Update app.py

app.py

@@ -4,8 +4,6 @@ from PIL import Image
 from ultralytics import ASSETS, YOLO
 from ultralytics.utils.downloads import safe_download
 from huggingface_hub import hf_hub_download
-import os
-os.environ["YOLO_CONFIG_DIR"] = "/tmp/Ultralytics"
 
 # Download OBB test image if not exists
 OBB_IMAGE = ASSETS.parent / "boats.jpg"
@@ -78,3 +76,80 @@ def predict_yoloe26(image, model_name, classes_text, conf, retina):
     )
 
     return Image.fromarray(results[0].plot()[..., ::-1])
+
+
+with gr.Blocks(title="Ultralytics YOLO26 & YOLOE26 Demo") as demo:
+    gr.Markdown(
+        "# 🚀 Ultralytics YOLO26 & YOLOE26 Demo\n"
+        "Showcasing YOLO26 tasks and YOLOE26 open-vocabulary detection. "
+        "[GitHub](https://github.com/ultralytics/ultralytics) | [Docs](https://docs.ultralytics.com/models/yolo26/)"
+    )
+
+    with gr.Tabs():
+        with gr.Tab("YOLO26 Tasks"):
+            gr.Markdown("### Ultralytics YOLO26: Detection, Segmentation, Pose, OBB, Classification")
+            with gr.Row():
+                with gr.Column():
+                    y26_image = gr.Image(type="pil", label="Upload Image")
+                    with gr.Row():
+                        y26_model = gr.Dropdown(["YOLO26-N", "YOLO26-S", "YOLO26-M", "YOLO26-L", "YOLO26-X"], label="Model")
+                        y26_task = gr.Dropdown(list(TASK_SUFFIX.keys()), label="Task")
+                    with gr.Accordion("Advanced Settings", open=False):
+                        y26_conf = gr.Slider(0, 1, label="Confidence Threshold")
+                        y26_iou = gr.Slider(0, 1, label="IoU Threshold")
+                        y26_retina = gr.Checkbox(label="Retina Masks", info="Higher quality masks, slower inference")
+                    y26_btn = gr.Button("Run Inference", variant="primary")
+                with gr.Column():
+                    y26_output = gr.Image(type="pil", label="Result")
+                    y26_label = gr.Label(label="Classification Results", visible=False)
+
+            y26_task.change(
+                lambda t: (gr.update(visible=t != "Classification"), gr.update(visible=t == "Classification")),
+                y26_task,
+                [y26_output, y26_label],
+            )
+            gr.Examples(
+                examples=[
+                    [str(ASSETS / "bus.jpg"), "YOLO26-M", "Detection", 0.25, 0.45, True],
+                    [str(ASSETS / "bus.jpg"), "YOLO26-M", "Segmentation", 0.25, 0.45, True],
+                    [str(ASSETS / "zidane.jpg"), "YOLO26-M", "Pose", 0.25, 0.45, True],
+                    [str(OBB_IMAGE), "YOLO26-M", "OBB", 0.25, 0.45, True],
+                ],
+                inputs=[y26_image, y26_model, y26_task, y26_conf, y26_iou, y26_retina],
+                outputs=[y26_output, y26_label],
+                fn=predict_yolo26,
+                cache_examples=True,
+            )
+            y26_btn.click(predict_yolo26, [y26_image, y26_model, y26_task, y26_conf, y26_iou, y26_retina], [y26_output, y26_label])
+
+        with gr.Tab("YOLOE26 Open-Vocabulary"):
+            gr.Markdown("### Ultralytics YOLOE26: Open-Vocabulary Segmentation - Detect any object by text description")
+            with gr.Row():
+                with gr.Column():
+                    ye_image = gr.Image(type="pil", label="Upload Image", value=str(ASSETS / "bus.jpg"))
+                    with gr.Row():
+                        ye_model = gr.Dropdown(
+                            ["YOLOE-26N", "YOLOE-26S", "YOLOE-26M", "YOLOE-26L", "YOLOE-26X"], value="YOLOE-26L", label="Model"
+                        )
+                        ye_classes = gr.Textbox(value="person, bus, car", label="Classes", placeholder="person, dog, cat...")
+                    with gr.Accordion("Advanced Settings", open=False):
+                        ye_conf = gr.Slider(0, 1, value=0.2, label="Confidence Threshold")
+                        ye_retina = gr.Checkbox(value=True, label="Retina Masks", info="Higher quality masks, slower inference")
+                    ye_btn = gr.Button("Run Inference", variant="primary")
+                with gr.Column():
+                    ye_output = gr.Image(type="pil", label="Result")
+
+            gr.Examples(
+                examples=[
+                    [str(ASSETS / "bus.jpg"), "YOLOE-26L", "person, bus, car", 0.2, True],
+                    [str(ASSETS / "zidane.jpg"), "YOLOE-26L", "person, football, grass", 0.2, True],
+                ],
+                inputs=[ye_image, ye_model, ye_classes, ye_conf, ye_retina],
+                outputs=ye_output,
+                fn=predict_yoloe26,
+                cache_examples=True,
+            )
+            ye_btn.click(predict_yoloe26, [ye_image, ye_model, ye_classes, ye_conf, ye_retina], ye_output)
+
+if __name__ == "__main__":
+    demo.launch(theme=theme, allowed_paths=[str(ASSETS), str(ASSETS.parent)])