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import gradio as gr
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
def detect_anomalies(series, window_size=7, threshold=2.5):
# Calculate the rolling mean and standard deviation using the specified window size
rolling_mean = series.rolling(window=window_size, center=True).mean()
rolling_std = series.rolling(window=window_size, center=True).std()
# Calculate the z-score for each data point
z_scores = np.abs((series - rolling_mean) / rolling_std)
# Detect anomalies based on the threshold
anomalies = series[z_scores > threshold]
return anomalies.index
def plot_anomalies(series, anomalies):
plt.figure(figsize=(12, 6))
plt.plot(series, label='Original Series')
plt.scatter(anomalies, series[anomalies], color='red', label='Anomalies')
plt.legend()
plt.title('Time Series with Anomalies')
plt.xlabel('Date')
plt.ylabel('Value')
plt.show()
# Função para o Gradio
def anomaly_detection_app(time_series_txt, window_size=7, threshold=2.5):
# Carregar a série temporal a partir do arquivo de texto
df = pd.read_csv(time_series_txt, header=None, names=["Date", "Value"])
df['Date'] = pd.to_datetime(df['Date'])
df = df.set_index('Date')
# Detectar anomalias na série temporal usando a média móvel
anomalies = detect_anomalies(df['Value'], window_size=window_size, threshold=threshold)
# Plotar a série temporal com as anomalias destacadas
plot_anomalies(df['Value'], anomalies)
return anomalies
# Interface do Gradio
iface = gr.Interface(
fn=anomaly_detection_app,
inputs=[
gr.inputs.File(label="Carregar série temporal (TXT)"),
gr.inputs.Number(default=7, label="Tamanho da janela da média móvel"),
gr.inputs.Number(default=2.5, label="Threshold para anomalias (em desvios-padrão)")
],
outputs="text",
title="Análise de Anomalias em Séries Temporais",
description="Este aplicativo detecta anomalias em uma série temporal carregada através de um arquivo de texto (TXT) usando a média móvel.",
live=True
)
if __name__ == "__main__":
iface.launch()
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