added yield

app.py

@@ -13,9 +13,16 @@ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 OUT_PATH ="./savedPredictions/results.csv"
 
 def dataProcessing(file, timestamp_column:str=None):
+    global chronos2
     if os.path.exists(OUT_PATH):
         os.remove(OUT_PATH)
-    global chronos2
+
+    yield (
+        gr.update(visible=True, value="⏳ Processing file, please wait..."),  # processing_msg
+        gr.update(visible=False),   # plot_output
+        gr.update(visible=False),   # download_output
+        gr.update(visible=False, value="")  # errorHandler
+    )
 
     try:
         validateData(file, timestamp_column)
@@ -29,12 +36,12 @@ def dataProcessing(file, timestamp_column:str=None):
         df.to_csv(OUT_PATH, index=False)
 
         if fig is not None:
-            return fig, OUT_PATH, gr.update(visible=False, value="")
+            yield (gr.update(visible=False), fig, OUT_PATH, gr.update(visible=False, value=""))
 
-        return gr.update(visible=False), OUT_PATH, gr.update(visible=False, value="")
+        yield gr.update(visible=False),gr.update(visible=False), OUT_PATH, gr.update(visible=False, value="")
 
     except Exception as e:
-        return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True, value=f"Error: {e}")
+        yield gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=True, value=f"Error: {e}")
 
 
 if os.path.exists(OUT_PATH):
@@ -55,13 +62,13 @@ with gr.Blocks(title="Time series anomaly detection with Chronos2") as demo:
         and visualize the detected anomalies using the Chronos2 pipeline.
 
         ## Instructions
-        1. Click on the "Upload Time Series Data" button to upload your time series data in CSV format. The CSV file should have as columns only: 
-            - "timestamp": the timestamp column of your data (e.g., "2023-01-01 00:00:00"). It is optional, if missing we will add a default timestamp ourselves.
-            - "values": the columns containing the values of the time series data. They can be named as you wish. At least one column of values is required.
+        1. Click on the *Upload Time Series Data* button to upload your time series data in CSV format. The CSV file should have as columns only: 
+            - **"timestamp":** the timestamp column of your data (e.g., "2023-01-01 00:00:00"). It is optional. 
+            - **"values":** the columns containing the values of the time series data. They can be named as you wish. At least one column of values is required.
         2. Answer the question about the timestamp in your data to help the model understand the temporal structure of your data.
             - if present, you will need to specify the column name of the timestamp in your data.
             - Otherwise, no need to do anything, just mark No.
-        3. Click on the "Detect Anomalies" button to run the Chronos2 pipeline and visualize the detected anomalies.
+        3. Click on the *Detect Anomalies* button to run the Chronos2 pipeline and visualize the detected anomalies.
         4. If the number of series is reasonably small, we will plot the original time series along with the detected anomalies. 
         5. We will provide a downloadable CSV file containing the original time series data along with an additional column indicating whether each point is an anomaly or not. We will label as 1 anomalies, as 0 normal points and as -1 the points for which we don't have a prediction because they are before the minimum length required by the model. 
 
@@ -98,18 +105,6 @@ with gr.Blocks(title="Time series anomaly detection with Chronos2") as demo:
 
             processing_msg = gr.Markdown("⏳ Processing file, please wait...", visible=False)
 
-            detect_button.click(
-                lambda : gr.update(visible=True),
-                inputs=None,
-                outputs=processing_msg
-            )
-
-            detect_button.click(
-                lambda : gr.update(visible=False, value=""),
-                inputs=None,
-                outputs=[plot_output, download_output, errorHandler]
-            )
-
             detect_button.click(
                 lambda file, timestamp_question, timestamp_column:
                     dataProcessing(
@@ -117,14 +112,7 @@ with gr.Blocks(title="Time series anomaly detection with Chronos2") as demo:
                         timestamp_column if timestamp_question == "Yes" else None
                     ),
                 inputs=[file_input, timestamp_question, timestamp_column_input],
-                outputs=[plot_output, download_output, errorHandler]
+                outputs=[processing_msg, plot_output, download_output, errorHandler]
             )
 
-            detect_button.click(
-                lambda : gr.update(visible=False),
-                inputs=None,
-                outputs=processing_msg
-            )
-
-
 demo.launch(share=True)