Delete app1

app1

@@ -1,145 +0,0 @@
-import gradio as gr
-import spaces
-from huggingface_hub import hf_hub_download
-
-
-def download_models(model_id):
-    hf_hub_download("merve/yolov9", filename=f"{model_id}", local_dir=f"./")
-    return f"./{model_id}"
-
-@spaces.GPU
-def yolov9_inference(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 = download_models(model_id)
-    model = yolov9.load(model_path, device="cuda:0")
-    
-    # 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():
-    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="Inference")
-
-            with gr.Column():
-                output_numpy = gr.Image(type="numpy",label="Output")
-
-        yolov9_infer.click(
-            fn=yolov9_inference,
-            inputs=[
-                img_path,
-                model_path,
-                image_size,
-                conf_threshold,
-                iou_threshold,
-            ],
-            outputs=[output_numpy],
-        )
-        
-        gr.Examples(
-            examples=[
-                [
-                    "data/zidane.jpg",
-                    "gelan-e.pt",
-                    640,
-                    0.4,
-                    0.5,
-                ],
-                [
-                    "data/huggingface.jpg",
-                    "yolov9-c.pt",
-                    640,
-                    0.4,
-                    0.5,
-                ],
-            ],
-            fn=yolov9_inference,
-            inputs=[
-                img_path,
-                model_path,
-                image_size,
-                conf_threshold,
-                iou_threshold,
-            ],
-            outputs=[output_numpy],
-            cache_examples=True,
-        )
-
-
-gradio_app = gr.Blocks()
-with gradio_app:
-    gr.HTML(
-        """
-    <h1 style='text-align: center'>
-    YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information
-    </h1>
-    """)
-    gr.HTML(
-        """
-        <h3 style='text-align: center'>
-        Follow me for more!
-        <a href='https://twitter.com/kadirnar_ai' target='_blank'>Twitter</a> | <a href='https://github.com/kadirnar' target='_blank'>Github</a> | <a href='https://www.linkedin.com/in/kadir-nar/' target='_blank'>Linkedin</a>  | <a href='https://www.huggingface.co/kadirnar/' target='_blank'>HuggingFace</a>
-        </h3>
-        """)
-    with gr.Row():
-        with gr.Column():
-            app()
-
-gradio_app.launch(debug=True)