File size: 2,301 Bytes
ad86461 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 | 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, sep='\t')
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()
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