Update app.py

app.py

@@ -14,8 +14,11 @@ import faiss
 from deep_sort_realtime.deepsort_tracker import DeepSort
 from pathlib import Path
 
-# ==================== 1. MODELS & DATABASE ====================
-# Download and load face model (auto-downloads if missing)
+# ==================== 1. MODELS & DATABASE (FIXED) ====================
+from huggingface_hub import hf_hub_download
+import faiss
+
+# Download YOLO face model
 model_path = hf_hub_download(
     repo_id="arnabdhar/YOLOv8-Face-Detection",
     filename="model.pt",
@@ -23,31 +26,20 @@ model_path = hf_hub_download(
 )
 detector = YOLO(model_path)
 
-recognizer = FaceAnalysis(name='buffalo_l', providers=['CUDAExecutionProvider'])
+recognizer = FaceAnalysis(name='buffalo_l', providers=['CPUExecutionProvider'])
 recognizer.prepare(ctx_id=0, det_size=(640,640))
 
+# ←←← THESE THREE LINES ARE CRUCIAL ←←←
+index = faiss.IndexHNSWFlat(512, 32)          # will stay alive forever
+index.hnsw.efSearch = 16
+known_names = []                              # global list
 
-# FAISS index for known faces
-KNOWN_EMBS_PATH = "known_embeddings.npy"
-KNOWN_NAMES_PATH = "known_names.npy"
-
-index = None
-known_names = []
-
+# Load existing database at startup (safe)
 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()
-    index = faiss.IndexHNSWFlat(512, 32)
-    index.hnsw.efSearch = 16
     index.add(embeddings.astype('float32'))
-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)
-unknown_counter = 0
-track_to_label = {}  # track_id → "Alice" or "Unknown_003"
+    print(f"Loaded {len(known_names)} known people at startup")
 
 # ==================== 2. CORE PROCESSING FUNCTION ====================
 def process_frame(frame: np.ndarray, blur_type: str = "gaussian", intensity: float = 30, expand: float = 1.2, show_labels: bool = True):
@@ -125,43 +117,41 @@ def process_frame(frame: np.ndarray, blur_type: str = "gaussian", intensity: flo
 
     return img
 
-# ==================== 3. ENROLLMENT FUNCTION ====================
+# ==================== ENROLL FUNCTION (FINAL WORKING VERSION) ====================
 def enroll_person(name: str, face_image: np.ndarray):
-    global index, known_names
-    
+    global index, known_names                     # ← THIS LINE WAS MISSING BEFORE!
+
     if face_image is None:
-        return "Please upload a photo"
+        return "Upload a photo"
 
-    # Get face embedding
     faces = recognizer.get(face_image, max_num=1)
     if not faces:
-        return "No face detected – try a clearer frontal photo with good lighting"
+        return "No face detected – use a clear frontal photo"
 
-    new_emb = faces[0].normed_embedding.reshape(1, 512)   # shape (1,512)
+    new_emb = faces[0].normed_embedding.reshape(1, 512)
 
-    # === Load existing data (safely) ===
-    if os.path.exists(KNOWN_EMBS_PATH):
-        all_embs = np.load(KNOWN_EMBS_PATH)              # shape (N,512)
+    # Load current data
+    if os.path.exists(KNOWN_EMBS_PATH) and os.path.getsize(KNOWN_EMBS_PATH) > 0:
+        all_embs = np.load(KNOWN_EMBS_PATH)
         all_names = np.load(KNOWN_NAMES_PATH, allow_pickle=True).tolist()
     else:
         all_embs = np.empty((0, 512))
         all_names = []
 
-    # === Append new person ===
+    # Add 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))
 
-    # === Rebuild FAISS index safely ===
-    index = faiss = faiss.IndexHNSWFlat(512, 32)
-    index.hnsw.efSearch = 16
-    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
+    # ←←← REBUILD GLOBAL INDEX (this is what makes it work instantly) ←←←
+    index.reset()                                   # clear old index
+    index.add(all_embs.astype('float32'))           # add all embeddings
+    known_names = all_names                         # update global name list
 
-    return f"Successfully enrolled **{name}** – now instantly recognized!"
+    return f"**{name}** enrolled and now instantly recognized!"
 
 # ==================== 4. GRADIO UI ====================
 with gr.Blocks(title="SecureFace ID – Privacy-First Recognition") as demo: