Improve adapter selection and prompt alignment

app.py

@@ -25,8 +25,8 @@ HF_TOKEN = os.getenv("HF_TOKEN")
 BASE_MODEL_ID = "llava-hf/llava-1.5-7b-hf"
 ADAPTER_PATH = ROOT_DIR / "final-production-weights" / "best_model"
 ADAPTER_REPO_ID = "Werrewulf/tmos-dd-weights"
-ADAPTER_SUBFOLDER = os.getenv("ADAPTER_SUBFOLDER", "multimodal/checkpoint-5")
-PROMPT = "USER: <image>\nIs this image real or produced by AI?\nASSISTANT:"
+ADAPTER_SUBFOLDER = os.getenv("ADAPTER_SUBFOLDER", "")
+PROMPT = "USER: <image>\nIs this video real or produced by AI?\nASSISTANT:"
 TARGET_IMAGE_SIZE = 336
 THRESHOLD = 0.5
 
@@ -66,7 +66,7 @@ def load_model_and_processor():
         adapter_source = ADAPTER_REPO_ID
 
     dtype = select_torch_dtype()
-    print(f"Loading TMOS-DD model from {adapter_source} (subfolder={ADAPTER_SUBFOLDER}) with dtype={dtype}...")
+    print(f"Loading TMOS-DD model from {adapter_source} with dtype={dtype}...")
 
     base_model = TMOSClassifier(
         base_model_id=BASE_MODEL_ID,
@@ -76,9 +76,27 @@ def load_model_and_processor():
     )
     peft_kwargs = {"is_trainable": False, "token": HF_TOKEN}
     if adapter_source == ADAPTER_REPO_ID:
-        peft_kwargs["subfolder"] = ADAPTER_SUBFOLDER
+        candidate_subfolders = [
+            s for s in [ADAPTER_SUBFOLDER, "multimodal", "multimodal/checkpoint-5", "llava"] if s is not None
+        ]
+
+        last_error = None
+        for subfolder in candidate_subfolders:
+            try:
+                current_kwargs = dict(peft_kwargs)
+                if subfolder:
+                    current_kwargs["subfolder"] = subfolder
+                model = PeftModel.from_pretrained(base_model, adapter_source, **current_kwargs)
+                print(f"Loaded adapter from repo subfolder: '{subfolder or '.'}'")
+                break
+            except Exception as exc:
+                last_error = exc
+                continue
+        else:
+            raise RuntimeError(f"Failed to load adapter from repo {adapter_source} using known subfolders.") from last_error
+    else:
+        model = PeftModel.from_pretrained(base_model, adapter_source, **peft_kwargs)
 
-    model = PeftModel.from_pretrained(base_model, adapter_source, **peft_kwargs)
     model.eval()
 
     processor = AutoProcessor.from_pretrained(BASE_MODEL_ID, token=HF_TOKEN)