance(data, pd.DataFrame):
             return "Error loading data. Please check the file format and content.", None, None, None
 
@@ -171,10 +170,7 @@ def full_forecast_pipeline(file_obj, time_col, target_col, forecast_horizon, fin
             return "Error: Timestamp column or Value column not found in the data.", None, None, None
 
         # Convert time column to datetime
-        try:
-            data[time_col] = pd.to_datetime(data[time_col], format='%Y-%m')
-        except ValueError as e:
-            return f"Error converting time column: {e}", None, None, None
+        data[time_col] = pd.to_datetime(data[time_col])
 
         # Sort the DataFrame by the time column
         data = data.sort_values(by=time_col)
@@ -196,10 +192,7 @@ def full_forecast_pipeline(file_obj, time_col, target_col, forecast_horizon, fin
         data = data.set_index(time_col)
 
         # Resample the data
-        try:
-            data = data.resample("M").mean()
-        except ValueError as e:
-            return f"Error resampling data: {e}", None, None, None
+        data = data.resample(resample_freq).mean()
         data.reset_index(inplace=True)
 
         forecast_result = forecast_nixtla(data, forecast_horizon, finetune_steps, freq, time_col, target_col)
@@ -241,17 +234,6 @@ def get_column_names(file_obj):
         print(f"Error in get_column_names: {e}")
         return []
 
-def update_dropdown_choices(file_obj):
-    """
-    Updates the dropdown choices based on the uploaded file.
-    """
-    try:
-        columns = get_column_names(file_obj)
-        return gr.Dropdown.update(choices=columns), gr.Dropdown.update(choices=columns)
-    except Exception as e:
-        logger.error(f"Error updating dropdown choices: {e}", exc_info=True)
-        return gr.Dropdown.update(choices=[]), gr.Dropdown.update(choices=[])
-
 def create_interface():
     with gr.Blocks() as iface:
         gr.Markdown("""
@@ -264,18 +246,14 @@ def create_interface():
             time_col_dropdown = gr.Dropdown(choices=[], label="Select Timestamp Column")
             target_col_dropdown = gr.Dropdown(choices=[], label="Select Value Column")
 
-        # Remove the file_input.upload event
-        # file_input.upload(
-        #     update_dropdown_choices,
-        #     [file_input],
-        #     [time_col_dropdown, target_col_dropdown]
-        # )
+        def update_dropdown_choices(file_obj):
+            columns = get_column_names(file_obj)
+            return gr.update(choices=columns), gr.update(choices=columns)
 
-        update_dropdown_button = gr.Button("Update Dropdowns")
-        update_dropdown_button.click(
+        file_input.upload(
             update_dropdown_choices,
-            inputs=[file_input],
-            outputs=[time_col_dropdown, target_col_dropdown]
+            [file_input],
+            [time_col_dropdown, target_col_dropdown]
         )
 
         with gr.Row():
@@ -299,11 +277,11 @@ def create_interface():
         # Button to trigger the full pipeline
         btn = gr.Button("Generate Forecast")
         btn.click(
-            fn=lambda *args: (update_dropdown_choices(file_input.value), full_forecast_pipeline(*args)),  # Call update_dropdown_choices first
+            fn=full_forecast_pipeline,
             inputs=[file_input, time_col_dropdown, target_col_dropdown, forecast_horizon_input, finetune_steps_input, freq_dropdown, start_date_input, end_date_input, start_time_input, end_time_input, resample_freq_dropdown],
             outputs=[output_html, output_plot, download_button, error_output]
         )
     return iface
 
 iface = create_interface()
-iface.launch()
+iface.launch()