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

from pathlib import Path
import PIL
import torchvision.transforms as transforms



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

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
    
image = transforms.Resize((480,640))(Image.fromarray(image))
tensor = my_transforms(image_aux).unsqueeze(0).to(device)


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

outputs = torch.argmax(outputs,1)


mask = np.array(outputs.cpu())
mask[mask==0]=255
mask[mask==1]=150
mask[mask==2]=76
mask[mask==3]=25
mask[mask==4]=0

mask=np.reshape(mask,(480,640))
return Image.fromarray(mask.astype('uint8'))


    
# Creamos la interfaz y la lanzamos. 
gr.Interface(fn=transform_image, inputs=gr.inputs.Image(shape=(640, 480)), outputs=gr.outputs.Image(),examples=['color_188.jpg','color_189.jpg']).launch(share=False)