Create app.py

app.py

@@ -0,0 +1,147 @@
+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: Annotated image with the number of objects detected.
+    """
+    # 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
+    annotated_image = results.render()
+    
+    # Count the number of detected objects
+    num_objects_detected = len(results.pred[0])
+    
+    return annotated_image, f"Number of objects detected: {num_objects_detected}"
+
+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_image = gr.Image(type="numpy",label="Output")
+                num_objects_detected = gr.Textbox(label="Number of Objects Detected", readonly=True)
+
+        yolov9_infer.click(
+            fn=yolov9_inference,
+            inputs=[
+                img_path,
+                model_path,
+                image_size,
+                conf_threshold,
+                iou_threshold,
+            ],
+            outputs=[output_image, num_objects_detected],
+        )
+        
+        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_image, num_objects_detected],
+            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)