Update app.py

app.py

@@ -1,141 +1,177 @@
-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.
+# @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: 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(model_id)
+#     model = yolov9.load(model_path, device="cuda:0")
     
-    # 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")
+#                 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],
+#         )
         
-        # 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)
+#         # 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 torch
+import numpy as np
+from models.common import DetectMultiBackend
+from utils.general import non_max_suppression, scale_boxes
+from utils.torch_utils import select_device, smart_inference_mode
+from utils.augmentations import letterbox
+import PIL.Image
+from huggingface_hub import hf_hub_download 
+hf_hub_download("SakshiRathi77/void-space-detection", filename="weights/best.pt", local_dir="./")
+
+
+@smart_inference_mode()
+def predict(image_path, weights='best.pt', imgsz=640, conf_thres=0.1, iou_thres=0.45):
+    # Initialize
+    device = select_device('0')
+    # model = DetectMultiBackend(weights='best.pt', device="0", fp16=False, data='data/coco.yaml')
+        model = DetectMultiBackend(weights='best.pt', device="0", fp16=False)
+    stride, names, pt = model.stride, model.names, model.pt
+
+    # Load image
+    image = np.array(PIL.Image.open(image_path))
+    img = letterbox(img0, imgsz, stride=stride, auto=True)[0]
+    img = img[:, :, ::-1].transpose(2, 0, 1)
+    img = np.ascontiguousarray(img)
+    img = torch.from_numpy(img).to(device).float()
+    img /= 255.0
+    if img.ndimension() == 3:
+        img = img.unsqueeze(0)
+
+    # Inference
+    # pred = model(img, augment=False, visualize=False)
+pred = model(img)
+    # Apply NMS
+    pred = non_max_suppression(pred[0][0], conf_thres, iou_thres, classes=None, max_det=1000)