Update app.py

app.py

@@ -15,7 +15,7 @@ def create_sequences(values, time_steps=TIME_STEPS):
     output = []
     for i in range(len(values) - time_steps + 1):
         output.append(values[i : (i + time_steps)])
-    return np.array(output)
+    return np.stack(output)
 
 
 def normalize_data(data):
@@ -58,7 +58,7 @@ def plot_anomalies(df_test_value, data, anomalies):
     ax.set_ylabel("Value")
     ax.set_title("Anomalous Data Points")
     return fig
-
+                                      
 def master(file):
     # read file
     data = pd.read_csv(file, parse_dates=True, index_col="timestamp")
@@ -67,7 +67,7 @@ def master(file):
     plot1 = plot_test_data(df_test_value)
     # predict
     anomalies = get_anomalies(df_test_value)
-    # plot anomalous data points
+    #plot anomalous data points
     plot2 = plot_anomalies(df_test_value, data, anomalies)
     return plot2
 
@@ -77,7 +77,7 @@ iface = gr.Interface(
     fn=master,
     inputs=gr.inputs.File(label="CSV File"),
     outputs=outputs,
-    examples=["art_daily_jumpsup.csv"],
+    examples=["example_1.csv", "example_2.csv"],
     title="Timeseries Anomaly Detection Using an Autoencoder",
     description="Anomaly detection of timeseries data."
 )