Update app.py

app.py

@@ -1,53 +1,74 @@
 import gradio as gr
-import PIL.Image as Image
-from ultralytics import ASSETS, YOLO
 
+from ultralytics import YOLO
 
-from PIL import Image
-import gradio as gr
+# Load the YOLOv8 model from the 'best.pt' checkpoint
+model_path = "new_data_improved_object_detector.pt"
+
+model = YOLO(model_path)  
 
-model_path = 'new_data_improved_object_detector.pt'
+import torch
 
-model = YOLO(model_path)
+#from ultralyticsplus import render_result
+from render import custom_render_result
+def yoloV8_func(image: gr.Image = None,
+                image_size: int = 640,
+                conf_threshold: float = 0.4,
+                iou_threshold: float = 0.5):
+    
+    """This function performs YOLOv8 object detection on the given image.
 
+    Args:
+        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.
+    """
+   
+    # model = torch.hub.load('ultralytics/yolov8', 'custom', path='/content/best.pt', force_reload=True, trust_repo=True)
 
-def predict_image(img, conf_threshold, iou_threshold):
-    """Predicts and plots labeled objects in an image using YOLOv8 model with adjustable confidence and IOU thresholds."""
-    # Convert the input image to grayscale
-    img = img.convert('L')
+    # Perform object detection on the input image using the YOLOv8 model
+    results = model.predict(image,
+                            conf=conf_threshold,
+                            iou=iou_threshold,
+                            imgsz=image_size)
 
-    results = model.predict(
-        source=img,
-        conf=conf_threshold,
-        iou=iou_threshold,
-        show_labels=True,
-        show_conf=True,
-        imgsz=640,
-    )
+    # Print the detected objects' information (class, coordinates, and probability)
+    box = results[0].boxes
+    print("Object type:", box.cls)
+    print("Coordinates:", box.xyxy)
+    print("Probability:", box.conf)
 
-    for r in results:
-        im_array = r.plot()
-        im = Image.fromarray(im_array[..., ::-1])
+    # Render the output image with bounding boxes around detected objects
+    render = custom_render_result(model=model, image=image, result=results[0])
+    return render
 
-    return im
 
+inputs = [
+    gr.Image(type="filepath", label="Input Image"),
+    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"),
+]
 
-iface = gr.Interface(
-    
-    fn=predict_image,
-    inputs=[
-        gr.Image(type="pil", label="Upload Image"),
-        gr.Slider(minimum=0, maximum=1, value=0.25, label="Confidence threshold"),
-        gr.Slider(minimum=0, maximum=1, value=0.45, label="IoU threshold"),
-    ],
-    outputs=gr.Image(type="pil", label="Result"),
-    title="Ultralytics Gradio",
-    description="Upload images for inference. The Ultralytics YOLOv8n model is used by default.",
-    examples = [
+outputs = gr.Image(type="filepath", label="Output Image")
+
+title = "YOLOv8 101: Custom Object Detection on meter"
+
+examples = [
             ['1.jpg'],
             ['2.jpg'],
             ['3.jpg']
       ]
+
+yolo_app = gr.Interface(
+    fn=yoloV8_func,
+    inputs=inputs,
+    outputs=outputs,
+    title=title,
+    examples=examples,
+    cache_examples=False,
 )
 
-gr_interface.launch(inline=False, share=False, debug=True)
+# Launch the Gradio interface in debug mode with queue enabled
+yolo_app.launch(debug=True, share=True).queue()