from huggingface_hub import from_pretrained_fastai
import gradio as gr
from fastai.vision.all import *
from icevision.all import *
from icevision.models.checkpoint import *
import PIL

checkpoint_path = "efficientdetMapaches.pth"
checkpoint_and_model = model_from_checkpoint(checkpoint_path)
model = checkpoint_and_model["model"]
model_type = checkpoint_and_model["model_type"]
class_map = checkpoint_and_model["class_map"]

img_size = checkpoint_and_model["img_size"]
valid_tfms = tfms.A.Adapter([*tfms.A.resize_and_pad(img_size), tfms.A.Normalize()])

# Definimos una función que se encarga de llevar a cabo las predicciones
def predict(img):
    img = PIL.Image.open(img)
    pred_dict  = model_type(img, valid_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=gr.inputs.Image(shape=(128, 128)), outputs=gr.outputs.Image(shape(128,128)),
             examples=['raccoon-161.jpg','raccoon-162.jpg']).launch(share=False)