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

model = load_learner('export (2).pkl')

def transform_image(image):
   my_transforms = transforms.Compose([transforms.ToTensor(), transforms.Normalzie([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)
   device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
   image = transforms.Resize((480,640))(img)
   tensor = transform_image(image=image)
   model.to(device)
   with torch.no_grad():
      outputs = model(tensor)
   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))
   Image.fromarray(mask.astype('uint8')) 
   
gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(128,128)), outputs=gr.inputs.Image(shape=(128,128)), examples=['color_157.jpg','color_158.jpg']).launch(share=False)