app.py

from fastai.vision.all import *
import gradio as gr
import torchvision.transforms as transforms

# Cargamos el learner
learn = load_learner('best_model.pkl')

# Definimos las etiquetas de nuestro modelo
labels = learn.dls.vocab

def transform_image(image, device):
    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)

def mask_to_img(mask):
  mask[mask == 1] = 255 # grape
  mask[mask == 2] = 150 # leaves
  mask[mask == 3] = 74 # pole
  mask[mask == 4] = 25 # wood
  mask=np.reshape(mask,(480,640))

  return mask

# Definimos una función que se encarga de llevar a cabo las predicciones
def predict(img):
  device = torch.device("cuda" if torch.cuda.is_available() else "cpu") 
  model = learn.cpu()
  model.eval()

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

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

  outputs = torch.argmax(outputs,1)
  
  mask = np.array(outputs.cpu())
  mask = mask_to_img(mask)

  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.Image(shape=(128,128)),examples=['1002_5866_6582.jpg','1038_31199_2068.jpg']).launch(share=False)