fix: 🐞 show files when image saved

Signed-off-by: Onuralp SEZER <[email protected]>

app.py

@@ -14,7 +14,7 @@ from mmengine.runner.amp import autocast
 from mmyolo.registry import RUNNERS
 from torchvision.ops import nms
 import supervision as sv
-import PIL.Image
+from PIL import Image
 import cv2
 
 import gradio as gr
@@ -23,10 +23,10 @@ import gradio as gr
 TITLE = """
 # YOLO-World-Seg
 
-This is a demo of zero-shot object detection and instance segmentation using 
-[YOLO-World](https://github.com/AILab-CVC/YOLO-World) 
+This is a demo of zero-shot object detection and instance segmentation using only
+[YOLO-World](https://github.com/AILab-CVC/YOLO-World) done via newest model YOLO-World-Seg.
 
-Powered by [Supervision](https://github.com/roboflow/supervision).
+Annototions Powered by [Supervision](https://github.com/roboflow/supervision).
 """
 
 EXAMPLES = [
@@ -62,11 +62,13 @@ def run_image(
         max_num_boxes=100,
         ):
     runner = load_runner()
-    with open("input.jpeg", "wb") as f:
-        f.write(input_image)
+    
+    image_path='./work_dirs/input.png'
+    os.makedirs('./work_dirs', exist_ok=True)
+    input_image.save(image_path)
             
     texts = [[t.strip()] for t in class_names.split(",")] + [[" "]]
-    data_info = runner.pipeline(dict(img_id=0, img_path="input.jpeg",
+    data_info = runner.pipeline(dict(img_id=0, img_path=image_path,
                                      texts=texts))
 
     data_batch = dict(
@@ -102,7 +104,8 @@ def run_image(
         in zip(detections.class_id, detections.confidence)
     ]
     
-    svimage = box_annotator.annotate(input_image, detections)
+    svimage = np.array(input_image)
+    svimage = box_annotator.annotate(svimage, detections)
     svimage = label_annotator.annotate(svimage, detections, labels)
     svimage = mask_annotator.annotate(svimage,detections)
     return svimage
@@ -142,7 +145,7 @@ with gr.Blocks() as demo:
     with gr.Tab(label="Image"):
         with gr.Row():
             input_image_component = gr.Image(
-                type='numpy',
+                type='pil',
                 label='Input Image'
             )
             output_image_component = gr.Image(