Update app.py

app.py

@@ -1,196 +1,21 @@
-# import gradio as gr
-# import spaces
-# from huggingface_hub import hf_hub_download
-# # Import YOLOv9
-# import yolov9
-
-# def download_models(model_id):
-#     hf_hub_download("SakshiRathi77/void-space-detection/weights", filename=f"{model_id}", local_dir=f"./")
-#     return f"./{model_id}"
-
-# def download_models(model_id):
-#     hf_hub_download("merve/yolov9", filename=f"{model_id}", local_dir=f"./")
-#     return f"./{model_id}"
-
-# def yolov9_inference(img_path, 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.
-#     """
-
-    
-#     # Load the model
-#     model_path = download_models()
-#     # model = yolov9.load("./best.pt")
-    
-#     # 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")
-#                 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],
-#         )
-        
-  
-
-
-# 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!
-#         </h3>
-#         """)
-#     with gr.Row():
-#         with gr.Column():
-#             app()
-
-# gradio_app.launch(debug=True)
-
-# make sure you have the following dependencies
-# import gradio as gr
-# import torch
-# from torchvision import transforms
-# from PIL import Image
-
-# # Load the YOLOv9 model
-# model_path = "best.pt"  # Replace with the path to your YOLOv9 model
-# model = torch.load(model_path)
-
-# # Define preprocessing transforms
-# preprocess = transforms.Compose([
-#     transforms.Resize((640, 640)),  # Resize image to model input size
-#     transforms.ToTensor(),           # Convert image to tensor
-# ])
-
-# # Define a function to perform inference
-# def detect_void(image):
-#     # Preprocess the input image
-#     image = Image.fromarray(image)
-#     image = preprocess(image).unsqueeze(0)  # Add batch dimension
-
-#     # Perform inference
-#     with torch.no_grad():
-#         output = model(image)
-
-#     # Post-process the output if needed
-#     # For example, draw bounding boxes on the image
-
-#     # Convert the image back to numpy array
-#     # and return the result
-#     return output.squeeze().numpy()
-
-# # Define Gradio interface components
-# input_image = gr.inputs.Image(shape=(640, 640), label="Input Image")
-# output_image = gr.outputs.Image(label="Output Image")
-
-# # Create Gradio interface
-# gr.Interface(fn=detect_void, inputs=input_image, outputs=output_image, title="Void Detection App").launch()
-
-
-
 
 import gradio as gr
 import spaces
 from huggingface_hub import hf_hub_download
-
-
-# def download_models(model_id):
-#     hf_hub_download("SakshiRathi77/void-space-detection/weights", filename=f"{model_id}", local_dir=f"./")
-#     return f"./{model_id}"
-
+import yolov9
 
 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_id)
-    
     # 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]
 
 
@@ -202,7 +27,7 @@ def app():
                 model_path = gr.Dropdown(
                     label="Model",
                     choices=[
-                        "best.pt",
+                        "./best.pt",
                     ],
                     value="./best.pt",
                 )
@@ -252,16 +77,9 @@ with gradio_app:
     gr.HTML(
         """
     <h1 style='text-align: center'>
-    YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information
+    YOLOv9: Detect Void Space in Retail Shelf
     </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()