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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
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
def plot_test_data(df_test_value):
fig, ax = plt.subplots(figsize=(12, 6))
df_test_value.plot(legend=False, ax=ax)
ax.set_xlabel("Time")
ax.set_ylabel("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")
# 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 > 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]):
anomalous_data_indices.append(data_idx)
df_subset = data.iloc[anomalous_data_indices]
fig, ax = plt.subplots(figsize=(12, 6))
data.plot(legend=False, ax=ax)
df_subset.plot(legend=False, ax=ax, color="r")
ax.set_xlabel("Time")
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")
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
outputs = gr.outputs.Image()
iface = gr.Interface(
fn=master,
inputs=gr.inputs.File(label="CSV File"),
outputs=outputs,
examples=["art_daily_jumpsup.csv"],
title="Timeseries Anomaly Detection Using an Autoencoder",
description="Anomaly detection of timeseries data."
)
iface.launch()
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