app.py

from huggingface_hub import from_pretrained_fastai
import gradio as gr
from icevision.all import *

repo_id = "paascorb/image-detection-efficientdet"

learner = from_pretrained_fastai(repo_id)

# Definimos una función que se encarga de llevar a cabo las predicciones
def predict(img):
    img = PIL.Image.open('mapaches/test/images/raccoon-190.jpg')
    infer_tfms = tfms.A.Adapter([*tfms.A.resize_and_pad(size),tfms.A.Normalize()])
    pred_dict  = models.ross.efficientdet.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=gr.inputs.Image(shape=(128, 128)), outputs=gr.outputs.Image(shape(128,128)),
             examples=['raccoon-161.jpg','raccoon-162.jpg']).launch(share=False)