fix

src/backend.py

@@ -133,33 +133,30 @@ def pattern_match(patterns, source_list):
 
 def _backend_routine():
     # List only the text classification models
-    rl_models = [(model.modelId, model.sha) for model in API.list_models(filter=["reinforcement-learning"])]
+    rl_models = API.list_models(filter=["reinforcement-learning"])
     logger.info(f"Found {len(rl_models)} RL models")
-    dataset = load_dataset(
-        RESULTS_REPO, split="train", download_mode="force_redownload", verification_mode="no_checks"
-    )
-    evaluated_models = [("/".join([x["user_id"], x["model_id"]]), x["sha"]) for x in dataset]
-    pending_models = list(set(rl_models) - set(evaluated_models))
-    pending_and_compatible_models = []
-    for repo_id, sha in pending_models:
-        try:
-            siblings = API.model_info(repo_id, revision="main").siblings
-        except Exception:
-            continue
-        filenames = [sib.rfilename for sib in siblings]
+
+    compatible_models = []
+    for model in rl_models:
+        filenames = [sib.rfilename for sib in model.siblings]
         if "agent.pt" in filenames:
-            pending_and_compatible_models.append((repo_id, sha))
+            compatible_models.append((model.modelId, model.sha))
 
-    logger.info(f"Found {len(pending_and_compatible_models)} compatible pending models")
+    logger.info(f"Found {len(compatible_models)} compatible models")
+
+    dataset = load_dataset(RESULTS_REPO, split="train", download_mode="force_redownload", verification_mode="no_checks")
+    evaluated_models = [("/".join([x["user_id"], x["model_id"]]), x["sha"]) for x in dataset]
+    pending_models = list(set(compatible_models) - set(evaluated_models))
+    logger.info(f"Found {len(pending_models)} pending models")
 
-    if len(pending_and_compatible_models) == 0:
+    if len(pending_models) == 0:
         return None
 
     # Shuffle the dataset
-    random.shuffle(pending_and_compatible_models)
+    random.shuffle(pending_models)
 
     # Select a random model
-    repo_id, sha = pending_and_compatible_models.pop()
+    repo_id, sha = pending_models.pop()
     user_id, model_id = repo_id.split("/")
     row = {"model_id": model_id, "user_id": user_id, "sha": sha}