Update app.py

app.py

@@ -9,6 +9,7 @@ import insightface
 import numpy as np
 import onnxruntime as ort
 from PIL import Image
+import io
 
 TITLE = "insightface Person Detection"
 DESCRIPTION = "https://github.com/deepinsight/insightface/tree/master/examples/person_detection"
@@ -43,15 +44,24 @@ def extract_persons(image: np.ndarray, bboxes: np.ndarray) -> list[np.ndarray]:
     return person_images
 
 
+def convert_to_png(image: np.ndarray) -> bytes:
+    """Convert a NumPy image array to a PNG byte stream."""
+    pil_image = Image.fromarray(image)
+    buffer = io.BytesIO()
+    pil_image.save(buffer, format="PNG")
+    buffer.seek(0)
+    return buffer.read()
+
+
 detector = load_model()
 detector.prepare(-1, nms_thresh=0.5, input_size=(640, 640))
 
 
-def detect(image: np.ndarray) -> list[np.ndarray]:
+def detect(image: np.ndarray) -> list[bytes]:
     image = image[:, :, ::-1]  # RGB -> BGR
     bboxes = detect_person(image, detector)
     person_images = extract_persons(image, bboxes)  # Extract each person as a separate image
-    return [person_img[:, :, ::-1] for person_img in person_images]  # BGR -> RGB
+    return [convert_to_png(person_img[:, :, ::-1]) for person_img in person_images]  # BGR -> RGB
 
 
 examples = sorted(pathlib.Path("images").glob("*.jpg"))
@@ -59,7 +69,7 @@ examples = sorted(pathlib.Path("images").glob("*.jpg"))
 demo = gr.Interface(
     fn=detect,
     inputs=gr.Image(label="Input", type="numpy"),
-    outputs=gr.Gallery(label="Detected Persons"),  # Removed the postprocess argument
+    outputs=gr.Gallery(label="Detected Persons"),  # Display multiple images in a gallery
     examples=examples,
     cache_examples=False,  # Disable caching of examples
     examples_per_page=30,