Update app.py

app.py

@@ -1,26 +1,31 @@
 import gradio as gr
 
+
+import torch
 from ultralyticsplus import YOLO, render_result
 
+torch.hub.download_url_to_file(
+    'https://external-content.duckduckgo.com/iu/?u=https%3A%2F%2Ftexashafts.com%2Fwp-content%2Fuploads%2F2016%2F04%2Fconstruction-worker.jpg', 'one.jpg')
+torch.hub.download_url_to_file(
+    'https://www.pearsonkoutcherlaw.com/wp-content/uploads/2020/06/Construction-Workers.jpg', 'two.jpg')
+torch.hub.download_url_to_file(
+    'https://nssgroup.com/wp-content/uploads/2019/02/Building-maintenance-blog.jpg', 'three.jpg')
 
-#torch.hub.download_url_to_file("img1.jpg", 'one.jpg')
-#torch.hub.download_url_to_file("img2.jpg", 'two.jpg')
-#torch.hub.download_url_to_file("img3.jpg", 'three.jpg')
 
 def yoloV8_func(image: gr.Image = None,
-                image_size: gr.Slider = 640,
-                conf_threshold: gr.Slider = 0.4,
-                iou_threshold: gr.Slider = 0.50):
+                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.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.pt"
+    model_path = "./best.pt.pt"
     model = YOLO(model_path)
 
     # Perform object detection on the input image using the YOLOv8 model
@@ -38,39 +43,31 @@ def yoloV8_func(image: gr.Image = None,
     # Render the output image with bounding boxes around detected objects
     render = 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, value=640,  # Changed 'default' to 'value'
-              step=32, label="Image Size"),
-    gr.Slider(minimum=0.0, maximum=1.0, value=0.25,  # Changed 'default' to 'value'
-              step=0.05, label="Confidence Threshold"),
-    gr.Slider(minimum=0.0, maximum=1.0, value=0.45,  # Changed 'default' to 'value'
-              step=0.05, label="IOU Threshold"),
+    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"),
 ]
 
 
 
-output_image = gr.Image(label="Output Image")
-
+outputs = gr.Image(type="filepath", label="Output Image")
 
 title = "YOLOv8 101: Custom Object Detection on Construction Workers"
 
-
-examples = [['img1.jpg', 640, 0.5, 0.7],
-            ['img2.jpg', 800, 0.5, 0.6],
-            ['img3.jpg', 900, 0.5, 0.8]]
+examples = [['one.jpg', 640, 0.5, 0.7],
+            ['two.jpg', 800, 0.5, 0.6],
+            ['three.jpg', 900, 0.5, 0.8]]
 
 yolo_app = gr.Interface(
     fn=yoloV8_func,
     inputs=inputs,
-    outputs=output_image,
+    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).queue()