app.py

import gradio as gr
from huggingface_hub import hf_hub_download
import os
import cv2  # Import OpenCV
import gradio.inputs as gr_inputs 

def download_models(model_id):
    # Check if the model file exists locally
    local_model_path = "./gelan-c-seg.pt"
    if not os.path.exists(local_model_path):
        # Download the model from Hugging Face if it doesn't exist locally
        hf_hub_download(model_id, filename="gelan-c-seg.pt", local_dir="./")
    return local_model_path


def yolov9_inference(img):
    """
    Perform inference on an image using the YOLOv9 model.
    :param img: Input image.
    :return: Output image with detections.
    """
    # Load the model
    model_path = download_models("merve/yolov9")
    # Assuming you're using a YOLOv9 model from Ultralytics, you would typically use their library to load the model
    model = cv2.dnn.readNetFromDarknet(model_path)
    # Perform inference
    # Placeholder for inference code; replace with your actual inference code
    results = model.forward()
    # Optionally, show detection bounding boxes on image
    output_image = img  # Placeholder for output image; replace with your actual output image
    return output_image



def app():
    return gr.Interface(
        fn=yolov9_inference,
        inputs=inputs.Image(type="file", label="Image"),  # Use inputs.Image instead of gr.inputs.Image
        outputs="image",
        title="YOLOv9 Inference",
        description="Perform object detection using the YOLOv9 model.",
        theme="compact"
    )


gradio_app = app()
gradio_app.launch(debug=True)