app.py

import icevision
from icevision.all import *
import icedata
import PIL
import torch
from torchvision import transforms
import gradio as gr
from icevision.models.checkpoint import *

model_type = models.mmdet.retinanet ##
backbone = model_type.backbones.resnet50_fpn_1x##

model = torch.hub.load('~/Download/Model_M_set11_ret_nov8_map63.6.pth', source = 'local')

def show_preds_gradio(input_image, display_label, display_bbox, detection_threshold):

    if detection_threshold==0: detection_threshold=0.5

    img = PIL.Image.fromarray(input_image, 'RGB')

    pred_dict  = model_type.end2end_detect(img, 
                                           valid_tfms, 
                                           model_loaded, ## 
                                           class_map=class_map, 
                                           detection_threshold=detection_threshold,
                                           display_label=display_label, 
                                           display_bbox=display_bbox, 
                                           return_img=True, 
                                           font_size=16, 
                                           label_color="#FF59D6")

    return pred_dict['img']

display_chkbox_label = gr.inputs.Checkbox(label="Label", default=True)
display_chkbox_box = gr.inputs.Checkbox(label="Box", default=True)

detection_threshold_slider = gr.inputs.Slider(minimum=0, maximum=1, step=0.1, default=0.5, label="Detection Threshold")

outputs = gr.outputs.Image(type="pil")

# Option 1: Get an image from local drive
gr_interface = gr.Interface(fn=show_preds_gradio, inputs=["image", display_chkbox_label, display_chkbox_box,  detection_threshold_slider], outputs=outputs, title='IceApp - COCO')

gr_interface.launch(inline=False, share=True, debug=True)