Update app.py

app.py

@@ -12,9 +12,29 @@ face_mesh = mp_face_mesh.FaceMesh(static_image_mode=True, max_num_faces=1, refin
 
 def extract_features(image, landmarks):
     mean_intensity = np.mean(image)
+    h, w, _ = image.shape
+
     bbox_width = max(pt.x for pt in landmarks) - min(pt.x for pt in landmarks)
     bbox_height = max(pt.y for pt in landmarks) - min(pt.y for pt in landmarks)
-    return [mean_intensity, bbox_width, bbox_height]
+
+    # Compute facial region ratios (eye distance, nose length, jaw width, etc.)
+    def dist(p1, p2):
+        return ((p1.x - p2.x)**2 + (p1.y - p2.y)**2) ** 0.5
+
+    eye_dist = dist(landmarks[33], landmarks[263])  # between left and right eye
+    nose_len = dist(landmarks[1], landmarks[2]) + dist(landmarks[2], landmarks[98])  # bridge + tip
+    jaw_width = dist(landmarks[234], landmarks[454])
+
+    # Skin tone analysis from cheeks
+    left_cheek = landmarks[234]
+    right_cheek = landmarks[454]
+    cx1, cy1 = int(left_cheek.x * w), int(left_cheek.y * h)
+    cx2, cy2 = int(right_cheek.x * w), int(right_cheek.y * h)
+    skin_tone1 = np.mean(image[cy1-5:cy1+5, cx1-5:cx1+5]) if 5 <= cy1 < h-5 and 5 <= cx1 < w-5 else 0
+    skin_tone2 = np.mean(image[cy2-5:cy2+5, cx2-5:cx2+5]) if 5 <= cy2 < h-5 and 5 <= cx2 < w-5 else 0
+    avg_skin_tone = (skin_tone1 + skin_tone2) / 2
+
+    return [mean_intensity, bbox_width, bbox_height, eye_dist, nose_len, jaw_width, avg_skin_tone]
 
 def train_model(output_range):
     X = [[random.uniform(0.2, 0.5), random.uniform(0.05, 0.2), random.uniform(0.05, 0.2)] for _ in range(100)]