update

app.py

@@ -3,10 +3,21 @@ from huggingface_hub import from_pretrained_keras
 import pandas as pd
 import numpy as np
 import json
+from matplotlib import pyplot as plt
 
 f = open('scaler.json')
 scaler = json.load(f)
 
+TIME_STEPS = 288
+
+# Generated training sequences for use in the model.
+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.stack(output)
+
+
 def normalize_data(data):
     df_test_value = (data - scaler["mean"]) / scaler["std"]
     return df_test_value
@@ -27,7 +38,7 @@ def get_anomalies(df_test_value):
     test_mae_loss = test_mae_loss.reshape((-1))
 
     # Detect all the samples which are anomalies.
-    anomalies = test_mae_loss > threshold
+    anomalies = test_mae_loss > scaler["threshold"]
     return anomalies
 
 def plot_anomalies(df_test_value, data, anomalies):
@@ -59,9 +70,8 @@ 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 = "Space by: <a href=\"https://www.linkedin.com/in/olohireme-ajayi/\">Reme Ajayi</a> /n Keras Example by <a href=\"https://github.com/pavithrasv/\"> Pavithra Vijay</a>"
+                article = "Space by: <a href=\"https://www.linkedin.com/in/olohireme-ajayi/\">Reme Ajayi</a> /n Keras Example by <a href=\"https://github.com/pavithrasv/\"> Pavithra Vijay</a>")
 
-)
 
 
 iface.launch()