app.py

import gradio as gr
from fastai.vision.all import *
from icevision.all import *


class_map = ClassMap(['raccoon'])
model = models.torchvision.faster_rcnn.model(backbone=models.torchvision.faster_rcnn.backbones.resnet18_fpn(pretrained=True),num_classes=len(class_map))
state_dict = torch.load('fasterRCNNRaccoon.pth',map_location=torch.device('cpu'))
model.load_state_dict(state_dict)

infer_tfms = tfms.A.Adapter([*tfms.A.resize_and_pad(384),tfms.A.Normalize()])

def predict(img):
  #img = PILImage.create(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']

gr.Interface(fn=predict, inputs=gr.inputs.Image(type = 'filepath'), outputs=gr.outputs.Image(type='pil'),examples=['raccoon-103.jpg','raccoon-104.jpg']).launch(share=False)