Update app.py

app.py

@@ -34,14 +34,15 @@ KNOWN_NAMES_PATH = "known_names.npy"
 index = None
 known_names = []
 
-if os.path.exists(KNOWN_EMBS_PATH):
+if os.path.exists(KNOWN_EMBS_PATH) and os.path.getsize(KNOWN_EMBS_PATH) > 0:
     embeddings = np.load(KNOWN_EMBS_PATH)
     known_names = np.load(KNOWN_NAMES_PATH, allow_pickle=True).tolist()
-    dim = embeddings.shape[1]
-    index = faiss.IndexHNSWFlat(dim, 32)
+    index = faiss.IndexHNSWFlat(512, 32)
     index.hnsw.efSearch = 16
     index.add(embeddings.astype('float32'))
-    print(f"Loaded {len(known_names)} known people")
+else:
+    index = faiss.IndexHNSWFlat(512, 32)   # empty but valid index
+    known_names = []
 
 # Tracker for persistent IDs
 tracker = DeepSort(max_age=30, n_init=3, max_cosine_distance=0.4, nn_budget=None, embedder_gpu=True)
@@ -127,40 +128,40 @@ def process_frame(frame: np.ndarray, blur_type: str = "gaussian", intensity: flo
 # ==================== 3. ENROLLMENT FUNCTION ====================
 def enroll_person(name: str, face_image: np.ndarray):
     global index, known_names
+    
     if face_image is None:
-        return "Please upload a clear face photo"
+        return "Please upload a photo"
 
+    # Get face embedding
     faces = recognizer.get(face_image, max_num=1)
     if not faces:
-        return "No face detected! Try a clearer photo."
+        return "No face detected – try a clearer frontal photo with good lighting"
 
-    emb = faces[0].normed_embedding
+    new_emb = faces[0].normed_embedding.reshape(1, 512)   # shape (1,512)
 
-    # Load existing data (or create new)
-    if not os.path.exists(KNOWN_EMBS_PATH):
+    # === Load existing data (safely) ===
+    if os.path.exists(KNOWN_EMBS_PATH):
+        all_embs = np.load(KNOWN_EMBS_PATH)              # shape (N,512)
+        all_names = np.load(KNOWN_NAMES_PATH, allow_pickle=True).tolist()
+    else:
         all_embs = np.empty((0, 512))
         all_names = []
-    else:
-        all_embs = np.load(KNOWN_EMBS_PATH)
-        all_names = np.load(KNOWN_NAMES_PATH, allow_pickle=True).tolist()
 
-    # Append new person
-    all_embs = np.vstack([all_embs, emb])
+    # === Append new person ===
+    all_embs = np.vstack([all_embs, new_emb])
     all_names.append(name)
 
-    # Save to disk
+    # === Save to disk ===
     np.save(KNOWN_EMBS_PATH, all_embs)
     np.save(KNOWN_NAMES_PATH, np.array(all_names))
 
-    # ←←← THIS IS THE CRUCIAL PART ←←←
-    # Rebuild FAISS index immediately
-    index = faiss.IndexHNSWFlat(512, 32)
+    # === Rebuild FAISS index safely ===
+    index = faiss = faiss.IndexHNSWFlat(512, 32)
     index.hnsw.efSearch = 16
-    index.add(all_embs.astype('float32'))
-    known_names = all_names
-    # ←←← END ←←←
+    index.add(all_embs.astype('float32'))    # ← this line used to crash if all_embs was empty
+    known_names = all_names                  # refresh the global list too
 
-    return f"Successfully enrolled: **{name}** → now instantly recognized!"
+    return f"Successfully enrolled **{name}** – now instantly recognized!"
 
 # ==================== 4. GRADIO UI ====================
 with gr.Blocks(title="SecureFace ID – Privacy-First Recognition") as demo: