app.py

import gradio as gr
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.ensemble import IsolationForest
from datetime import datetime

def detect_anomalies(text, contamination=0.1):
    lines = text.strip().split("\n")
    data = []
    for line in lines:
        parts = line.strip().split(",")
        if len(parts) == 2:
            try:
                date = datetime.strptime(parts[0], "%Y-%m-%d")
                value = float(parts[1])
                data.append((date, value))
            except (ValueError, IndexError):
                print(f"Ignorando linha inválida: {line}")

    index = [item[0] for item in data]
    values = [item[1] for item in data]
    series = pd.Series(values, index=index)
    data = pd.DataFrame({"Data": series.index, "Valor": series.values})

    model = IsolationForest(contamination=contamination)
    model.fit(data[["Valor"]])

    anomalies = model.predict(data[["Valor"]])
    anomaly_series = pd.Series(anomalies, index=data["Data"]).astype(bool)

    fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(8, 8))
    ax1.plot(series.index, series.values)
    ax1.set_xlabel('Data')
    ax1.set_ylabel('Valor')
    ax1.set_title('Série Temporal de Entrada')

    ax2.plot(anomaly_series.index, anomaly_series.values)
    ax2.set_xlabel('Data')
    ax2.set_ylabel('Anomalia')
    ax2.set_title('Pontos de Anomalia')

    plt.tight_layout()
    plt.show()

    return anomaly_series.to_json()

data_input = gr.inputs.Textbox(label="Série Temporal (data, valor)", lines=10)
contamination_input = gr.inputs.Slider(
    minimum=0.01, maximum=0.5, step=0.01, default=0.1, label="Taxa de Contaminação"
)
label_output = gr.outputs.Textbox(label="Pontos de Anomalia (JSON)")

iface = gr.Interface(
    fn=detect_anomalies,
    inputs=[data_input, contamination_input],
    outputs=label_output,
    title="Detecção de Anomalias em Série Temporal",
    description="Insira uma série temporal no formato 'data, valor' separados por linha e ajuste a taxa de contaminação para encontrar os pontos de anomalia.",
    examples=[["2022-01-01, 10.2\n2022-01-02, 9.8\n2022-01-03, 9.5"]],
    examples_per_page=1
)

iface.launch()