Update app.py

app.py

@@ -1,23 +1,27 @@
 import gradio as gr
+
+from ultralytics import YOLO
+model = YOLO('./best_model.pt')  # load your custom trained model
 import torch
-from ultralyticsplus import YOLO, render_result
+#from ultralyticsplus import render_result
+from render import custom_render_result
+
 
 
-def yoloV9_func(image: gr.Image = None,
+def yoloV8_func(image: gr.Image = None,
                 image_size: int = 640,
                 conf_threshold: float = 0.4,
-                iou_threshold: float = 0.50):
-    """This function performs YOLOv9 object detection on the given image.
-
+                iou_threshold: float = 0.5):
+    """This function performs YOLOv8 object detection on the given image.
     Args:
-        image (gr.inputs.Image, optional): Input image to detect objects on. Defaults to None.
-        image_size (gr.inputs.Slider, optional): Desired image size for the model. Defaults to 640.
-        conf_threshold (gr.inputs.Slider, optional): Confidence threshold for object detection. Defaults to 0.4.
-        iou_threshold (gr.inputs.Slider, optional): Intersection over Union threshold for object detection. Defaults to 0.50.
+        image (gr.Image, optional): Input image to detect objects on. Defaults to None.
+        image_size (int, optional): Desired image size for the model. Defaults to 640.
+        conf_threshold (float, optional): Confidence threshold for object detection. Defaults to 0.4.
+        iou_threshold (float, optional): Intersection over Union threshold for object detection. Defaults to 0.50.
     """
     # Load the YOLOv8 model from the 'best.pt' checkpoint
-    model_path = "best_model.pt"
-    model = YOLO(model_path)
+    model_path = "yolov5s.pt"
+    # model = torch.hub.load('ultralytics/yolov8', 'custom', path='/content/best.pt', force_reload=True, trust_repo=True)
 
     # Perform object detection on the input image using the YOLOv8 model
     results = model.predict(image,
@@ -32,35 +36,33 @@ def yoloV9_func(image: gr.Image = None,
     print("Probability:", box.conf)
 
     # Render the output image with bounding boxes around detected objects
-    render = render_result(model=model, image=image, result=results[0])
+    render = custom_render_result(model=model, image=image, result=results[0])
     return render
 
 
 inputs = [
     gr.Image(type="filepath", label="Input Image"),
-    gr.Slider(minimum=320, maximum=1280, step=32, label="Image Size"),
+    gr.Slider(minimum=320, maximum=1280, step=32, label="Image Size", value=640),
     gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label="Confidence Threshold"),
     gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label="IOU Threshold"),
 ]
 
-
 outputs = gr.Image(type="filepath", label="Output Image")
 
-title = "CUSTOM yolov9 model for room cleanliness"
-
+title = "YOLOv8 101: Custom Object Detection on meter"
 
 examples = [['one.jpg', 640, 0.5, 0.7],
             ['two.jpg', 640, 0.5, 0.6],
             ['three.jpg', 640, 0.5, 0.8]]
 
 yolo_app = gr.Interface(
-    fn=yoloV9_func,
+    fn=yoloV8_func,
     inputs=inputs,
     outputs=outputs,
     title=title,
     examples=examples,
-    cache_examples=True,
+    cache_examples=False,
 )
 
 # Launch the Gradio interface in debug mode with queue enabled
-yolo_app.launch(debug=True, enable_queue=True)
+yolo_app.launch(debug=True,share=True).queue()