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app.py

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+import gradio as gr
+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.linear_model import LogisticRegression
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import roc_auc_score, roc_curve, precision_score, recall_score, f1_score, confusion_matrix
+from imblearn.over_sampling import SMOTE
+import matplotlib.pyplot as plt
+
+def process_file(file):
+    df = pd.read_csv(file.name)
+
+    return df.head().to_html()
+
+def train_model(file, target, features, model_choice):
+    df = pd.read_csv(file.name)
+
+    X = df[features]
+    y = df[target]
+
+    sm = SMOTE()
+    X_res, y_res = sm.fit_resample(X, y)
+
+    X_train, X_test, y_train, y_test = train_test_split(X_res, y_res, test_size=0.2)
+
+    if model_choice == "Regressão Logística":
+        scaler = StandardScaler()
+        X_train = scaler.fit_transform(X_train)
+        X_test = scaler.transform(X_test)
+        model = LogisticRegression()
+    else:
+        model = RandomForestClassifier()
+
+    model.fit(X_train, y_train)
+    y_pred = model.predict(X_test)
+    y_proba = model.predict_proba(X_test)[:, 1]
+
+    auc = roc_auc_score(y_test, y_proba)
+    prec = precision_score(y_test, y_pred)
+    rec = recall_score(y_test, y_pred)
+    f1 = f1_score(y_test, y_pred)
+    cm = confusion_matrix(y_test, y_pred)
+
+    return f"""
+    <b>AUC:</b> {auc}<br>
+    <b>Precisão:</b> {prec}<br>
+    <b>Recall:</b> {rec}<br>
+    <b>F1:</b> {f1}<br>
+    <b>Matriz de confusão:</b><br>{cm}
+    """
+
+with gr.Blocks() as demo:
+    gr.Markdown("# 📊 Dashboard da Tarefa 4 — Bônus")
+    file = gr.File(label="Envie seu CSV")
+    
+    show_head = gr.Button("Mostrar primeiras linhas")
+    head_output = gr.HTML()
+
+    with gr.Row():
+        target = gr.Textbox(label="Variável alvo (Y)")
+        features = gr.Textbox(label="Features (X) separadas por vírgula")
+
+    model_choice = gr.Dropdown(["Regressão Logística", "Random Forest"], label="Modelo")
+
+    train_button = gr.Button("Treinar Modelo")
+    train_output = gr.HTML()
+
+    show_head.click(process_file, inputs=file, outputs=head_output)
+    train_button.click(train_model, inputs=[file, target, features, model_choice], outputs=train_output)
+
+demo.launch()

readme.py

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+# 📊 Dashboard Interativo – Tarefa 4 (SIEP)
+Este é o dashboard para o bônus de inovação, permitindo:
+
+- Upload da base
+- Seleção de variáveis
+- Treinamento de modelo (Logística ou Random Forest)
+- Exibição de métricas (AUC, Precisão, Recall, F1)
+- Matriz de confusão
+
+Criado com **Gradio**, totalmente compatível com Hugging Face Spaces.

requisitos.py

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+gradio
+pandas
+numpy
+scikit-learn
+imbalanced-learn
+matplotlib
+