app.py

import torch
from transformers import AutoModel
import torch.nn as nn
from PIL import Image
import numpy as np
import streamlit as st

# Definir o dispositivo
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# Carregar o modelo treinado do Hugging Face Hub
model = AutoModel.from_pretrained('dhhd255/EfficientNet_ParkinsonsPred')

# Mover o modelo para o dispositivo
model = model.to(device)

# Adicionar CSS personalizado para usar a fonte Inter, definir classes personalizadas para resultados de saída e estilos de rodapé
st.markdown("""
 <style>
 @import url('https://fonts.googleapis.com/css2?family=Inter&display=swap');
 body {
 font-family: 'Inter', sans-serif;
 }
 .result {
 font-size: 24px;
 font-weight: bold;
 }
 .healthy {
 color: #007E3F;
 }
 .parkinsons {
 color: #C30000;
 }
        .social-links {
            display: flex;
            text-decoration:none;
            justify-content: center;
        }
        .social-links a {
            text-decoration:none;
            padding: 0 10px;
        }
 </style>
""", unsafe_allow_html=True)

st.title("Predição de Doença de Parkinson")

uploaded_file = st.file_uploader("Envie seu desenho em espiral aqui", type=["png", "jpg", "jpeg"])
st.empty()
if uploaded_file is not None:
    col1, col2 = st.columns(2)

    # Carregar e redimensionar a imagem
    image_size = (224, 224)
    new_image = Image.open(uploaded_file).convert('RGB').resize(image_size)
    col1.image(new_image, width=255)
    new_image = np.array(new_image)
    new_image = torch.from_numpy(new_image).transpose(0, 2).float().unsqueeze(0)

    # Mover os dados para o dispositivo
    new_image = new_image.to(device)

    # Fazer previsões usando o modelo treinado
    with torch.no_grad():
        predictions = model(new_image)
        logits = predictions.last_hidden_state
        logits = logits.view(logits.shape[0], -1)
        num_classes = 2
        feature_reducer = nn.Linear(logits.shape[1], num_classes)

        logits = logits.to(device)
        feature_reducer = feature_reducer.to(device)

        logits = feature_reducer(logits)
        predicted_class = torch.argmax(logits, dim=1).item()
        confidence = torch.softmax(logits, dim=1)[0][predicted_class].item()
        
        # Traduzir as mensagens de saída
        if predicted_class == 0:
            col2.markdown('<span class="result parkinsons">Classe prevista: Parkinson</span>', unsafe_allow_html=True)
            col2.caption(f'{confidence*100:.0f}% de certeza')
        else:
            col2.markdown('<span class="result healthy">Classe prevista: Saudável</span>', unsafe_allow_html=True)
            col2.caption(f'{confidence*100:.0f}% de certeza')

uploaded_file = st.file_uploader("Envie seu desenho de onda aqui", type=["png", "jpg", "jpeg"])
st.divider()
st.markdown("""
    <div class="social-links">
        <a href="https://twitter.com/seu_twitter">Twitter</a>
        <a href="https://facebook.com/sua_pagina_facebook">Facebook</a>
        <a href="https://instagram.com/seu_instagram">Instagram</a>
    </div>
""", unsafe_allow_html=True)