import cv2
# from icevision.all import *
# import icedata
# import PIL, requests
# import torch
# from torchvision import transforms
# import gradio as gr

# # Download the dataset
# url = "https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjects.zip"
# dest_dir = "fridge"
# data_dir = icedata.load_data(url, dest_dir)

# # Create the parser
# parser = parsers.VOCBBoxParser(annotations_dir=data_dir / "odFridgeObjects/annotations", images_dir=data_dir / "odFridgeObjects/images")

# # Parse annotations to create records
# train_records, valid_records = parser.parse()

# class_map = parser.class_map

# extra_args = {}
# model_type = models.torchvision.retinanet
# backbone = model_type.backbones.resnet50_fpn
# # Instantiate the model
# model = model_type.model(backbone=backbone(pretrained=True), num_classes=len(parser.class_map), **extra_args) 

# # Transforms
# # size is set to 384 because EfficientDet requires its inputs to be divisible by 128
# image_size = 384
# train_tfms = tfms.A.Adapter([*tfms.A.aug_tfms(size=image_size, presize=512), tfms.A.Normalize()])
# valid_tfms = tfms.A.Adapter([*tfms.A.resize_and_pad(image_size), tfms.A.Normalize()])
# # Datasets
# train_ds = Dataset(train_records, train_tfms)
# valid_ds = Dataset(valid_records, valid_tfms)
# # Data Loaders
# train_dl = model_type.train_dl(train_ds, batch_size=8, num_workers=4, shuffle=True)
# valid_dl = model_type.valid_dl(valid_ds, batch_size=8, num_workers=4, shuffle=False)
# metrics = [COCOMetric(metric_type=COCOMetricType.bbox)]
# learn = model_type.fastai.learner(dls=[train_dl, valid_dl], model=model, metrics=metrics)

# learn = learn.load('model')

# def show_preds(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, 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 = gr.inputs.CheckboxGroup(["Label", "BBox"], label="Display", default=True)
# 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, inputs=["image", display_chkbox_label, display_chkbox_box,  detection_threshold_slider], outputs=outputs, title='IceApp - Fridge Object')

# # #  Option 2: Grab an image from a webcam
# # gr_interface = gr.Interface(fn=show_preds, inputs=["webcam", display_chkbox_label, display_chkbox_box,  detection_threshold_slider], outputs=outputs, title='IceApp - COCO', live=False)

# # #  Option 3: Continuous image stream from the webcam
# # gr_interface = gr.Interface(fn=show_preds, inputs=["webcam", display_chkbox_label, display_chkbox_box,  detection_threshold_slider], outputs=outputs, title='IceApp - COCO', live=True)


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