app.py

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

#repo_id = "Ignaciobfp/segmentacion-dron-marras"
#learner = from_pretrained_fastai(repo_id)

device = torch.device("cpu") 
#model = learner.model
model = torch.jit.load("pr3.pth")
model = model.cpu()

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_pil = PIL.Image.fromarray(img, 'RGB')
    image = transforms.Resize((400,400))(img_pil)
    tensor = transform_image(image=image)
    model.to(device)
    with torch.no_grad():
        outputs = model(tensor)
    outputs = torch.argmax(outputs,1)
    mask = np.array(outputs.cpu())
    mask[mask==1]=255
    mask=np.reshape(mask,(400,400))
    return Image.fromarray(mask.astype('uint8'))
    
# Creamos la interfaz y la lanzamos.
gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(400, 400)), outputs=gr.outputs.Image(type="pil"), 
             examples=['examplesB/color_180.jpg', 'examplesB/color_179.jpg', 'examplesB/color_156.jpg', 'examplesB/color_155.jpg', 'examplesB/color_154.jpg']).launch(share=False)