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app.py

@@ -1,181 +1,118 @@
-# import gradio as gr
-# import spaces
-# from huggingface_hub import hf_hub_download
+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", filename=f"{model_id}", local_dir=f"./")
+    return f"./{model_id}"
 
-# def download_models(model_id):
-#     hf_hub_download("SakshiRathi77/void-space-detection", 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.
+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
+    :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")
+    # Load the model
+    model_path = download_models("best.pt")
+    model = yolov9.load(model_path, device="cpu")
     
-#     # Set model parameters
-#     model.conf = conf_threshold
-#     model.iou = iou_threshold
+    # Set model parameters
+    model.conf = conf_threshold
+    model.iou = iou_threshold
     
-#     # Perform inference
-#     results = model(img_path, size=image_size)
+    # Perform inference
+    results = model(img_path, size=image_size)
 
-#     # Optionally, show detection bounding boxes on image
-#     output = results.render()
+    # 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=[
-#                         "state_dict.pt"
-#                     ],
-#                     value="state_dict.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],
-#         )
+    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,
+                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!
-#         </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")
+            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)
 
-# Create Gradio interface
-gr.Interface(fn=detect_void, inputs=input_image, outputs=output_image, title="Void Detection App").launch()