import gradio as gr
import os
os.system("pip -qq install yoloxdetect==0.0.7")
import torch
from yoloxdetect import YoloxDetector

# Images
torch.hub.download_url_to_file('https://tochkanews.ru/wp-content/uploads/2020/09/0.jpg', '1.jpg')
torch.hub.download_url_to_file('https://s.rdrom.ru/1/pubs/4/35893/1906770.jpg', '2.jpg')
torch.hub.download_url_to_file('https://static.mk.ru/upload/entities/2022/04/17/07/articles/detailPicture/5b/39/28/b6/ffb1aa636dd62c30e6ff670f84474f75.jpg', '3.jpg')

def yolox_inference(
    image_path: gr.inputs.Image = None,
    model_path: gr.inputs.Dropdown = 'kadirnar/yolox_s-v0.1.1',
    config_path: gr.inputs.Textbox = 'configs.yolox_s',
    image_size: gr.inputs.Slider = 640
):
    """
    YOLOX inference function
    Args:
        image: Input image
        model_path: Path to the model
        config_path: Path to the config file
        image_size: Image size
    Returns:
        Rendered image
    """

    model = YoloxDetector(model_path, config_path=config_path, device="cpu", hf_model=True)
    pred = model.predict(image_path=image_path, image_size=image_size)
    return pred
        

inputs = [
    gr.inputs.Image(type="filepath", label="Input Image"),
    gr.inputs.Dropdown(
        label="Model Path",
        choices=[
            "kadirnar/yolox_s-v0.1.1",
            "kadirnar/yolox_m-v0.1.1",
            "kadirnar/yolox_tiny-v0.1.1",
        ],
        default="kadirnar/yolox_s-v0.1.1",
    ),
    gr.inputs.Dropdown(
        label="Config Path",
        choices=[
            "configs.yolox_s",
            "configs.yolox_m",
            "configs.yolox_tiny",
        ],
        default="configs.yolox_s",
    ),
    gr.inputs.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size"),
]

outputs = gr.outputs.Image(type="filepath", label="Output Image")
title = "YOLOX is a high-performance anchor-free YOLO."

examples = [
    ["1.jpg", "kadirnar/yolox_m-v0.1.1", "configs.yolox_m", 640],
    ["2.jpg", "kadirnar/yolox_s-v0.1.1", "configs.yolox_s", 640],
    ["3.jpg", "kadirnar/yolox_tiny-v0.1.1", "configs.yolox_tiny", 640],
]

demo_app = gr.Interface(
    fn=yolox_inference,
    inputs=inputs,
    outputs=outputs,
    title=title,
    examples=examples,
    cache_examples=True,
    theme='huggingface',
)
demo_app.launch(debug=True, enable_queue=True)