Update app.py

app.py

@@ -1,5 +1,4 @@
 import gradio as gr
-from huggingface_hub import from_pretrained_keras
 import pandas as pd
 import numpy as np
 import json
@@ -22,6 +21,7 @@ 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)
@@ -30,10 +30,13 @@ 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)
-    model = from_pretrained_keras("keras-io/timeseries-anomaly-detection")
+
+    # Load the pre-trained model.
+    model = load_pretrained_model()
 
     # Get test MAE loss.
     x_test_pred = model.predict(x_test)
@@ -44,11 +47,12 @@ 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))
@@ -58,18 +62,24 @@ 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
+    
+    # Predict anomalies
     anomalies = get_anomalies(df_test_value)
-    #plot anomalous data points
+    
+    # Plot anomalous data points
     plot2 = plot_anomalies(df_test_value, data, anomalies)
-    return plot2
+    
+    return plot1, plot2
+
 
 outputs = gr.outputs.Image()
 
@@ -83,3 +93,4 @@ iface = gr.Interface(
 )
 
 iface.launch()
+