hot fix

app.py

@@ -60,12 +60,11 @@ with gr.Blocks(title="Time series anomaly detection with Chronos2") as demo:
             - "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.
         2. Answer the question about the timestamp in your data to help the model understand the temporal structure of your data.
-            - if present, the timestamp column should be in a recognizable datetime format (e.g., "YYYY-MM-DD HH:MM:SS").
             - if present, you will need to specify the column name of the timestamp in your data.
-            - If missing, we will add a default timestamp ourselves.
+            - 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.
         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.
+        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. 
 
         ## Note
         - The Chronos2 pipeline is designed to handle multivariate time series data, so you can upload datasets with multiple columns of values.
@@ -105,4 +104,4 @@ with gr.Blocks(title="Time series anomaly detection with Chronos2") as demo:
         )
 
 
-demo.launch()
+demo.launch(share=True)

utils.py

@@ -46,7 +46,7 @@ def validateData(file, timestamp_column:str=None):
 
     assert df.isna().sum().sum() == 0, "Missing values detected in the uploaded data. Please ensure your CSV file does not contain any missing values (NaNs) for accurate anomaly detection."
     assert np.isfinite(df.select_dtypes(include=[np.number])).all().all(), "Non-finite values detected in the uploaded data. Please ensure your CSV file does not contain any non-finite values (e.g., inf, -inf) for accurate anomaly detection."
-    assert not (df.columns.dtype == 'object').any(), "Non-numeric value columns detected. Please ensure all value columns in your CSV file contain numeric data for accurate anomaly detection."
+    assert 'object' not in df.columns.dtype, "Non-numeric value columns detected. Please ensure all value columns in your CSV file contain numeric data for accurate anomaly detection."