app.py

from huggingface_hub import hf_hub_download
import gradio as gr
from fastai.vision.all import *
import torch
import torchvision.transforms as transforms
import numpy as np
from PIL import Image

repo_id = "hafsa101010/Practica3"

model_path = hf_hub_download(repo_id=repo_id, filename="unet.pth")

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = torch.jit.load(model_path, map_location=device)
model.eval()

def transform_image(image):
    image = image.resize((640, 480))  # Asegurar tamaño correcto
    my_transforms = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ])
    return my_transforms(image).unsqueeze(0).to(device)

def predict(img):
    tensor = transform_image(img)
    
    with torch.no_grad():
        outputs = model(tensor)
    
    outputs = torch.argmax(outputs, 1).cpu().numpy().squeeze()

    mask = np.zeros_like(outputs, dtype=np.uint8)
    mask[outputs == 1] = 255  # grape
    mask[outputs == 2] = 150  # leaves
    mask[outputs == 3] = 76   # pole
    mask[outputs == 4] = 74   # pole
    mask[outputs == 5] = 29   # wood
    mask[outputs == 6] = 25   # wood

    return Image.fromarray(mask)

gr.Interface(fn=predict, inputs=gr.Image(type="pil"), outputs=gr.Image(type="pil"), examples=["color_181.jpg", "color_155.jpg"]).launch()