# import gradio as gr
# import torch
# import yolov5

# # Images
# torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg', 'zidane.jpg')
# torch.hub.download_url_to_file('https://raw.githubusercontent.com/WongKinYiu/yolov7/main/inference/images/image3.jpg', 'image3.jpg')

# def yolov5_inference(
#     image: gr.inputs.Image = None,
#     model_path: gr.inputs.Dropdown = None,
#     image_size: gr.inputs.Slider = 640,
#     conf_threshold: gr.inputs.Slider = 0.25,
#     iou_threshold: gr.inputs.Slider = 0.45,
# ):
#     """
#     YOLOv5 inference function
#     Args:
#         image: Input image
#         model_path: Path to the model
#         image_size: Image size
#         conf_threshold: Confidence threshold
#         iou_threshold: IOU threshold
#     Returns:
#         Rendered image
#     """
#     model = yolov5.load(model_path, device="cpu")
#     model.conf = conf_threshold
#     model.iou = iou_threshold
#     results = model([image], size=image_size)
#     return results.render()[0]
        

# inputs = [
#     gr.inputs.Image(type="pil", label="Input Image"),
#     gr.inputs.Dropdown(["yolov5s.pt", "yolov5l.pt", "yolov5x.pt"], label="Model"),
#     gr.inputs.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size"),
#     gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"),
#     gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold"),
# ]

# outputs = gr.outputs.Image(type="filepath", label="Output Image")
# title = "YOLOv5"
# description = "YOLOv5 is a family of object detection models pretrained on COCO dataset. This model is a pip implementation of the original YOLOv5 model."

# examples = [['zidane.jpg', 'yolov5s.pt', 640, 0.25, 0.45], ['image3.jpg', 'yolov5s.pt', 640, 0.25, 0.45]]
# demo_app = gr.Interface(
#     fn=yolov5_inference,
#     inputs=inputs,
#     outputs=outputs,
#     title=title,
#     examples=examples,
#     cache_examples=True,
#     live=True,
#     theme='huggingface',
# )
# demo_app.launch(debug=True, enable_queue=True)
import gradio as gr
import torch
import yolov5
import subprocess
import tempfile
import time
from pathlib import Path
import uuid
import cv2
import gradio as gr



# Images
#torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg', 'zidane.jpg')
#torch.hub.download_url_to_file('https://raw.githubusercontent.com/obss/sahi/main/tests/data/small-vehicles1.jpeg', 'small-vehicles1.jpeg')
    
def image_fn(
    image: gr.inputs.Image = None,
    model_path: gr.inputs.Dropdown = None,
    image_size: gr.inputs.Slider = 640,
    conf_threshold: gr.inputs.Slider = 0.25,
    iou_threshold: gr.inputs.Slider = 0.45,
):
    """
    YOLOv5 inference function
    Args:
        image: Input image
        model_path: Path to the model
        image_size: Image size
        conf_threshold: Confidence threshold
        iou_threshold: IOU threshold
    Returns:
        Rendered image
    """

    model = yolov5.load(model_path, device="cpu", hf_model=True, trace=False)
    model.conf = conf_threshold
    model.iou = iou_threshold
    results = model([image], size=image_size)
    return results.render()[0]
  


demo_app = gr.Interface(
    fn=image_fn,
    inputs=[
    gr.inputs.Image(type="pil", label="Input Image"),
    gr.inputs.Dropdown(
        choices=[
            "alshimaa/yolo5_epoch100",
            #"kadirnar/yolov7-v0.1",
        ],
        default="alshimaa/yolo5_epoch100",
        label="Model",
    )
    #gr.inputs.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size")
    #gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"),
    #gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold")
],
    outputs=gr.outputs.Image(type="filepath", label="Output Image"),
    title="Object Detector: Identify People Without Mask",
    examples=[['img1.png', 'alshimaa/yolo5_epoch100', 640, 0.25, 0.45], ['img2.png', 'alshimaa/yolo5_epoch100', 640, 0.25, 0.45], ['img3.png', 'alshimaa/yolo5_epoch100', 640, 0.25, 0.45]],
    cache_examples=True,
    live=True,
    theme='huggingface',
)
demo_app.launch(debug=True, enable_queue=True)