import gradio as gr
import torch
import yolov7


def yolov7_inference(
    image,
    model_path,
    image_size,
    conf_threshold,
    iou_threshold,
):
    """
    YOLOv7 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 = yolov7.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]
        

inputs = [
    gr.Image(type="pil", label="Input Image"),
    gr.Dropdown(
        choices=[
            "nihalbaig/yolov7",
        ],
        value="nihalbaig0/yolov7",
        label="Model",
    ),
    gr.Slider(minimum=320, maximum=1280, value=640, step=32, label="Image Size"),
    gr.Slider(minimum=0.0, maximum=1.0, value=0.25, step=0.05, label="Confidence Threshold"),
    gr.Slider(minimum=0.0, maximum=1.0, value=0.45, step=0.05, label="IOU Threshold"),
]

outputs = gr.Image(type="filepath", label="Output Image")
title = "Project-350: BD Vehicle Detection for Autonomous Vehicle"

demo_app = gr.Interface(
    fn=yolov7_inference,
    inputs=inputs,
    outputs=outputs,
    title=title,
    cache_examples=True,
    theme="dark",
)
demo_app.launch(debug=True, queue=True)