#app.py:
# from huggingface_hub import from_pretrained_fastai
import gradio as gr

from fastai.data.block import DataBlock

from fastai.data.transforms import get_image_files, FuncSplitter, Normalize
from fastai.layers import Mish
from fastai.losses import BaseLoss
from fastai.optimizer import ranger
from fastai.torch_core import tensor
from fastai.vision.augment import aug_transforms
from fastai.vision.core import PILImage, PILMask
from fastai.vision.data import ImageBlock, MaskBlock, imagenet_stats
from fastai.vision.learner import unet_learner
from PIL import Image
import numpy as np
from torch import nn
import torch
import torch.nn.functional as F


# # repo_id = "YOUR_USERNAME/YOUR_LEARNER_NAME"
# repo_id = "islasher/segm-grapes"
# repo_id='islasher/segm-grapes'


# # Definimos una función que se encarga de llevar a cabo las predicciones

# from fastai.learner import load_learner

# # Cargar el modelo y el tokenizador
# learn = load_learner(repo_id)
#learner = from_pretrained_fastai(repo_id)

from huggingface_hub import from_pretrained_fastai



import torchvision.transforms as transforms



class TargetMaskConvertTransform(ItemTransform):
    def __init__(self):
        pass
    def encodes(self, x):
        img,mask = x

        #Convert to array
        mask = np.array(mask)


        # Changes: (codes= array(['Background', 'Leaves', 'Wood', 'Pole', 'Grape'], dtype='<U10'))
        mask[mask==150]=1 #leaves

        mask[mask==76]=3 #pole
        mask[mask==74]=3 #pole

        mask[mask==29]=2 #wood
        mask[mask==25]=2 #wood

        mask[mask==255]=4 #grape

        mask[mask==0]=0
        # Back to PILMask
        mask = PILMask.create(mask)
        return img, mask




learn = from_pretrained_fastai("islasher/segm-grapes")

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)
    with torch.no_grad():
        outputs = learn.model(tensor)
    
    outputs = torch.argmax(outputs,1)

    mask = np.array(outputs)
    mask[mask==1]=150
    mask[mask==3]=76 #pole # y no 74
# mask[mask==5]=74 #pole
    mask[mask==2]=29 #wood # y no 25
# mask[mask==6]=25 #wood
    mask[mask==4]=255 #grape
    mask=np.reshape(mask,(480,640)) #en modo matriz
    return Image.fromarray(mask.astype('uint8'))
    
# Creamos la interfaz y la lanzamos. 
gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(128, 128)), outputs=gr.outputs.Image(shape=(480,640)),examples=['color_154.jpg','color_155.jpg']).launch(share=False)