Added file for initial run

app.py

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+import gradio as gr
+from ultralyticsplus import YOLO, render_result, postprocess_classify_output
+
+
+def classification(image, threshold):
+    model = YOLO('yolov8n-cls.pt')
+    model.overrides['conf'] = threshold
+
+    # result = model('bus.jpg')
+    result = model.predict(image)
+    render = postprocess_classify_output(model=model, result=result[0])
+    return render
+
+
+def detection(image, threshold):
+
+    model = YOLO('yolov8n.pt')
+    model.overrides['conf'] = threshold
+    results = model.predict(image)
+    render = render_result(model=model, image=image, result=results[0])
+    return render
+
+
+def segmentation(image, threshold):
+
+    model = YOLO('yolov8n-seg.pt')
+    model.overrides['conf'] = threshold
+    results = model.predict(image)
+    render = render_result(model=model, image=image, result=results[0])
+    return render
+
+
+with gr.Blocks() as demo:
+
+    with gr.Tab("Detection"):
+        with gr.Row():
+            with gr.Column():
+                detect_input = gr.Image()
+                detect_threshold = gr.Slider(
+                    maximum=1,
+                    step=0.01,
+                    value=0.25,
+                    label="Threshold:",
+                    interactive=True)
+                detect_button = gr.Button("Detect!")
+            with gr.Column():
+                detect_output = gr.Image(
+                    label="Predictions:", interactive=False)
+
+    with gr.Tab("Segmentation"):
+        with gr.Row():
+            with gr.Column():
+                segment_input = gr.Image()
+                segment_threshold = gr.Slider(
+                    maximum=1,
+                    step=0.01,
+                    value=0.25,
+                    label="Threshold:",
+                    interactive=True)
+                segment_button = gr.Button("Segment!")
+            with gr.Column():
+                segment_output = gr.Image(
+                    label="Predictions:", interactive=False)
+    with gr.Tab("Classification"):
+        with gr.Row():
+            with gr.Column():
+                classify_input = gr.Image()
+                classify_threshold = gr.Slider(
+                    maximum=1,
+                    step=0.01,
+                    value=0.25,
+                    label="Threshold:",
+                    interactive=True)
+                classify_button = gr.Button("Classify!")
+            with gr.Column():
+                classify_output = gr.Label(
+                    label="Predictions:", show_label=True, num_top_classes=5)
+
+    detect_button.click(
+        detection,
+        inputs=[
+            detect_input,
+            detect_threshold],
+        outputs=detect_output,
+        api_name="Detect")
+
+    segment_button.click(
+        segmentation,
+        inputs=[
+            segment_input,
+            segment_threshold],
+        outputs=segment_output,
+        api_name="Segmentation")
+
+    classify_button.click(
+        classification,
+        inputs=[
+            classify_input,
+            classify_threshold],
+        outputs=classify_output,
+        api_name="classify")
+
+
+demo.launch(debug=True, enable_queue=True)

requirements.txt

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+ultralyticsplus==0.0.28
+gradio==3.23.0

yolov8n-cls.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f5079cd980628313a2e62cd09d358e5c8debf0d8f75b6e8be7973d94e3a5da9f
+size 5533216

yolov8n-seg.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d39e867b2c3a5dbc1aa764411544b475cb14727bf6af1ec46c238f8bb1351ab9
+size 7054355

yolov8n.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:31e20dde3def09e2cf938c7be6fe23d9150bbbe503982af13345706515f2ef95
+size 6534387