Update app.py

app.py

@@ -33,17 +33,17 @@ def convert_seg_to_bbox(api_key: str, dataset_url: str):
     dataset = version_obj.download("coco-segmentation")
     root = dataset.location  # e.g. "/home/user/app/YourProject"
 
-    # 2) find the train JSON (anything with "train" in the name)
+    # 2) search for any JSON file with "train" in its name
     ann_file = None
-    for dp, _, files in os.walk(root):
-        for fname in files:
-            if fname.lower().endswith(".json") and "train" in fname.lower():
-                ann_file = os.path.join(dp, fname)
+    for dirpath, _, filenames in os.walk(root):
+        for fname in filenames:
+            if 'train' in fname.lower() and fname.lower().endswith('.json'):
+                ann_file = os.path.join(dirpath, fname)
                 break
         if ann_file:
             break
     if ann_file is None:
-        raise FileNotFoundError(f"Could not locate any '*train*.json' under {root}")
+        raise FileNotFoundError(f"No JSON file containing 'train' found under {root}")
 
     # 3) load COCO annotations
     with open(ann_file, 'r') as f:
@@ -80,11 +80,11 @@ def convert_seg_to_bbox(api_key: str, dataset_url: str):
         )
         annos.setdefault(img_id, []).append(line)
 
-    # 7) locate your train-images folder (first folder under root with any .jpg/.png)
+    # 7) locate your train-images folder (first folder under root with any image files)
     train_img_dir = None
-    for dp, _, files in os.walk(root):
-        if any(f.lower().endswith((".jpg", ".png", ".jpeg")) for f in files):
-            train_img_dir = dp
+    for dirpath, _, files in os.walk(root):
+        if any(f.lower().endswith(('.jpg', '.png', '.jpeg')) for f in files):
+            train_img_dir = dirpath
             break
     if train_img_dir is None:
         raise FileNotFoundError(f"No image files found under {root}")
@@ -107,7 +107,7 @@ def convert_seg_to_bbox(api_key: str, dataset_url: str):
         src = os.path.join(train_img_dir, fname)
         img = cv2.cvtColor(cv2.imread(src), cv2.COLOR_BGR2RGB)
 
-        # draw polys
+        # draw segmentation polygons
         seg_vis = img.copy()
         img_id = name_to_id[fname]
         for anno in coco['annotations']:
@@ -116,7 +116,7 @@ def convert_seg_to_bbox(api_key: str, dataset_url: str):
             pts = np.array(anno['segmentation'][0], dtype=np.int32).reshape(-1, 2)
             cv2.polylines(seg_vis, [pts], True, (255, 0, 0), 2)
 
-        # draw boxes
+        # draw bounding boxes
         box_vis = img.copy()
         for line in annos.get(img_id, []):
             _, cxn, cyn, wnorm, hnorm = map(float, line.split())
@@ -135,10 +135,9 @@ def convert_seg_to_bbox(api_key: str, dataset_url: str):
 # --- Gradio app ---
 with gr.Blocks() as app:
     gr.Markdown("# Segmentation → YOLOv8 Converter")
-    api_input = gr.Textbox(label="Roboflow API Key", type="password")
-    url_input = gr.Textbox(label="Roboflow Dataset URL (Segmentation)")
-    run_btn   = gr.Button("Convert")
-
+    api_input      = gr.Textbox(label="Roboflow API Key", type="password")
+    url_input      = gr.Textbox(label="Roboflow Dataset URL (Segmentation)")
+    run_btn        = gr.Button("Convert")
     before_gallery = gr.Gallery(label="Before (Segmentation)", columns=5, height="auto")
     after_gallery  = gr.Gallery(label="After (Bounding Boxes)", columns=5, height="auto")
 
@@ -149,5 +148,4 @@ with gr.Blocks() as app:
     )
 
 if __name__ == "__main__":
-    # set share=True if you need a public link
     app.launch()