import gradio as gr
import pandas as pd
import torch
import torch.nn.functional as F
from fastai.tabular.all import *
import pickle

with open('Kidney_stones.pkl', 'rb') as arquivo:
    modelo = pickle.load(arquivo)

def calc_preds(coeffs, indeps):
    layers,consts = coeffs
    n = len(layers)
    res = indeps
    for i,l in enumerate(layers):
        res = res@l + consts[i]
        if i!=n-1: res = F.relu(res)
    return torch.sigmoid(res)

def predict(Gravity, Ph, Osmo, Cond, Urea, Calc):
    input = torch.tensor([Gravity, Ph, Osmo, Cond, Urea, Calc], dtype=torch.float)
    pred = calc_preds(coeffs=modelo, indeps=input)
    return 'Kidney Stone' if pred.item() < 0.55 else 'No Kidney Stones'

interface = gr.Interface(
    fn=predict,
    inputs=["number", "number", "number", "number", "number", "number"],
    outputs="text",
    title="Verificador de Pedras nos Rins",
    description="Esse modelo é capaz de realizar uma análise com base nos dados da urina de uma pessoa e identificar se ela possui pedra nos rins",
)

interface.launch()