Update app.py

app.py

@@ -11,7 +11,9 @@ import math
 import torch
 from torch import nn
 from PIL import Image
+from feat import Detector
 from feat.utils.io import get_resource_path
+from feat.pretrained import get_pretrained_models
 
 CLASS_LABELS = ['angry', 'disgust', 'fear', 'happy', 'neutral', 'sad', 'surprise']
 
@@ -20,7 +22,31 @@ def _get_resource_path():
 
 get_resource_path = _get_resource_path
 
-from feat import Detector
+def _get_pretrained_models(face_model, landmark_model, au_model, emotion_model, facepose_model, verbose):
+    return (
+        face_model,
+        landmark_model,
+        au_model,
+        emotion_model,
+        facepose_model,
+    )
+get_pretrained_models = _get_pretrained_models
+
+
+def _calculate_md5(fpath: str, chunk_size: int = 1024 * 1024) -> str:
+    # Setting the `usedforsecurity` flag does not change anything about the functionality, but indicates that we are
+    # not using the MD5 checksum for cryptography. This enables its usage in restricted environments like FIPS. Without
+    # it torchvision.datasets is unusable in these environments since we perform a MD5 check everywhere.
+    if sys.version_info >= (3, 9):
+        md5 = hashlib.md5(usedforsecurity=True)
+    else:
+        md5 = hashlib.md5()
+    with open(fpath, "rb") as f:
+        while chunk := f.read(chunk_size):
+            md5.update(chunk)
+    return md5.hexdigest()
+
+
 
 os.environ["TWILIO_ACCOUNT_SID"] = "ACf1e76f3fd6e9cbca940decc4ed443c20"
 os.environ["TWILIO_AUTH_TOKEN"] = "5cadf5cc7120dd995f11b3dc57e46d52"
@@ -148,7 +174,7 @@ st.markdown("""
     </style>
     """, unsafe_allow_html=True)
 
-detector = Detector(face_model="retinaface", landmark_model= "pfld", au_model = "xgb", emotion_model="resmasknet")
+detector = Detector(face_model="retinaface", landmark_model= "pfld", au_model="xgb", facepose_model="img2pose", emotion_model="resmasknet")
 source = "Webcam"
 recog = True