app.py

import gradio as gr
from icevision.all import *
import PIL
class_map = ClassMap(['raccoon'])
model = models.torchvision.faster_rcnn.model(backbone=models.torchvision.faster_rcnn.backbones.resnet50_fpn(pretrained=True), num_classes=len(class_map))
state_dict = torch.load('fasterRCNNRaccoonRESNET50.pth')
model.load_state_dict(state_dict)
size = 384
infer_tfms = tfms.A.Adapter([*tfms.A.resize_and_pad(size),tfms.A.Normalize()])

def predict(img):
  # img = PIL.Image.open(img)
  np.int = int
  img = PIL.Image.fromarray(img)

  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=['raccoon/train/images/raccoon-197.jpg','raccoon/train/images/raccoon-177.jpg']).launch(share=True,debug=True)