app.py

import streamlit as st
from PIL import Image
from transformers import pipeline

# Create an image classification pipeline with scores
pipe = pipeline("image-classification", model="trpakov/vit-face-expression", top_k=None)

# Define emotion labels
#emotion_labels = ["Neutral", "Sad", "Angry", "Surprised", "Happy"]

# Streamlit app
st.title("Emotion Recognition with vit-face-expression")

# Slider example
x = st.slider('Select a value')
st.write(f"{x} squared is {x * x}")

# Upload images
uploaded_images = st.file_uploader("Upload images", type=["jpg", "png"], accept_multiple_files=True)

if st.button("Predict Emotions") and uploaded_images:
    if len(uploaded_images) == 2:
        # Open the uploaded images
        images = [Image.open(img) for img in uploaded_images]
        file_names = [img.name for img in uploaded_images]  # Get file names

        # Predict emotion for each image using the pipeline
        results = [pipe(image) for image in images]

        # Display images and predicted emotions side by side
        col1, col2 = st.columns(2)
        for i in range(2):
            predicted_class = results[i][0]["label"]
            predicted_emotion = predicted_class.split("_")[-1].capitalize()
            col = col1 if i == 0 else col2
            col.image(images[i], caption=f"Predicted emotion: {predicted_emotion}", use_column_width=True)
            col.write(f"Emotion Scores: {predicted_emotion}: {results[i][0]['score']:.4f}")
            col.write(f"Original File Name: {file_names[i]}")  # Display original file name
    else:
        # Open the uploaded images
        images = [Image.open(img) for img in uploaded_images]

        # Predict emotion for each image using the pipeline
        results = [pipe(image) for image in images]

        # Display images and predicted emotions
        for i, result in enumerate(results):
            predicted_class = result[0]["label"]
            predicted_emotion = predicted_class.split("_")[-1].capitalize()
            st.image(images[i], caption=f"Predicted emotion: {predicted_emotion}", use_column_width=True)
            st.write(f"Emotion Scores for #{i+1} Image")
            st.write(f"{predicted_emotion}: {result[0]['score']:.4f}")
            st.write(f"Original File Name: {uploaded_images[i].name}")  # Display original file name