app.py

import gradio as gr
from ultralytics import YOLO
# Import YOLOv9
import yolov9

# Define function to perform prediction with YOLOv9 model
def predict_image(image, model_path, image_size, conf_threshold, iou_threshold):

    
    # Load YOLO model
    model = YOLO(model_path)
    
    # Perform inference with YOLO model
    results = model(image, size=image_size, conf=conf_threshold, iou=iou_threshold)
    
    # Render the output
    output_image = results.render()
    
    return output_image[0]

# Define Gradio interface
def app():
    with gr.Blocks():
        with gr.Row():
            with gr.Column():
                img_path = gr.Image(type="filepath", label="Image")
                model_path = gr.Dropdown(
                    label="Model",
                    choices=[
                        "yolov9c-seg.pt",
                        "yolov5m.pt",
                        "yolov5l.pt",
                        "yolov5x.pt",                                              
                    ],
                    value="yolov5s.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=predict_image,
            inputs=[
                img_path,
                model_path,
                image_size,
                conf_threshold,
                iou_threshold,
            ],
            outputs=[output_numpy],
        )   

gradio_app = gr.Blocks()
with gradio_app:
    gr.HTML(
        """
    <h1 style='text-align: center'>
    YOLOv9 Base Model
    </h1>
    """)
    gr.HTML(
        """
        <h3 style='text-align: center'>
        </h3>
        """)
    with gr.Row():
        with gr.Column():
            app()    
    
gradio_app.launch(debug=True)