app.py

from huggingface_hub import from_pretrained_fastai
import gradio as gr
from fastai.vision.all import *

repo_id = "ancebuc/grapes-segmentation"

learner = from_pretrained_fastai(repo_id)
labels = learner.dls.vocab

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = torch.jit.load("unet.pth")
model = model.cpu()
model.eval()

import torchvision.transforms as transforms
def transform_image(image):
    my_transforms = transforms.Compose([transforms.ToTensor(),
                                        transforms.Normalize(
                                            [0.485, 0.456, 0.406],
                                            [0.229, 0.224, 0.225])])
    image_aux = image
    return my_transforms(image_aux).unsqueeze(0).to(device)

# Definimos una función que se encarga de llevar a cabo las predicciones
def predict(img):
    img = PILImage.create(img)

    image = transforms.Resize((480,640))(img)
    tensor = transform_image(image=image)

    with torch.no_grad():
        outputs = model(tensor)

    outputs = torch.argmax(outputs,1)

    mask = np.array(outputs.cpu())
    mask = np.reshape(mask,(480,640))

    # Añadimos una dimesionalidad para colocar color
    mask = np.expand_dims(mask, axis=2)

    # Y añadimos los tres canales
    mask = np.repeat(mask, 3, axis=2)

    # Creamos las máscaras
    uvas = np.all(mask == [1, 1, 1], axis=2)
    hojas = np.all(mask == [2, 2, 2], axis=2)
    poste = np.all(mask == [3, 3, 3], axis=2)
    madera = np.all(mask == [4, 4, 4], axis=2)
    
    # Uvas
    mask[uvas] = [255, 255, 255]
    
    # Hojas
    mask[hojas] = [0, 255, 0]
    
    # Poste
    mask[poste] = [0, 0, 255]
    
    # Madera
    mask[madera] = [255, 0, 0]

    return Image.fromarray(mask.astype('uint8'))
    
# Creamos la interfaz y la lanzamos. 
gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(128, 128)), outputs=gr.outputs.inputs.Image(shape=(128, 128)),examples=['color_158.jpg','color_157.jpg']).launch(share=False)