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 PIL
import torchvision.transforms as transforms

import gradio as gr


# Cargamos el learner
#learn = load_learner('export.pkl')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu") 
model = torch.jit.load("unet.pth")
model = model.cpu()
model.eval()

# Definimos las etiquetas de nuestro modelo
#labels = learn.dls.vocab

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):
    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 #grape
    mask[mask==1]=150 #leaves
    mask[mask==2]=76 #pole
    mask[mask==2]=74 #pole
    mask[mask==3]=29 #wood
    mask[mask==3]=25 #wood

    mask=np.reshape(mask,(480,640))
    
    return Image.fromarray(mask.astype('uint8'))
    #pred,pred_idx,probs = learn.predict(img)
    #return {labels[i]: float(probs[i]) for i in range(len(labels))}
    
    
# Creamos la interfaz y la lanzamos. 
gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(128, 128)), outputs=gr.outputs.Image(),examples=['color_154.jpg','color_155.jpg']).launch(share=False)