Add auto model downloader from Hugging Face Models

.gitignore

@@ -3,6 +3,3 @@ model/
 *.keras
 *.h5
 *.npy
-!model/
-!model/*.keras
-!model/*.npy

routes/classification_routes.py

@@ -17,11 +17,33 @@ preprocess_input = _mv2.preprocess_input
 classification_bp = Blueprint('classification_bp', __name__)
 
 # ------------------------------------------------------------
-# Model setup
+# Model setup and auto-download
 # ------------------------------------------------------------
-BASE_DIR  = Path(__file__).resolve().parent.parent   # -> /app/server/
+BASE_DIR = Path(__file__).resolve().parent.parent   # -> /app/
 MODEL_DIR = BASE_DIR / "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 ---
 CANDIDATES = [
     "cheating_mobilenetv2_final.keras",
     "mnv2_clean_best.keras",
@@ -37,11 +59,12 @@ else:
     model = None
     print(f"⚠️ No model found in {MODEL_DIR}. Put one of: {CANDIDATES}")
 
+# --- Load threshold ---
 thr_file = MODEL_DIR / "best_threshold.npy"
 THRESHOLD = float(np.load(thr_file)[0]) if thr_file.exists() else 0.555
 print(f"📊 Using decision threshold: {THRESHOLD:.3f}")
 
-# Input shape
+# --- Input shape ---
 if model is not None:
     H, W = model.input_shape[1:3]
 else: