Create app.py

app.py

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+import gradio as gr
+
+
+def yolov9_inference(model_path, device, conf_threshold, iou_threshold, img_path, size=640):
+    """
+    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 device: Computation device, 'cpu' or 'cuda'.
+    :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 = yolov9.load(model_path, device=device)
+    
+    # Set model parameters
+    model.conf = conf_threshold
+    model.iou = iou_threshold
+    
+    # Perform inference
+    results = model(img_path, size=size)
+
+    # Optionally, show detection bounding boxes on image
+    save_path = 'output/'
+    results.save(labels=True, save_dir=save_path)
+    
+
+    return save_path + 'elon.jpg'
+
+
+inputs = [
+    gr.Image(label="Input Image"),
+    gr.Dropdown(
+        label="Model",
+        choices=[
+            "gelan-c.pt",
+            "gelan-e.pt",
+            "yolov9-c.pt",
+            "yolov9-e.pt",
+        ],
+        value="gelan-e.pt",
+    ),
+    gr.Slider(minimum=320, maximum=1280, value=1280, step=32, label="Image Size"),
+    gr.Slider(minimum=0.0, maximum=1.0, value=0.25, step=0.05, label="Confidence Threshold"),
+    gr.Slider(minimum=0.0, maximum=1.0, value=0.45, step=0.05, label="IOU Threshold"),
+]
+
+outputs = gr.Image(type="filepath", label="Output Image")
+title = "YOLOv9"
+
+demo_app = gr.Interface(
+    fn=yolov9_inference,
+    inputs=inputs,
+    outputs=outputs,
+    title=title,
+)
+demo_app.launch(debug=True)