Update app.py

app.py

@@ -9,17 +9,14 @@ import cv2
 import numpy as np
 from PIL import Image
 import gradio as gr
-from roboflow import Roboflow, RoboflowError
+from roboflow import Roboflow  # removed RoboflowError, just import Roboflow
 
 
 def parse_roboflow_url(url: str):
-    """
-    Extract (workspace, project slug, version) from any Roboflow URL.
-    """
     parsed = urlparse(url)
     parts = parsed.path.strip('/').split('/')
-    ws       = parts[0]
-    proj     = parts[1]
+    ws   = parts[0]
+    proj = parts[1]
     try:
         ver = int(parts[-1])
     except ValueError:
@@ -28,14 +25,13 @@ def parse_roboflow_url(url: str):
 
 
 def convert_seg_to_bbox(api_key: str, dataset_url: str, split_ratios=(0.8, 0.1, 0.1)):
-    # --- download segmentation export
-    rf           = Roboflow(api_key=api_key)
+    rf            = Roboflow(api_key=api_key)
     workspace, proj_name, ver = parse_roboflow_url(dataset_url)
-    version_obj  = rf.workspace(workspace).project(proj_name).version(ver)
-    dataset      = version_obj.download("coco-segmentation")
-    root         = dataset.location
+    version_obj   = rf.workspace(workspace).project(proj_name).version(ver)
+    dataset       = version_obj.download("coco-segmentation")
+    root          = dataset.location
 
-    # --- find the COCO JSON
+    # find COCO JSON
     ann_file = None
     for dp, _, files in os.walk(root):
         for f in files:
@@ -52,7 +48,7 @@ def convert_seg_to_bbox(api_key: str, dataset_url: str, split_ratios=(0.8, 0.1,
     cat_ids     = sorted(c['id'] for c in coco.get('categories', []))
     id_to_index = {cid: idx for idx, cid in enumerate(cat_ids)}
 
-    # --- flatten + convert to YOLO bboxes
+    # flatten & convert
     out_root = tempfile.mkdtemp(prefix="yolov8_")
     flat_img = os.path.join(out_root, "flat_images")
     flat_lbl = os.path.join(out_root, "flat_labels")
@@ -76,7 +72,6 @@ def convert_seg_to_bbox(api_key: str, dataset_url: str, split_ratios=(0.8, 0.1,
         )
         annos.setdefault(img_id, []).append(line)
 
-    # --- map filenames to their disk paths
     name_to_id = {img['file_name']: img['id'] for img in coco['images']}
     file_paths = {}
     for dp, _, files in os.walk(root):
@@ -84,7 +79,6 @@ def convert_seg_to_bbox(api_key: str, dataset_url: str, split_ratios=(0.8, 0.1,
             if f in name_to_id:
                 file_paths[f] = os.path.join(dp, f)
 
-    # --- copy images and write YOLO .txt labels
     for fname, img_id in name_to_id.items():
         src = file_paths.get(fname)
         if not src:
@@ -93,7 +87,7 @@ def convert_seg_to_bbox(api_key: str, dataset_url: str, split_ratios=(0.8, 0.1,
         with open(os.path.join(flat_lbl, fname.rsplit('.',1)[0] + ".txt"), 'w') as lf:
             lf.write("\n".join(annos.get(img_id, [])))
 
-    # --- split into train/val/test
+    # split
     all_files = sorted(f for f in os.listdir(flat_img)
                        if f.lower().endswith(('.jpg','.png','.jpeg')))
     random.shuffle(all_files)
@@ -108,7 +102,6 @@ def convert_seg_to_bbox(api_key: str, dataset_url: str, split_ratios=(0.8, 0.1,
         "test":  all_files[n_train+n_valid:]
     }
 
-    # --- arrange into Roboflow‑friendly folder tree
     for split, files in splits.items():
         idir = os.path.join(out_root, "images", split)
         ldir = os.path.join(out_root, "labels", split)
@@ -124,7 +117,7 @@ def convert_seg_to_bbox(api_key: str, dataset_url: str, split_ratios=(0.8, 0.1,
     shutil.rmtree(flat_img)
     shutil.rmtree(flat_lbl)
 
-    # --- make a few before/after visual samples
+    # prepare visuals
     before, after = [], []
     sample = random.sample(list(name_to_id.keys()), min(5, len(name_to_id)))
     for fname in sample:
@@ -152,7 +145,6 @@ def convert_seg_to_bbox(api_key: str, dataset_url: str, split_ratios=(0.8, 0.1,
         before.append(Image.fromarray(seg_vis))
         after.append(Image.fromarray(box_vis))
 
-    # return samples + local folder + the two slugs we need downstream
     return before, after, out_root, proj_name + "-detection", workspace
 
 
@@ -167,11 +159,10 @@ def upload_and_train_detection(
     rf = Roboflow(api_key=api_key)
     ws = rf.workspace(workspace)
 
-    # --- get‑or‑create project
+    # get‑or‑create project by inspecting exception text
     try:
         proj = ws.project(project_slug)
-    except RoboflowError as e:
-        # only create if truly “not found”
+    except Exception as e:
         if "does not exist" in str(e):
             proj = ws.create_project(
                 project_slug,
@@ -182,7 +173,7 @@ def upload_and_train_detection(
         else:
             raise
 
-    # --- upload the new train/val/test
+    # upload & train
     ws.upload_dataset(
         dataset_path,
         project_slug,
@@ -190,7 +181,6 @@ def upload_and_train_detection(
         project_type=project_type
     )
 
-    # --- spin up a new version and start training
     version_num = proj.generate_version(settings={
         "augmentation": {},
         "preprocessing": {},
@@ -210,11 +200,9 @@ with gr.Blocks() as app:
     run_btn    = gr.Button("Convert to BBoxes")
     before_g   = gr.Gallery(columns=5, label="Before")
     after_g    = gr.Gallery(columns=5, label="After")
-
-    # hidden states
-    ds_state   = gr.Textbox(visible=False)  # local dataset folder
-    slug_state = gr.Textbox(visible=False)  # project‑slug e.g. "myproj-detection"
-    ws_state   = gr.Textbox(visible=False)  # workspace name
+    ds_state   = gr.Textbox(visible=False)
+    slug_state = gr.Textbox(visible=False)
+    ws_state   = gr.Textbox(visible=False)
 
     run_btn.click(
         convert_seg_to_bbox,