Update app.py

app.py

@@ -1,15 +1,168 @@
+# 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
-# 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):
+
+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.
@@ -21,11 +174,12 @@ def yolov9_inference(img_path, image_size, conf_threshold, iou_threshold):
     :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 = yolov9.load("./best.pt")
+    model_path = download_models(model_id)
+    model = yolov9.load(model_path)
     
     # Set model parameters
     model.conf = conf_threshold
@@ -45,6 +199,13 @@ def app():
         with gr.Row():
             with gr.Column():
                 img_path = gr.Image(type="filepath", label="Image")
+                model_path = gr.Dropdown(
+                    label="Model",
+                    choices=[
+                        "yolov9-c.pt",
+                    ],
+                    value="yolov9-c.pt",
+                )
                 image_size = gr.Slider(
                     label="Image Size",
                     minimum=320,
@@ -75,7 +236,7 @@ def app():
             fn=yolov9_inference,
             inputs=[
                 img_path,
-                # model_path,
+                model_path,
                 image_size,
                 conf_threshold,
                 iou_threshold,
@@ -83,7 +244,7 @@ def app():
             outputs=[output_numpy],
         )
         
-  
+
 
 
 gradio_app = gr.Blocks()
@@ -98,6 +259,7 @@ with gradio_app:
         """
         <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():
@@ -105,43 +267,3 @@ with gradio_app:
             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()