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)