Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+import statsmodels.api as sm
+import numpy as np
+
+df = pd.read_excel('MOD_VC.xlsx', 'DF')
+
+# Separar as variáveis independentes (X) e dependente (y)
+X = df[['AREA', 'TEST', 'PAV', 'POS', 'RB']]
+y = df['VU']
+
+X_with_constant = sm.add_constant(X)
+model = sm.OLS(y, X_with_constant)
+results = model.fit()
+
+def predict(input_df):
+    # Processamento da planilha de input
+    input_df[['AREA', 'TEST']] = np.log(input_df[['AREA', 'TEST']])
+    input_df['RB'] = 1/input_df['RB']
+
+    # Preparar dados para predição
+    X_new = np.array(input_df)
+    X_new_with_constant = np.insert(X_new, 0, 1, axis=1)
+
+    # Fazer previsões
+    y_pred = results.predict(X_new_with_constant)
+    ci = results.get_prediction(X_new_with_constant).conf_int()
+    inter_conf = np.exp(ci)
+
+    # Adicionar previsões e intervalos de confiança à planilha
+    input_df['Predicted'] = np.exp(y_pred)
+    input_df['CI Lower'] = inter_conf[:, 0]
+    input_df['CI Upper'] = inter_conf[:, 1]
+
+    return input_df
+
+# Interface Gradio
+iface = gr.Interface(fn=predict, inputs="dataframe", outputs="dataframe")
+iface.launch()