attempt to dynamically add cl tagger versions as they release

app.py

@@ -270,15 +270,35 @@ def visualize_predictions(image: Image.Image, predictions: Dict, threshold: floa
     buf.seek(0)
     return Image.open(buf)
 
-# --- Constants ---
+# --- Dynamic Model Discovery ---
 REPO_ID = "cella110n/cl_tagger"
-MODEL_OPTIONS = {
-    "cl_tagger_1_00": "cl_tagger_1_00/model_optimized.onnx",
-    "cl_tagger_1_01": "cl_tagger_1_01/model_optimized.onnx"
-}
-DEFAULT_MODEL = "cl_tagger_1_01"
 CACHE_DIR = "./model_cache"
 
+def discover_models(repo_id=REPO_ID):
+    api = HfApi()
+    files = api.list_repo_files(repo_id=repo_id)
+
+    model_options = {}
+    for f in files:
+        # look for ONNX models in subfolders
+        if f.endswith("model_optimized.onnx"):
+            # take the parent folder as model key
+            parts = f.split("/")
+            if len(parts) >= 2:
+                model_key = parts[0]  # e.g., "cl_tagger_1_01"
+                model_options[model_key] = f
+
+    if not model_options:
+        raise RuntimeError(f"No ONNX models found in {repo_id}")
+
+    # pick the latest model as default (sorted by name)
+    default_model = sorted(model_options.keys())[-1]
+
+    return model_options, default_model
+
+# Build dynamically
+MODEL_OPTIONS, DEFAULT_MODEL = discover_models()
+
 # --- Global variables for paths (initialized at startup) ---
 g_onnx_model_path = None
 g_tag_mapping_path = None