# from huggingface_hub import from_pretrained_fastai
import gradio as gr
# from fastai.vision.all import *
from icevision.all import *

class_map = {'background': 0, 'kangaroo': 1}
model = models.torchvision.faster_rcnn.model(backbone=models.torchvision.faster_rcnn.backbones.resnet50_fpn,
                                             num_classes=2)
state_dict = torch.load('fasterRCNNCanguros/fasterRCNNCanguros.pth')
model.load_state_dict(state_dict)
class_map = ClassMap(['kangaroo'])

# Definimos una función que se encarga de llevar a cabo las predicciones
def predict(img):
    img = PILImage.create(img)
    infer_tfms = tfms.A.Adapter([*tfms.A.resize_and_pad(size),tfms.A.Normalize()])
    pred_dict = models.torchvision.faster_rcnn.end2end_detect(img, infer_tfms, model.to("cpu"), class_map=class_map, detection_threshold=0.5)
    return pred_dict['img']
    
# Creamos la interfaz y la lanzamos. 
gr.Interface(fn=predict, inputs=image, outputs=image ,examples=['00011.jpg','00014.jpg']).launch(share=False)