app.py

import streamlit as st
# from transformers import pipeline
from deepface import DeepFace
import numpy as np
from PIL import Image

# pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")

st.title("Your Emotions? Or Nah?")
# st.title("Hot Dog? Or Not?")

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:
    # make two columns
    col1, col2 = st.columns(2)

    # capture image
    image = Image.open(file_name)
    # to display in in column 1
    col1.image(image, use_column_width=True)

    # capture image data for deepface
    image_data = np.array(image)
    # capture predictions from deepface
    predictions = DeepFace.analyze(image_data, actions=['emotion'])['emotion']
    # predictions = pipeline(image)

    # to display in column 2
    col2.header("Emotion Probabilities")
    # for p in predictions:
    for emotion in predictions:
        # col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%")
        col2.subheader(f"{emotion}")