Update app.py

app.py

@@ -1,237 +1,243 @@
-import os, io, tempfile, warnings
-import numpy as np
-import gradio as gr
-
-# =========================
-# TensorFlow / Keras
-# =========================
-import tensorflow as tf
-from tensorflow.keras.applications.resnet50 import preprocess_input as resnet_preprocess
-
-# GPU config for TensorFlow (enable if available)
-gpus = tf.config.experimental.list_physical_devices("GPU")
-if gpus:
-    try:
-        for g in gpus:
-            tf.config.experimental.set_memory_growth(g, True)
-        # Mixed precision can speed up on modern GPUs
-        try:
-            tf.keras.mixed_precision.set_global_policy("mixed_float16")
-            print("[INFO] TF mixed precision enabled.")
-        except Exception as e:
-            print(f"[WARN] Could not enable mixed precision: {e}")
-    except Exception as e:
-        print(f"[WARN] Could not set memory growth: {e}")
-
-# =========================
-# Vision stack
-# =========================
-import cv2
-import torch
-from facenet_pytorch import MTCNN
-import dlib
-from imutils import face_utils
-from scipy.spatial import distance as dist
-
-warnings.filterwarnings("ignore")
-
-# =========================
-# Paths / Config
-# =========================
-VIDEO_MODEL_PATH = "models/video_model.h5"
-DLIB_LANDMARK_MODEL = "shape_predictor_68_face_landmarks.dat"
-
-IMG_SIZE = (224, 224)
-FRAME_STEP = 5             # every 5th frame
-NUM_MAX_FACES = 300        # cap on faces collected
-
-# Blink (EAR) features
-EAR_THRESHOLD = 0.25
-EAR_CONSEC_FRAMES = 3
-
-# Prediction threshold
-PRED_THRESHOLD = 0.5
-
-# =========================
-# Lazy-loaded state
-# =========================
-_video_model = None
-_mtcnn = None
-_dlib_detector = None
-_dlib_predictor = None
-
-# Torch / MTCNN device selection
-_torch_device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-if torch.cuda.is_available():
-    torch.backends.cudnn.benchmark = True
-    try:
-        torch.set_float32_matmul_precision("medium")
-    except Exception:
-        pass
-print(f"[INFO] PyTorch device: {_torch_device}")
-
-def lazy_load():
-    global _video_model, _mtcnn, _dlib_detector, _dlib_predictor
-
-    if _video_model is None:
-        if not os.path.exists(VIDEO_MODEL_PATH):
-            raise FileNotFoundError(f"Missing: {VIDEO_MODEL_PATH}")
-        # Allow tf to place on GPU if available
-        _video_model = tf.keras.models.load_model(VIDEO_MODEL_PATH)
-        print("[INFO] Video model loaded.")
-
-    if _mtcnn is None:
-        _mtcnn = MTCNN(keep_all=True, device=_torch_device, image_size=IMG_SIZE[0])
-        print(f"[INFO] MTCNN ready on {_torch_device}.")
-
-    if _dlib_detector is None or _dlib_predictor is None:
-        if not os.path.exists(DLIB_LANDMARK_MODEL):
-            raise FileNotFoundError(
-                f"Missing dlib predictor: {DLIB_LANDMARK_MODEL}. Place it beside app.py."
-            )
-        _dlib_detector = dlib.get_frontal_face_detector()
-        _dlib_predictor = dlib.shape_predictor(DLIB_LANDMARK_MODEL)
-        print("[INFO] dlib detector + predictor ready.")
-
-# =========================
-# VIDEO: faces + blink features
-# =========================
-def _eye_aspect_ratio(eye_pts):
-    A = dist.euclidean(eye_pts[1], eye_pts[5])
-    B = dist.euclidean(eye_pts[2], eye_pts[4])
-    C = dist.euclidean(eye_pts[0], eye_pts[3])
-    return (A + B) / (2.0 * C + 1e-9)
-
-def extract_faces_all(path):
-    """Sample every 5th frame, keep ALL faces per frame, resize to 224x224."""
-    cap = cv2.VideoCapture(path)
-    total = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
-    if total <= 0:
-        cap.release()
-        return None
-
-    faces = []
-    for idx in range(0, total, FRAME_STEP):
-        if len(faces) >= NUM_MAX_FACES:
-            break
-        cap.set(cv2.CAP_PROP_POS_FRAMES, idx)
-        ok, frame = cap.read()
-        if not ok:
-            break
-        rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-        boxes, _ = _mtcnn.detect(rgb)
-        if boxes is None:
-            continue
-        for (x1, y1, x2, y2) in boxes.astype(int):
-            x1, y1 = max(0, x1), max(0, y1)
-            x2, y2 = min(frame.shape[1], x2), min(frame.shape[0], y2)
-            crop = frame[y1:y2, x1:x2]
-            if crop.size > 0:
-                faces.append(cv2.resize(crop, IMG_SIZE))
-            if len(faces) >= NUM_MAX_FACES:
-                break
-    cap.release()
-    if not faces:
-        return None
-    return np.stack(faces, axis=0).astype(np.uint8)
-
-def blink_features_from_crops(faces):
-    """Compute EAR on cropped faces; denominator = #frames with valid EAR."""
-    (lStart, lEnd) = face_utils.FACIAL_LANDMARKS_IDXS["left_eye"]
-    (rStart, rEnd) = face_utils.FACIAL_LANDMARKS_IDXS["right_eye"]
-
-    ear_values = []
-    blink_counter = 0
-    total_blinks = 0
-
-    for face in faces:
-        gray = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY)
-        rects = _dlib_detector(gray, 0)
-        if len(rects) == 0:
-            continue
-        shape = _dlib_predictor(gray, rects[0])
-        shape = face_utils.shape_to_np(shape)
-
-        left_eye = shape[lStart:lEnd]
-        right_eye = shape[rStart:rEnd]
-        ear = 0.5 * (_eye_aspect_ratio(left_eye) + _eye_aspect_ratio(right_eye))
-        ear_values.append(ear)
-
-        if ear < EAR_THRESHOLD:
-            blink_counter += 1
-        else:
-            if blink_counter >= EAR_CONSEC_FRAMES:
-                total_blinks += 1
-            blink_counter = 0
-
-    n_ear = len(ear_values)
-    if n_ear == 0:
-        return np.array([0, 0.0, 0.0], dtype=np.float32)
-
-    blink_freq = total_blinks / float(n_ear)
-    ear_var = float(np.var(np.array(ear_values, dtype=np.float32)))
-    return np.array([total_blinks, blink_freq, ear_var], dtype=np.float32)
-
-def predict_video_prob(video_path):
-    faces = extract_faces_all(video_path)
-    if faces is None or len(faces) == 0:
-        return None, "No faces detected."
-
-    blink_feats = blink_features_from_crops(faces)
-    # Tile to per-face samples
-    tiled = np.tile(blink_feats, (faces.shape[0], 1)).astype(np.float32)
-    imgs = resnet_preprocess(faces.astype(np.float32))
-
-    # Batch size heuristic (bigger if GPU is present)
-    has_tf_gpu = len(tf.config.experimental.list_physical_devices("GPU")) > 0
-    bs = 128 if has_tf_gpu else 32
-
-    preds = []
-    for i in range(0, imgs.shape[0], bs):
-        p = _video_model.predict([imgs[i:i+bs], tiled[i:i+bs]], verbose=0)
-        preds.append(p.reshape(-1))
-    return float(np.mean(np.concatenate(preds, axis=0))), None
-
-def to_verdict(score):
-    return "DEEPFAKE" if score >= PRED_THRESHOLD else "REAL"
-
-# =========================
-# Inference entry
-# =========================
-def run_inference(video_file):
-    lazy_load()
-    if video_file is None:
-        return None, None, "Please upload a video file."
-
-    video_prob, vmsg = predict_video_prob(video_file)
-    if video_prob is None:
-        return None, None, vmsg or "Unable to process the video."
-
-    verdict = f"VIDEO ONLY: {to_verdict(video_prob)}"
-    fmt = lambda x: None if x is None else round(float(x), 4)
-    return fmt(video_prob), verdict, None
-
-# =========================
-# Gradio UI
-# =========================
-with gr.Blocks(title="Deepfake Detector — Video Only (GPU-ready)") as demo:
-    gr.Markdown(
-        "### 🎭 Deepfake Detector — **Video Only**\n"
-        "- **Compute**: Uses GPU automatically if available (PyTorch MTCNN, TensorFlow model; mixed precision on TF).\n"
-        "- **Video path**: samples every 5th frame, keeps **all** faces, resizes to 224×224.\n"
-        "- **Features**: ResNet50 preprocessing + blink EAR features on cropped faces via dlib.\n"
-        f"- **Threshold**: {PRED_THRESHOLD} (≥ means DEEPFAKE).\n"
-    )
-    with gr.Row():
-        video_in = gr.Video(label="Video")
-    go = gr.Button("Analyze")
-    with gr.Row():
-        v_out = gr.Number(label="Video probability (deepfake)", precision=4)
-        verdict_out = gr.Textbox(label="Verdict", interactive=False)
-    msg_out = gr.Textbox(label="Message / Warnings", interactive=False)
-
-    go.click(run_inference, inputs=[video_in], outputs=[v_out, verdict_out, msg_out])
-
-if __name__ == "__main__":
-    lazy_load()
-    demo.launch(server_name="0.0.0.0", server_port=7860)
+import os, io, tempfile, warnings
+import numpy as np
+import gradio as gr
+import os
+
+# 🔧 Override Hugging Face’s wrong environment variable
+os.environ["OMP_NUM_THREADS"] = "4"
+os.environ["MKL_NUM_THREADS"] = "4"
+
+
+# =========================
+# TensorFlow / Keras
+# =========================
+import tensorflow as tf
+from tensorflow.keras.applications.resnet50 import preprocess_input as resnet_preprocess
+
+# GPU config for TensorFlow (enable if available)
+gpus = tf.config.experimental.list_physical_devices("GPU")
+if gpus:
+    try:
+        for g in gpus:
+            tf.config.experimental.set_memory_growth(g, True)
+        # Mixed precision can speed up on modern GPUs
+        try:
+            tf.keras.mixed_precision.set_global_policy("mixed_float16")
+            print("[INFO] TF mixed precision enabled.")
+        except Exception as e:
+            print(f"[WARN] Could not enable mixed precision: {e}")
+    except Exception as e:
+        print(f"[WARN] Could not set memory growth: {e}")
+
+# =========================
+# Vision stack
+# =========================
+import cv2
+import torch
+from facenet_pytorch import MTCNN
+import dlib
+from imutils import face_utils
+from scipy.spatial import distance as dist
+
+warnings.filterwarnings("ignore")
+
+# =========================
+# Paths / Config
+# =========================
+VIDEO_MODEL_PATH = "models/video_model.h5"
+DLIB_LANDMARK_MODEL = "shape_predictor_68_face_landmarks.dat"
+
+IMG_SIZE = (224, 224)
+FRAME_STEP = 5             # every 5th frame
+NUM_MAX_FACES = 300        # cap on faces collected
+
+# Blink (EAR) features
+EAR_THRESHOLD = 0.25
+EAR_CONSEC_FRAMES = 3
+
+# Prediction threshold
+PRED_THRESHOLD = 0.5
+
+# =========================
+# Lazy-loaded state
+# =========================
+_video_model = None
+_mtcnn = None
+_dlib_detector = None
+_dlib_predictor = None
+
+# Torch / MTCNN device selection
+_torch_device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+if torch.cuda.is_available():
+    torch.backends.cudnn.benchmark = True
+    try:
+        torch.set_float32_matmul_precision("medium")
+    except Exception:
+        pass
+print(f"[INFO] PyTorch device: {_torch_device}")
+
+def lazy_load():
+    global _video_model, _mtcnn, _dlib_detector, _dlib_predictor
+
+    if _video_model is None:
+        if not os.path.exists(VIDEO_MODEL_PATH):
+            raise FileNotFoundError(f"Missing: {VIDEO_MODEL_PATH}")
+        # Allow tf to place on GPU if available
+        _video_model = tf.keras.models.load_model(VIDEO_MODEL_PATH)
+        print("[INFO] Video model loaded.")
+
+    if _mtcnn is None:
+        _mtcnn = MTCNN(keep_all=True, device=_torch_device, image_size=IMG_SIZE[0])
+        print(f"[INFO] MTCNN ready on {_torch_device}.")
+
+    if _dlib_detector is None or _dlib_predictor is None:
+        if not os.path.exists(DLIB_LANDMARK_MODEL):
+            raise FileNotFoundError(
+                f"Missing dlib predictor: {DLIB_LANDMARK_MODEL}. Place it beside app.py."
+            )
+        _dlib_detector = dlib.get_frontal_face_detector()
+        _dlib_predictor = dlib.shape_predictor(DLIB_LANDMARK_MODEL)
+        print("[INFO] dlib detector + predictor ready.")
+
+# =========================
+# VIDEO: faces + blink features
+# =========================
+def _eye_aspect_ratio(eye_pts):
+    A = dist.euclidean(eye_pts[1], eye_pts[5])
+    B = dist.euclidean(eye_pts[2], eye_pts[4])
+    C = dist.euclidean(eye_pts[0], eye_pts[3])
+    return (A + B) / (2.0 * C + 1e-9)
+
+def extract_faces_all(path):
+    """Sample every 5th frame, keep ALL faces per frame, resize to 224x224."""
+    cap = cv2.VideoCapture(path)
+    total = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+    if total <= 0:
+        cap.release()
+        return None
+
+    faces = []
+    for idx in range(0, total, FRAME_STEP):
+        if len(faces) >= NUM_MAX_FACES:
+            break
+        cap.set(cv2.CAP_PROP_POS_FRAMES, idx)
+        ok, frame = cap.read()
+        if not ok:
+            break
+        rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        boxes, _ = _mtcnn.detect(rgb)
+        if boxes is None:
+            continue
+        for (x1, y1, x2, y2) in boxes.astype(int):
+            x1, y1 = max(0, x1), max(0, y1)
+            x2, y2 = min(frame.shape[1], x2), min(frame.shape[0], y2)
+            crop = frame[y1:y2, x1:x2]
+            if crop.size > 0:
+                faces.append(cv2.resize(crop, IMG_SIZE))
+            if len(faces) >= NUM_MAX_FACES:
+                break
+    cap.release()
+    if not faces:
+        return None
+    return np.stack(faces, axis=0).astype(np.uint8)
+
+def blink_features_from_crops(faces):
+    """Compute EAR on cropped faces; denominator = #frames with valid EAR."""
+    (lStart, lEnd) = face_utils.FACIAL_LANDMARKS_IDXS["left_eye"]
+    (rStart, rEnd) = face_utils.FACIAL_LANDMARKS_IDXS["right_eye"]
+
+    ear_values = []
+    blink_counter = 0
+    total_blinks = 0
+
+    for face in faces:
+        gray = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY)
+        rects = _dlib_detector(gray, 0)
+        if len(rects) == 0:
+            continue
+        shape = _dlib_predictor(gray, rects[0])
+        shape = face_utils.shape_to_np(shape)
+
+        left_eye = shape[lStart:lEnd]
+        right_eye = shape[rStart:rEnd]
+        ear = 0.5 * (_eye_aspect_ratio(left_eye) + _eye_aspect_ratio(right_eye))
+        ear_values.append(ear)
+
+        if ear < EAR_THRESHOLD:
+            blink_counter += 1
+        else:
+            if blink_counter >= EAR_CONSEC_FRAMES:
+                total_blinks += 1
+            blink_counter = 0
+
+    n_ear = len(ear_values)
+    if n_ear == 0:
+        return np.array([0, 0.0, 0.0], dtype=np.float32)
+
+    blink_freq = total_blinks / float(n_ear)
+    ear_var = float(np.var(np.array(ear_values, dtype=np.float32)))
+    return np.array([total_blinks, blink_freq, ear_var], dtype=np.float32)
+
+def predict_video_prob(video_path):
+    faces = extract_faces_all(video_path)
+    if faces is None or len(faces) == 0:
+        return None, "No faces detected."
+
+    blink_feats = blink_features_from_crops(faces)
+    # Tile to per-face samples
+    tiled = np.tile(blink_feats, (faces.shape[0], 1)).astype(np.float32)
+    imgs = resnet_preprocess(faces.astype(np.float32))
+
+    # Batch size heuristic (bigger if GPU is present)
+    has_tf_gpu = len(tf.config.experimental.list_physical_devices("GPU")) > 0
+    bs = 128 if has_tf_gpu else 32
+
+    preds = []
+    for i in range(0, imgs.shape[0], bs):
+        p = _video_model.predict([imgs[i:i+bs], tiled[i:i+bs]], verbose=0)
+        preds.append(p.reshape(-1))
+    return float(np.mean(np.concatenate(preds, axis=0))), None
+
+def to_verdict(score):
+    return "DEEPFAKE" if score >= PRED_THRESHOLD else "REAL"
+
+# =========================
+# Inference entry
+# =========================
+def run_inference(video_file):
+    lazy_load()
+    if video_file is None:
+        return None, None, "Please upload a video file."
+
+    video_prob, vmsg = predict_video_prob(video_file)
+    if video_prob is None:
+        return None, None, vmsg or "Unable to process the video."
+
+    verdict = f"VIDEO ONLY: {to_verdict(video_prob)}"
+    fmt = lambda x: None if x is None else round(float(x), 4)
+    return fmt(video_prob), verdict, None
+
+# =========================
+# Gradio UI
+# =========================
+with gr.Blocks(title="Deepfake Detector — Video Only (GPU-ready)") as demo:
+    gr.Markdown(
+        "### 🎭 Deepfake Detector — **Video Only**\n"
+        "- **Compute**: Uses GPU automatically if available (PyTorch MTCNN, TensorFlow model; mixed precision on TF).\n"
+        "- **Video path**: samples every 5th frame, keeps **all** faces, resizes to 224×224.\n"
+        "- **Features**: ResNet50 preprocessing + blink EAR features on cropped faces via dlib.\n"
+        f"- **Threshold**: {PRED_THRESHOLD} (≥ means DEEPFAKE).\n"
+    )
+    with gr.Row():
+        video_in = gr.Video(label="Video")
+    go = gr.Button("Analyze")
+    with gr.Row():
+        v_out = gr.Number(label="Video probability (deepfake)", precision=4)
+        verdict_out = gr.Textbox(label="Verdict", interactive=False)
+    msg_out = gr.Textbox(label="Message / Warnings", interactive=False)
+
+    go.click(run_inference, inputs=[video_in], outputs=[v_out, verdict_out, msg_out])
+
+if __name__ == "__main__":
+    lazy_load()
+    demo.launch(server_name="0.0.0.0", server_port=7860)