import gradio as gr
from huggingface_hub import hf_hub_download

def download_models(model_id):
    model_file_path = hf_hub_download("merve/yolov9", filename=model_id)
    return model_file_path

def yolov9_inference(img_path, model_id, image_size, conf_threshold, iou_threshold):
    """
    Performs object detection using a YOLOv9 model. This function loads a specified YOLOv9 model,
    configures it based on the provided parameters, and carries out inference on a given image. 
    """
    # Dummy placeholder for actual YOLOv9 import and inference logic
    # Assuming you have a YOLOv9 model loading and inference code here
    return img_path  # Placeholder return for demonstration

def app():
    with gr.Blocks() as blocks:
        # Display a title using HTML, centered.
        gr.HTML("""
            <h1 style='text-align: center; margin-bottom: 20px;'>
            YOLOv9 from PipYoloV9 on my data
            </h1>
        """)

        with gr.Row():
            with gr.Column():
                img_path = gr.Image(type="filepath", label="Image")
                model_id = gr.Dropdown(
                    label="Model",
                    choices=["gelan-c.pt", "gelan-e.pt", "yolov9-c.pt", "yolov9-e.pt"],
                    value="gelan-e.pt"
                )
                image_size = gr.Slider(label="Image Size", minimum=320, maximum=1280, step=32, value=640)
                conf_threshold = gr.Slider(label="Confidence Threshold", minimum=0.1, maximum=1.0, step=0.1, value=0.4)
                iou_threshold = gr.Slider(label="IoU Threshold", minimum=0.1, maximum=1.0, step=0.1, value=0.5)
                yolov9_infer = gr.Button("Inference")

            with gr.Column():
                output_image = gr.Image(type="numpy", label="Output")

        yolov9_infer.click(
            fn=yolov9_inference,
            inputs=[img_path, model_id, image_size, conf_threshold, iou_threshold],
            outputs=[output_image]
        )

    return blocks

gradio_app = app()

# Launch the Gradio app, enabling debug mode for detailed error logs and server information.
gradio_app.launch(debug=True)