Update app.py

app.py

@@ -68,6 +68,40 @@ except Exception as e:
 # --------------------------------------------------------------------------------------
 # Basics
 
+# Initialize device for model
+if torch.backends.mps.is_available():
+    RADIO_DEVICE = torch.device("mps")
+elif torch.cuda.is_available():
+    RADIO_DEVICE = torch.device("cuda")
+else:
+    RADIO_DEVICE = torch.device("cpu")
+
+# Global variables for C model
+radio_l_image_processor = None
+radio_l_model = None
+ai_detection_classifier = None
+
+
+# Preload the C model at startup
+def preload_models():
+    """Preload models at startup to improve response time"""
+    global radio_l_image_processor, radio_l_model
+
+    print("🔄 Preloading C model (4GB)...")
+    try:
+        hf_repo = os.getenv('MODEL_REPO', 'fallback')
+        if hf_repo and hf_repo != 'fallback':
+            from transformers import AutoModel, CLIPImageProcessor
+            radio_l_image_processor = CLIPImageProcessor.from_pretrained(hf_repo)
+            radio_l_model = AutoModel.from_pretrained(hf_repo, trust_remote_code=True)
+            radio_l_model = radio_l_model.to(RADIO_DEVICE)
+            radio_l_model.eval()
+            print("✅ C model preloaded successfully!")
+            return True
+    except Exception as e:
+        print(f"⚠️ Could not preload C model: {e}")
+    return False
+
 
 # Add current directory to path (for local modules if any)
 if os.getcwd() not in sys.path:
@@ -76,44 +110,8 @@ if os.getcwd() not in sys.path:
 # --------------------------------------------------------------------------------------
 # Hugging Face download (robust)
 # --------------------------------------------------------------------------------------
-try:
-    from huggingface_hub import hf_hub_download, HfHubHTTPError
-except Exception:
-    hf_hub_download = None
-    HfHubHTTPError = Exception
-
-HF_TOKEN = os.getenv("key")  # backwards compat
-# --------------------------------------------------------------------------------------
-print("DEBUG MODEL_REPO:", os.getenv("MODEL_REPO"))
-print("DEBUG MODEL_REPO_REVISION:", os.getenv("MODEL_REPO_REVISION"))
-print("DEBUG PRIVATE_REPO:", os.getenv("PRIVATE_REPO"))
-print("DEBUG HF token present:", bool(os.getenv("HUGGING_FACE_HUB_TOKEN") or os.getenv("HF_TOKEN") or os.getenv("key")))
-import torch, timm
-print("DEBUG torch:", torch.__version__)
-print("DEBUG timm:", timm.__version__)
-PRIVATE_REPO = os.getenv("PRIVATE_REPO") 
-huggingface_model_path = None
 
-def _try_hf_download(repo_id: str, filename: str, token: Optional[str]):
-    if hf_hub_download is None:
-        print("ℹ️ huggingface_hub not available; skipping HF download.")
-        return None
-    try:
-        return hf_hub_download(repo_id=repo_id, filename=filename, token=token)
-    except HfHubHTTPError as e:
-        print(f"⚠️ HF HTTP error: {e}")
-    except Exception as e:
-        print(f"⚠️ HF generic error: {e}")
-    return None
 
-if PRIVATE_REPO != "fallback":
-    huggingface_model_path = _try_hf_download(PRIVATE_REPO, "V1.pkl", HF_TOKEN)
-    if huggingface_model_path:
-        print(f"✅ Model downloaded: {huggingface_model_path}")
-    else:
-        print("⚠️ Could not download V1.pkl. Will try local path or demo mode.")
-else:
-    print("ℹ️ PRIVATE_REPO not set; will use local ./output/V1.pkl or demo mode.")
 
 # --------------------------------------------------------------------------------------
 # Paths / Devices
@@ -261,27 +259,7 @@ def run_damage_detection(pil_image: Image.Image, score_thresh: float = 0.5):
 # --------------------------------------------------------------------------------------
 # Stage 2: RADIO feature extractor + classifier
 # --------------------------------------------------------------------------------------
-def preload_models():
-    """Preload RADIO model at startup to improve response time (idempotent)."""
-    global image_processor, model, _preloaded
-    if _preloaded and image_processor is not None and model is not None:
-        return True
-    try:
-        hf_repo = os.getenv('MODEL_REPO')
-        if not hf_repo:
-            print("⚠️ MODEL_REPO not set → demo mode unless already loaded.")
-            return False
-        image_processor = CLIPImageProcessor.from_pretrained(hf_repo)
-        m = AutoModel.from_pretrained(hf_repo, trust_remote_code=True)
-        m.to(DEVICE).eval()
-        model = m
-        _preloaded = True
-        print("✅ RADIO model preloaded")
-        return True
-    except Exception as e:
-        print(f"❌ RADIO preload failed: {e}")
-        traceback.print_exc()
-        return False
+
 
 def load_ai_detection_classifier(model_path):
     """Load the AI detection classifier (joblib)."""