Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+import numpy as np
+from statsmodels.tsa.seasonal import STL
+from sklearn.ensemble import IsolationForest
+import matplotlib.pyplot as plt
+
+def detect_anomalies(series, seasonality_window=7, anomaly_threshold=0.05):
+    # Decompose series using Seasonal-Trend decomposition using LOESS (STL)
+    decomposition = STL(series, seasonal=seasonality_window).fit()
+    trend = decomposition.trend
+    seasonal = decomposition.seasonal
+    residual = decomposition.resid
+
+    # Combine trend and seasonal components to form detrended series
+    detrended_series = series - trend - seasonal
+
+    # Fit the Isolation Forest model to detect anomalies in the detrended series
+    model = IsolationForest(contamination=anomaly_threshold)
+    model.fit(detrended_series.values.reshape(-1, 1))
+
+    # Predict outliers in the detrended series
+    outlier_predictions = model.predict(detrended_series.values.reshape(-1, 1))
+    anomalies = detrended_series[outlier_predictions == -1]
+
+    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_csv):
+    # Carregar a série temporal a partir do arquivo CSV
+    df = pd.read_csv(time_series_csv)
+    df['Date'] = pd.to_datetime(df['Date'])
+    df = df.set_index('Date')
+
+    # Detectar anomalias na série temporal
+    anomalies = detect_anomalies(df['Value'])
+
+    # 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 (CSV)"),
+    outputs="text",
+    title="Análise de Anomalias em Séries Temporais",
+    description="Este aplicativo detecta anomalias em uma série temporal carregada através de um arquivo CSV.",
+    live=True
+)
+
+if __name__ == "__main__":
+    iface.launch()