Update app.py

app.py

@@ -9,7 +9,7 @@ import cv2
 import numpy as np
 from PIL import Image
 import gradio as gr
-from roboflow import Roboflow, RoboflowError
+from roboflow import Roboflow
 
 
 def parse_roboflow_url(url: str):
@@ -29,7 +29,7 @@ def convert_seg_to_bbox(api_key: str, dataset_url: str):
     """
     Download a segmentation dataset from Roboflow,
     convert masks → YOLOv8 bboxes,
-    and return (before, after) galleries + local YOLO‐dataset path + auto slug.
+    and return (before, after) galleries + local YOLO dataset path + auto slug.
     """
     rf = Roboflow(api_key=api_key)
     ws_name, seg_proj_slug, ver = parse_roboflow_url(dataset_url)
@@ -37,7 +37,7 @@ def convert_seg_to_bbox(api_key: str, dataset_url: str):
     dataset = version_obj.download("coco-segmentation")
     root = dataset.location
 
-    # --- find annotation JSON ---
+    # find the annotation JSON
     ann_file = None
     for dp, _, files in os.walk(root):
         for f in files:
@@ -62,14 +62,14 @@ def convert_seg_to_bbox(api_key: str, dataset_url: str):
     cat_ids = sorted(c["id"] for c in coco.get("categories", []))
     id_to_index = {cid: idx for idx, cid in enumerate(cat_ids)}
 
-    # --- prepare YOLOv8 folders ---
+    # prepare YOLOv8 folders
     out_root = tempfile.mkdtemp(prefix="yolov8_")
     img_out = os.path.join(out_root, "images")
     lbl_out = os.path.join(out_root, "labels")
     os.makedirs(img_out, exist_ok=True)
     os.makedirs(lbl_out, exist_ok=True)
 
-    # --- convert seg → bbox labels ---
+    # convert seg → bbox labels
     annos = {}
     for a in coco["annotations"]:
         pid = a["image_id"]
@@ -82,7 +82,7 @@ def convert_seg_to_bbox(api_key: str, dataset_url: str):
         line = f"{id_to_index[a['category_id']]} {cx/iw:.6f} {cy/ih:.6f} {w/iw:.6f} {h/ih:.6f}"
         annos.setdefault(pid, []).append(line)
 
-    # --- locate images and write label files ---
+    # locate images and write labels
     img_dir = None
     for dp, _, files in os.walk(root):
         if any(f.lower().endswith((".jpg", ".png", ".jpeg")) for f in files):
@@ -100,7 +100,7 @@ def convert_seg_to_bbox(api_key: str, dataset_url: str):
         with open(os.path.join(lbl_out, fname.rsplit(".", 1)[0] + ".txt"), "w") as lf:
             lf.write("\n".join(annos.get(pid, [])))
 
-    # --- build preview galleries ---
+    # build preview galleries
     before, after = [], []
     sample = random.sample(list(fname2id.keys()), min(5, len(fname2id)))
     for fn in sample:
@@ -118,14 +118,14 @@ def convert_seg_to_bbox(api_key: str, dataset_url: str):
             _, cxn, cyn, wnorm, hnorm = map(float, line.split())
             iw, ih = images_info[fname2id[fn]]["width"], images_info[fname2id[fn]]["height"]
             w0, h0 = int(wnorm * iw), int(hnorm * ih)
-            x0 = int(cxn * iw - w0 / 2)
-            y0 = int(cyn * ih - h0 / 2)
+            x0 = int(cxn * iw - w0/2)
+            y0 = int(cyn * ih - h0/2)
             cv2.rectangle(box_vis, (x0, y0), (x0 + w0, y0 + h0), (0, 255, 0), 2)
 
         before.append(Image.fromarray(seg_vis))
         after.append(Image.fromarray(box_vis))
 
-    # auto‐slug for detection project
+    # auto‐slug for the detection project
     detection_slug = f"{seg_proj_slug}-detection"
     return before, after, out_root, detection_slug
 
@@ -138,17 +138,17 @@ def upload_and_train_detection(api_key: str, project_slug: str, dataset_path: st
     rf = Roboflow(api_key=api_key)
     ws = rf.workspace()
 
-    # 1) get or create the detection project
+    # get or create the detection project
     try:
         proj = ws.project(project_slug)
-    except RoboflowError:
+    except Exception:
         proj = ws.create_project(
             project_name=project_slug,
             project_type="object-detection",
             project_license="MIT"
         )
 
-    # 2) upload the dataset
+    # upload the dataset
     ws.upload_dataset(
         dataset_path,
         proj.id,
@@ -159,17 +159,14 @@ def upload_and_train_detection(api_key: str, project_slug: str, dataset_path: st
         num_retries=0
     )
 
-    # 3) generate a new version (no extra aug/preproc by default)
-    new_v = proj.generate_version(settings={
-        "preprocessing": {},
-        "augmentation": {}
-    })
+    # generate a new version
+    new_v = proj.generate_version(settings={"preprocessing": {}, "augmentation": {}})
 
-    # 4) train it (fast)
+    # train (fast)
     version = proj.version(new_v)
     version.train(speed="fast")
 
-    # 5) return the hosted inference URL
+    # return the hosted inference URL
     m = version.model
     return f"{m['base_url']}{m['id']}?api_key={api_key}"