from fastai.basics import *
from fastai.vision import models
from fastai.vision.all import *
from fastai.metrics import *
from fastai.data.all import *
from fastai.callback import *
from pathlib import Path
import random
import torchvision.transforms as transforms
import gradio as gr
import PIL

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
    return my_transforms(image_aux).unsqueeze(0).to(device)

def predict(img):
    img = PILImage.create(img)
    
    image = transforms.Resize((480,640))(img)
    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==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=predict, inputs=["image"], outputs=["image"],
             examples=['color_154.jpg','color_186.jpg']).launch(share=True)