11

app.py

@@ -1,5 +1,44 @@
-import streamlit as st
+import gradio as gr
+import PIL.Image as Image
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+from ultralytics import ASSETS, YOLO
 
+model = None
+
+
+def predict_image(img, conf_threshold, iou_threshold, model_name):
+    """Predicts objects in an image using a YOLOv8 model with adjustable confidence and IOU thresholds."""
+    model = YOLO(model_name)
+    results = model.predict(
+        source=img,
+        conf=conf_threshold,
+        iou=iou_threshold,
+        show_labels=True,
+        show_conf=True,
+        imgsz=640,
+    )
+
+    for r in results:
+        im_array = r.plot()
+        im = Image.fromarray(im_array[..., ::-1])
+
+    return im
+
+
+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"),
+        gr.Radio(choices=["yolo11n", "yolo11s", "yolo11n-seg", "yolo11s-seg", "yolo11n-pose", "yolo11s-pose"], label="Model Name", value="yolo11n"),
+    ],
+    outputs=gr.Image(type="pil", label="Result"),
+    title="Ultralytics Gradio Application 🚀",
+    description="Upload images for inference. The Ultralytics YOLO11n model is used by default.",
+    examples=[
+        [ASSETS / "bus.jpg", 0.25, 0.45, "yolo11n.pt"],
+        [ASSETS / "zidane.jpg", 0.25, 0.45, "yolo11n.pt"],
+    ],
+)
+iface.launch(share=True)

requirements.txt

@@ -0,0 +1,6 @@
+gradio
+torch>=1.8.0
+torch>=1.8.0,!=2.4.0; sys_platform == 'win32'  # Windows CPU errors w/ 2.4.0 https://github.com/ultralytics/ultralytics/issues/15049
+torchvision>=0.9.0
+ultralytics>=8.3.0
+Pillow