add code

app.py

@@ -0,0 +1,35 @@
+import gradio as gr
+import supervision as sv
+import numpy as np
+from gradio.components import Image, Textbox, Radio
+
+def plot_bounding_boxes(image, boxes, box_type):
+    x0, y0, x1, y1 = [int(i.strip()) for i in boxes.split(",")]
+
+    detections = sv.Detections(
+        xyxy=np.array([[x0, y0, x1, y1]]),
+        class_id=np.array([0]),
+        confidence=np.array([1.0]),
+    )
+    # convert to cv2
+    image = np.array(image)
+
+    bounding_box_annotator = sv.BoundingBoxAnnotator()
+    annotated_image = bounding_box_annotator.annotate(
+        scene=image, detections=detections)
+    
+    return annotated_image
+
+iface = gr.Interface(
+    fn=plot_bounding_boxes,
+    inputs=[
+        Image(type="pil", label="Image"),
+        Textbox(label="Bounding Boxes", lines=3, default="100,100,200,200"),
+        Radio(["xyxy", "xywh"], label="Bounding Box Type"),
+    ],
+    outputs=Image(type="pil"),
+    title="Plot Bounding Boxes",
+    description="Plot bounding boxes on an image. Useful for testing object detection models without writing bounding box code. Powered by [supervision](https://github.com/roboflow/supervision)."
+)
+
+iface.launch()

requirements.txt

@@ -0,0 +1 @@
+supervision