Update app.py

app.py

@@ -1,4 +1,5 @@
 import gradio as gr
+from huggingface_hub import from_pretrained_keras
 import pandas as pd
 import numpy as np
 import json
@@ -21,7 +22,6 @@ def normalize_data(data):
     df_test_value = (data - scaler["mean"]) / scaler["std"]
     return df_test_value
 
-
 def plot_test_data(df_test_value):
     fig, ax = plt.subplots(figsize=(12, 6))
     df_test_value.plot(legend=False, ax=ax)
@@ -30,13 +30,10 @@ def plot_test_data(df_test_value):
     ax.set_title("Input Test Data")
     return fig
 
-
 def get_anomalies(df_test_value):
     # Create sequences from test values.
     x_test = create_sequences(df_test_value.values)
-
-    # Load the pre-trained model.
-    model = load_pretrained_model()
+    model = from_pretrained_keras("keras-io/timeseries-anomaly-detection")
 
     # Get test MAE loss.
     x_test_pred = model.predict(x_test)
@@ -47,12 +44,11 @@ def get_anomalies(df_test_value):
     anomalies = test_mae_loss > scaler["threshold"]
     return anomalies
 
-
 def plot_anomalies(df_test_value, data, anomalies):
     # data i is an anomaly if samples [(i - timesteps + 1) to (i)] are anomalies
     anomalous_data_indices = []
     for data_idx in range(TIME_STEPS - 1, len(df_test_value) - TIME_STEPS + 1):
-        if np.all(anomalies[data_idx -         TIME_STEPS + 1 : data_idx]):
+        if np.all(anomalies[data_idx - TIME_STEPS + 1 : data_idx]):
             anomalous_data_indices.append(data_idx)
     df_subset = data.iloc[anomalous_data_indices]
     fig, ax = plt.subplots(figsize=(12, 6))
@@ -62,24 +58,18 @@ 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
+    # read file
     data = pd.read_csv(file, parse_dates=True, index_col="timestamp")
     df_test_value = normalize_data(data)
-    
-    # Plot input test data
+    # plot input test data
     plot1 = plot_test_data(df_test_value)
-    
-    # Predict anomalies
+    # predict
     anomalies = get_anomalies(df_test_value)
-    
-    # Plot anomalous data points
+    #plot anomalous data points
     plot2 = plot_anomalies(df_test_value, data, anomalies)
-    
-    return plot1, plot2
-
+    return plot2
 
 outputs = gr.outputs.Image()
 
@@ -92,5 +82,4 @@ iface = gr.Interface(
     description="Anomaly detection of timeseries data."
 )
 
-iface.launch()
-
+iface.launch()