app.py

import gradio as gr
import torch
from huggingface_hub import hf_hub_download
from torchvision import transforms
from PIL import Image
import requests
import os

# URL del modelo en Hugging Face
model_url = "https://huggingface.co/macapa/blindness_clas/resolve/main/blindness_model.pth"
model_path = "best_model_resnet18.pth"


hf_hub_download(
    repo_id='macapa/blindness_clas',
    filename='best_model_resnet18.pth',
    local_dir='.'
)


# Cargar el modelo PyTorch
model = torch.load(model_path, map_location=torch.device('cpu'))
# model.eval()

# Definir las transformaciones de la imagen
preprocess = transforms.Compose([
    transforms.Resize((256, 256)),
    transforms.ToTensor(),
])

# Definir las etiquetas de clasificación
labels = ["No Blindness", "Mild", "Moderate", "Severe", "Proliferative"]

# Función para predecir la clase de ceguera
def classify_image(img):
    img = preprocess(img).unsqueeze(0)
    with torch.no_grad():
        outputs = model(img)
        _, predicted = torch.max(outputs, 1)
        return labels[predicted.item()]

# Definir la interfaz de Gradio
interface = gr.Interface(
    fn=classify_image,
    inputs=gr.Image(label="Carga una imagen aquí"),
    outputs=gr.Label(num_top_classes=1),
    title="Blindness Classification",
    description="Classify the severity of blindness from retinal images."
)


# Ejecutar la aplicación
interface.launch(share=True)