from fastai.vision.all import *
from icevision.all import *
from fastai.basics import *
from fastai.callback import *
from icevision import models
import gradio as gr
import PIL


class_map = ClassMap(['kangaroo']) 
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('fasterRCNNKangaroo.pth', map_location=torch.device('cpu'))
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 = 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(shape=(128,128)), outputs=gr.outputs.Image(),examples=['00001.jpg','00002.jpg']).launch(share=False)