Update app.py

app.py

@@ -4,27 +4,50 @@ import numpy as np
 import math
 import json
 from PIL import Image
-
-# ── MediaPipe setup ──
-mp_face_mesh = mp.solutions.face_mesh
-face_mesh = mp_face_mesh.FaceMesh(
-    static_image_mode=True,
-    max_num_faces=1,
-    refine_landmarks=True,
-    min_detection_confidence=0.4
+from mediapipe.tasks import python as mp_python
+from mediapipe.tasks.python import vision as mp_vision
+import urllib.request
+import os
+
+# ── Download model if not cached ──
+MODEL_PATH = "/tmp/face_landmarker.task"
+MODEL_URL  = "https://storage.googleapis.com/mediapipe-models/face_landmarker/face_landmarker/float16/1/face_landmarker.task"
+
+if not os.path.exists(MODEL_PATH):
+    print("Downloading face landmarker model...")
+    urllib.request.urlretrieve(MODEL_URL, MODEL_PATH)
+    print("Model downloaded.")
+
+# ── Build FaceLandmarker ──
+base_options = mp_python.BaseOptions(
+    model_asset_path=MODEL_PATH,
+    delegate=mp_python.BaseOptions.Delegate.CPU
+)
+options = mp_vision.FaceLandmarkerOptions(
+    base_options=base_options,
+    output_face_blendshapes=False,
+    output_facial_transformation_matrixes=False,
+    num_faces=1,
+    min_face_detection_confidence=0.4,
+    min_face_presence_confidence=0.4
 )
+landmarker = mp_vision.FaceLandmarker.create_from_options(options)
+print("FaceLandmarker ready.")
+
 
 def analyse_face(image):
     try:
-        img_rgb = np.array(image.convert("RGB"))
-        h, w = img_rgb.shape[:2]
+        # Convert PIL to MediaPipe Image
+        img_rgb  = np.array(image.convert("RGB"))
+        h, w     = img_rgb.shape[:2]
+        mp_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=img_rgb)
 
-        results = face_mesh.process(img_rgb)
+        result = landmarker.detect(mp_image)
 
-        if not results.multi_face_landmarks:
+        if not result.face_landmarks or len(result.face_landmarks) == 0:
             return json.dumps({"error": "No face detected. Please upload a clear, front-facing photo."})
 
-        lm = results.multi_face_landmarks[0].landmark
+        lm = result.face_landmarks[0]   # list of NormalizedLandmark
 
         def dist(a, b):
             return math.sqrt(
@@ -32,12 +55,11 @@ def analyse_face(image):
                 ((lm[a].y - lm[b].y) * h) ** 2
             )
 
-        # Reference distance
         face_ref = dist(33, 263)
         if face_ref == 0:
             return json.dumps({"error": "Could not measure face. Please try a different photo."})
 
-        # Age estimation
+        # ── Age estimation ──
         face_height  = dist(10, 152)
         lower_face_h = dist(168, 152)
         lower_ratio  = lower_face_h / face_height if face_height > 0 else 0.52
@@ -49,10 +71,10 @@ def analyse_face(image):
         nasolabial = ((dist(50, 61) + dist(280, 291)) / 2) / face_ref
 
         forehead_idx = [10,109,67,103,54,21,162,127,234,338,297,332,284,251,389,356,454]
-        z_vals   = [lm[i].z for i in forehead_idx]
-        z_mean   = sum(z_vals) / len(z_vals)
-        z_var    = sum((z - z_mean) ** 2 for z in z_vals) / len(z_vals)
-        texture  = math.sqrt(abs(z_var)) * 100
+        z_vals  = [lm[i].z for i in forehead_idx]
+        z_mean  = sum(z_vals) / len(z_vals)
+        z_var   = sum((z - z_mean) ** 2 for z in z_vals) / len(z_vals)
+        texture = math.sqrt(abs(z_var)) * 100
 
         age_raw = (20
             + (lower_ratio  - 0.52) * 120
@@ -63,14 +85,14 @@ def analyse_face(image):
         age_mid   = max(18, min(72, round(age_raw)))
         age_range = f"{max(18, age_mid - 4)}\u2013{age_mid + 4}"
 
-        # Wrinkle score
+        # ── Wrinkle score ──
         wrinkle = round(max(1.0, min(9.9, 1 + texture * 18 + (age_mid - 18) * 0.10)), 1)
 
-        # Elasticity score
-        cheek_sag = ((dist(116, 61) + dist(345, 291)) / 2) / face_ref
+        # ── Elasticity score ──
+        cheek_sag  = ((dist(116, 61) + dist(345, 291)) / 2) / face_ref
         elasticity = round(max(1.0, min(9.9, 10 - (cheek_sag - 0.55) * 18 - (age_mid - 18) * 0.10)), 1)
 
-        # Jawline score
+        # ── Jawline score ──
         jaw_pts = [234,93,132,58,172,136,150,149,176,148,152,377,400,378,379,365,397,288,361,323,454]
         jaw_dev = 0.0
         for i in range(1, len(jaw_pts) - 1):
@@ -83,7 +105,7 @@ def analyse_face(image):
         jaw_dev /= len(jaw_pts)
         jawline = round(max(1.0, min(9.9, 10 - jaw_dev * 0.8 - (age_mid - 18) * 0.09)), 1)
 
-        age_factor    = round(max(0, min(1, (age_mid - 18) / 54)), 3)
+        age_factor    = round(max(0.0, min(1.0, (age_mid - 18) / 54)), 3)
         years_younger = max(3, round(age_factor * 14 + 2))
 
         landmarks_out = [{"x": float(l.x), "y": float(l.y), "z": float(l.z)} for l in lm]
@@ -102,7 +124,8 @@ def analyse_face(image):
         })
 
     except Exception as e:
-        return json.dumps({"error": str(e)})
+        import traceback
+        return json.dumps({"error": str(e), "trace": traceback.format_exc()})
 
 
 iface = gr.Interface(