upload app.py

app.py

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+import gradio as gr
+from huggingface_hub import from_pretrained_keras
+import pandas as pd
+import numpy as np
+import json
+
+f = open('scaler.json')
+scaler = json.load(f)
+
+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()
+    df_test_value.plot(legend=False, ax=ax)
+    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("remeajayi/timeseries-anomaly-detection")
+
+    # Get test MAE loss.
+    x_test_pred = model.predict(x_test)
+    test_mae_loss = np.mean(np.abs(x_test_pred - x_test), axis=1)
+    test_mae_loss = test_mae_loss.reshape((-1))
+
+    # Detect all the samples which are anomalies.
+    anomalies = test_mae_loss > 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]):
+            anomalous_data_indices.append(data_idx)
+    df_subset = data.iloc[anomalous_data_indices]
+    fig, ax = plt.subplots()
+    data.plot(legend=False, ax=ax)
+    df_subset.plot(legend=False, ax=ax, color="r")
+    return fig
+                                      
+def master(file):
+    # read file
+    data = pd.read_csv(file, parse_dates=True, index_col="timestamp")
+    df_test_value = normalize_data(data)
+    # plot input test data
+    plot1 = plot_test_data(df_test_value)
+    # predict
+    anomalies = get_anomalies(df_test_value)
+    #plot anomalous data points
+    plot2 = plot_anomalies(df_test_value, data, anomalies)
+    return plot2
+
+iface = gr.Interface(master,
+gr.inputs.File(label="csv file"),
+outputs=['plot'], 
+examples=["art_daily_jumpsup.csv"], title="Anomaly detection of timeseries data",
+description = "Anomaly detection of timeseries data.",
+        article = "Author: <a href=\"https://www.linkedin.com/in/olohireme-ajayi/\">Reme Ajayi</a>"
+)
+
+
+iface.launch()