import gradio as gr
import spaces
from huggingface_hub import hf_hub_download

class YOLOv9App:
    def __init__(self):
        self.gradio_app = gr.Blocks()

    def download_models(self, model_id):
        hf_hub_download("merve/yolov9", filename=f"{model_id}", local_dir="./")
        return f"./{model_id}"

    @spaces.GPU
    def yolov9_inference(self, img_path, model_id, image_size, conf_threshold, iou_threshold):
        """
        Load a YOLOv9 model, configure it, perform inference on an image, and optionally adjust 
        the input size and apply test time augmentation.
        
        :param model_path: Path to the YOLOv9 model file.
        :param conf_threshold: Confidence threshold for NMS.
        :param iou_threshold: IoU threshold for NMS.
        :param img_path: Path to the image file.
        :param size: Optional, input size for inference.
        :return: A tuple containing the detections (boxes, scores, categories) and the results object for further actions like displaying.
        """
        # Import YOLOv9
        import yolov9
        
        # Load the model
        model_path = self.download_models(model_id)
        model = yolov9.load(model_path, device="cpu")
        
        # Set model parameters
        model.conf = conf_threshold
        model.iou = iou_threshold
        
        # Perform inference
        results = model(img_path, size=image_size)

        # Optionally, show detection bounding boxes on image
        output = results.render()
        
        return output[0]

    def app(self):
        with gr.Blocks():
            with gr.Row():
                with gr.Column():
                    img_path = gr.Image(type="filepath", label="Image")
                    model_path = 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(value="Submit")

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

            yolov9_infer.click(
                fn=self.yolov9_inference,
                inputs=[
                    img_path,
                    model_path,
                    image_size,
                    conf_threshold,
                    iou_threshold,
                ],
                outputs=[output_numpy],
            )

    def launch(self):
        with self.gradio_app:
            gr.HTML(
                """
                <h1 style='text-align: center'>
                YOLOv9 Base Model
                </h1>
                """
            )
            gr.HTML(
                """
                <h3 style='text-align: center'>
                Aplicação para ajudar nos resgates do RS
                </h3>
                """
            )
            with gr.Row():
                with gr.Column():
                    self.app()
        self.gradio_app.launch(debug=True)

# Criação e execução da aplicação
if __name__ == "__main__":
    yolov9_app = YOLOv9App()
    yolov9_app.launch()