Create app.py

app.py

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+# --- Hugging Face fixes (add these 4 lines at the very top) ---
+import os
+if os.path.exists("/usr/bin/apt"):
+    import subprocess, sys
+    subprocess.check_call([sys.executable, "-m", "pip", "install", "insightface==0.7.3", "faiss-gpu", "deep-sort-realtime", "ultralytics", "onnxruntime-gpu", "--no-cache-dir"])
+# ---------------------------------------------------------------
+# SECUREFACE ID - FINAL UNIFIED APP
+# Privacy by default + Accurate Recognition + Persistent Tracking
+# Combines your two perfect apps into one
+
+import os
+import cv2
+import numpy as np
+import gradio as gr
+from ultralytics import YOLO
+import insightface
+from insightface.app import FaceAnalysis
+import faiss
+from deep_sort_realtime.deepsort_tracker import DeepSort
+from pathlib import Path
+
+# ==================== 1. MODELS & DATABASE ====================
+detector = YOLO("yolov8n-face.pt")
+recognizer = FaceAnalysis(name='buffalo_l', providers=['CUDAExecutionProvider'])
+recognizer.prepare(ctx_id=0, det_size=(640,640))
+
+# FAISS index for known faces
+KNOWN_EMBS_PATH = "known_embeddings.npy"
+KNOWN_NAMES_PATH = "known_names.npy"
+
+index = None
+known_names = []
+
+if os.path.exists(KNOWN_EMBS_PATH):
+    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.hnsw.efSearch = 16
+    index.add(embeddings.astype('float32'))
+    print(f"Loaded {len(known_names)} known people")
+
+# 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"
+
+# ==================== 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):
+    global unknown_counter, track_to_label
+
+    img = frame.copy()
+    h, w = img.shape[:2]
+
+    # Detect faces
+    results = detector(img, conf=0.4)[0]
+    detections = []
+    crops = []
+
+    for box in results.boxes:
+        x1, y1, x2, y2 = map(int, box.xyxy[0])
+        # Expand bbox
+        expand_w = int((x2 - x1) * (expand - 1) / 2)
+        expand_h = int((y2 - y1) * (expand - 1) / 2)
+        x1 = max(0, x1 - expand_w)
+        y1 = max(0, y1 - expand_h)
+        x2 = min(w, x2 + expand_w)
+        y2 = min(h, y2 + expand_h)
+
+        crop = img[y1:y2, x1:x2]
+        if crop.size == 0: continue
+        detections.append(([x1, y1, x2-x1, y2-y1], box.conf[0].item(), 'face'))
+        crops.append((crop, (x1, y1, x2, y2)))
+
+    # Track
+    tracks = tracker.update_tracks(detections, frame=img)
+
+    for track, (crop, (x1, y1, x2, y2)) in zip(tracks, crops):
+        if not track.is_confirmed(): continue
+        track_id = track.track_id
+
+        # Recognize only when needed
+        if track_id not in track_to_label or track.time_since_update % 15 == 0:
+            faces = recognizer.get(crop, max_num=1)
+            name = "Unknown"
+            if faces and index is not None:
+                emb = faces[0].normed_embedding.reshape(1, -1).astype('float32')
+                D, I = index.search(emb, k=1)
+                if D[0][0] < 0.45:
+                    name = known_names[I[0][0]]
+
+            if name == "Unknown":
+                if track_id not in track_to_label:
+                    unknown_counter += 1
+                    track_to_label[track_id] = f"Unknown_{unknown_counter:03d}"
+                name = track_to_label[track_id]
+            else:
+                track_to_label[track_id] = name
+
+        label = track_to_label.get(track_id, "Unknown")
+
+        # ALWAYS BLUR
+        face_region = img[y1:y2, x1:x2]
+        if blur_type == "gaussian":
+            k = int(min(x2-x1, y2-y1) * (intensity / 100)) | 1
+            blurred = cv2.GaussianBlur(face_region, (k, k), 0)
+        elif blur_type == "pixelate":
+            small = cv2.resize(face_region, (20, 20), interpolation=cv2.INTER_LINEAR)
+            blurred = cv2.resize(small, (x2-x1, y2-y1), interpolation=cv2.INTER_NEAREST)
+        else:  # solid
+            blurred = np.zeros_like(face_region)
+            blurred[:] = (0, 0, 0)
+
+        img[y1:y2, x1:x2] = blurred
+
+        # Optional: show label
+        if show_labels:
+            color = (0, 255, 0) if "Unknown" not in label else (0, 255, 255)
+            cv2.rectangle(img, (x1, y1), (x2, y2), color, 2)
+            cv2.putText(img, label, (x1, y1-10), cv2.FONT_HERSHEY_DUPLEX, 0.9, color, 2)
+
+    return img
+
+# ==================== 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"
+
+    faces = recognizer.get(face_image, max_num=1)
+    if not faces:
+        return "No face detected! Please try a clearer photo."
+
+    emb = faces[0].normed_embedding
+
+    # Save to disk
+    embs = np.load(KNOWN_EMBS_PATH) if os.path.exists(KNOWN_EMBS_PATH) else np.empty((0, 512))
+    names = np.load(KNOWN_NAMES_PATH, allow_pickle=True).tolist() if os.path.exists(KNOWN_NAMES_PATH) else []
+
+    embs = np.vstack([embs, emb])
+    names.append(name)
+
+    np.save(KNOWN_EMBS_PATH, embs)
+    np.save(KNOWN_NAMES_PATH, np.array(names))
+
+    # Rebuild index
+    dim = 512
+    index = faiss.IndexHNSWFlat(dim, 32)
+    index.add(embs.astype('float32'))
+    known_names = names
+
+    return f"Successfully enrolled: {name}"
+
+# ==================== 4. GRADIO UI ====================
+with gr.Blocks(title="SecureFace ID – Privacy-First Recognition", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# SecureFace ID")
+    gr.Markdown("**Every face is always blurred • Only authorized people are identified • Persistent tracking**")
+
+    with gr.Tab("Live Privacy Mode"):
+        with gr.Row():
+            inp = gr.Image(sources=["webcam", "upload"], streaming=True, height=600)
+            out = gr.Image(height=600)
+        with gr.Row():
+            blur_type = gr.Radio(["gaussian", "pixelate", "solid"], value="gaussian", label="Blur Style")
+            intensity = gr.Slider(10, 100, 40, label="Blur Intensity")
+            expand = gr.Slider(1.0, 2.0, 1.3, label="Blur Area Size")
+            show_labels = gr.Checkbox(True, label="Show Names / Unknown IDs")
+        inp.stream(process_frame, [inp, blur_type, intensity, expand, show_labels], out)
+
+    with gr.Tab("Enroll New Person"):
+        gr.Markdown("### Add someone to the database")
+        name_in = gr.Textbox(label="Full Name or ID", placeholder="Alice Smith")
+        img_in = gr.Image(label="Clear face photo", sources=["upload", "webcam"])
+        btn = gr.Button("Enroll Person", variant="primary")
+        status = gr.Markdown()
+        btn.click(enroll_person, [name_in, img_in], status)
+
+    with gr.Tab("Database"):
+        gr.Markdown(f"**{len(known_names)} people in database:**")
+        for name in known_names:
+            gr.Markdown(f"• {name}")
+
+demo.launch()