deepface ?

app.py

@@ -1,20 +1,26 @@
 import streamlit as st
-from transformers import pipeline
+# from transformers import pipeline
+from deepface import DeepFace
 from PIL import Image
 
-pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
+# pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
 
-st.title("Hot Dog? Or Not?")
+st.title("Your Emotions? Or Nah?")
+# st.title("Hot Dog? Or Not?")
 
-file_name = st.file_uploader("Upload a hot dog candidate image")
+file_name = st.file_uploader("Upload a photo of your face.")
+# file_name = st.file_uploader("Upload a hot dog candidate image")
 
 if file_name is not None:
     col1, col2 = st.columns(2)
 
     image = Image.open(file_name)
     col1.image(image, use_column_width=True)
-    predictions = pipeline(image)
+    predictions = DeepFace.analyze(image, actions=['emotion'])
+    # predictions = pipeline(image)
 
     col2.header("Probabilities")
-    for p in predictions:
-        col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%")
+    # for p in predictions:
+    for emotion in predictions['emotion']:
+        # col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%")
+        col2.subheader(f"{emotion.keys()}: {emotion.values()}")