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()