from huggingface_hub import from_pretrained_fastai
import gradio as gr
import numpy as np
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 *
import torchvision.transforms as transforms

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

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):
    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==1] = 255
    mask[mask==2] = 150
    mask[mask==3] = 76
    mask[mask==4] = 29
    mask=np.reshape(mask,(480,640))
    return Image.fromarray(mask.astype('uint8'))
    
# Creamos la interfaz y la lanzamos. 
gr.Interface(fn=predict, inputs=gr.inputs.Image(type='pil'), outputs=gr.outputs.Image(type='numpy'), examples=['color_154.jpg','color_155.jpg']).launch(share=False)