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| 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() |