Update app.py

app.py

@@ -12,22 +12,16 @@ 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)
-    def dist(p1, p2):
-        return ((p1.x - p2.x)**2 + (p1.y - p2.y)**2) ** 0.5
-    eye_dist = dist(landmarks[33], landmarks[263])
-    nose_len = dist(landmarks[1], landmarks[2]) + dist(landmarks[2], landmarks[98])
-    jaw_width = dist(landmarks[234], landmarks[454])
-    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]
+    red_channel = image[:, :, 2]
+    green_channel = image[:, :, 1]
+    blue_channel = image[:, :, 0]
+    total_pixels = image.shape[0] * image.shape[1]
+
+    red_percent = 100 * np.sum(red_channel > green_channel) / total_pixels
+    green_percent = 100 * np.sum(green_channel > red_channel) / total_pixels
+    blue_percent = 100 * np.sum(blue_channel > red_channel) / total_pixels
+
+    return [red_percent, green_percent, blue_percent]
 
 def train_model(output_range):
     X = [[random.uniform(0.2, 0.5), random.uniform(0.05, 0.2), random.uniform(0.05, 0.2),