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 PIL

import gradio as gr

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])])
   return my_transforms(image).unsqueeze(0).to(device)

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[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'))
  
gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(128,128)), outputs=gr.inputs.Image(), examples=['color_157.jpg','color_158.jpg']).launch(share=False)