done fixing path

routes/classification_routes.py

@@ -6,44 +6,37 @@ import numpy as np
 from PIL import Image
 
 try:
-    # TF-bundled Keras (common with TensorFlow installs)
-    from tensorflow.keras.applications import mobilenet_v2 as _mv2  # type: ignore
+    from tensorflow.keras.applications import mobilenet_v2 as _mv2
 except Exception:
-    # Standalone Keras 3
-    from keras.applications import mobilenet_v2 as _mv2  # type: ignore
+    from keras.applications import mobilenet_v2 as _mv2
 
-# --- Model preprocessing ---
 preprocess_input = _mv2.preprocess_input
 classification_bp = Blueprint('classification_bp', __name__)
 
 # ------------------------------------------------------------
 # Model setup and auto-download
 # ------------------------------------------------------------
-BASE_DIR = Path(__file__).resolve().parent.parent   # -> /app/
-MODEL_DIR = BASE_DIR / "model"
+MODEL_DIR = Path(os.getenv("MODEL_DIR", "/tmp/model"))
 os.makedirs(MODEL_DIR, exist_ok=True)
 
-# --- Hugging Face model URLs ---
 MODEL_URLS = {
     "model": "https://huggingface.co/Gwen01/ProctorVision-Models/resolve/main/cheating_mobilenetv2_final.keras",
     "threshold": "https://huggingface.co/Gwen01/ProctorVision-Models/resolve/main/best_threshold.npy"
 }
 
-# --- Download missing model files ---
-for name, url in MODEL_URLS.items():
-    dest = MODEL_DIR / os.path.basename(url)
-    if not dest.exists():
-        print(f"📥 Downloading {name} from {url} ...")
-        try:
-            r = requests.get(url)
-            r.raise_for_status()
-            with open(dest, "wb") as f:
-                f.write(r.content)
-            print(f"✅ Saved to {dest}")
-        except Exception as e:
-            print(f"⚠️ Failed to download {name}: {e}")
-
-# --- Load model ---
+MODEL_PATHS = {}
+for key, url in MODEL_URLS.items():
+    local_path = MODEL_DIR / Path(url).name
+    MODEL_PATHS[key] = str(local_path)
+    if not local_path.exists():
+        print(f"📥 Downloading {key} from Hugging Face…")
+        r = requests.get(url)
+        r.raise_for_status()
+        with open(local_path, "wb") as f:
+            f.write(r.content)
+        print(f"✅ Saved {key} → {local_path}")
+
+# Candidate filenames for compatibility
 CANDIDATES = [
     "cheating_mobilenetv2_final.keras",
     "mnv2_clean_best.keras",
@@ -51,6 +44,7 @@ CANDIDATES = [
     "mnv2_finetune_best.keras",
 ]
 
+
 model_path = next((MODEL_DIR / f for f in CANDIDATES if (MODEL_DIR / f).exists()), None)
 if model_path and model_path.exists():
     model = tf.keras.models.load_model(model_path, compile=False)