Delete app.py

app.py

@@ -1,73 +0,0 @@
-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()