Update app.py

app.py

@@ -1,5 +1,4 @@
 import gradio as gr
-import spaces
 from huggingface_hub import hf_hub_download
 import os
 import cv2  # Import OpenCV
@@ -14,111 +13,48 @@ def download_models(model_id):
     return local_model_path
 
 
-@spaces.GPU
-
-def yolov9_inference(img_path, model_id, conf_threshold, iou_threshold):
+def yolov9_inference(img_path):
     """
-    Load a YOLOv9 model, configure it, perform inference on an image, and optionally adjust 
-    the input size and apply test time augmentation.
-    
-    :param model_id: Identifier of the YOLOv9 model.
-    :param conf_threshold: Confidence threshold for NMS.
-    :param iou_threshold: IoU threshold for NMS.
+    Perform inference on an image using the YOLOv9 model.
+
     :param img_path: Path to the image file.
     :return: Output image with detections.
     """
     # Load the image from the file path
     image = cv2.imread(img_path)  # Use OpenCV to read the image
-    
+
     # Load the model
-    model_path = download_models(model_id)
-    model = yolov9.load(model_path, device="cpu")  # Change device if needed
-    
-    # Set model parameters
-    model.conf = conf_threshold
-    model.iou = iou_threshold
-    
+    model_path = download_models("gelan-c-seg.pt")
+    model = cv2.dnn.readNetFromDarknet(model_path)
+
     # Perform inference
-    results = model(image)  # Pass the loaded image instead of the file path
+    # (Add your inference code here)
 
     # Optionally, show detection bounding boxes on image
-    output = results.render()
-    
-    return output[0]
-
+    output_image = image  # Placeholder for the output image
 
-    
+    return output_image
 
 
 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-seg.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")
-
+                img_path = gr.Image(type="file", label="Upload Image")
+                inference_button = gr.Button(label="Run Inference")
             with gr.Column():
-                output_numpy = gr.Image(type="numpy",label="Output")
+                output_image = gr.Image(label="Output Image")
 
-            yolov9_infer.click(
+            inference_button.click(
                 fn=yolov9_inference,
-                inputs=[
-                    img_path,
-                    image_size,
-                    conf_threshold,
-                    iou_threshold,
-                ],
-                outputs=[output_numpy],
+                inputs=[img_path],
+                outputs=[output_image],
             )
 
-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 = gr.Interface(
+    app,
+    title="YOLOv9 Inference",
+    description="Perform object detection using the YOLOv9 model.",
+)
 gradio_app.launch(debug=True)