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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]
# 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 for {predicted_emotion}: {results[i][0]['score']:.4f}")
# Display scores for other categories
st.write(f"Emotion Scores for other categories (Image {i+1}):")
for label, score in zip(emotion_labels, results[i][0]["score"]):
if label.lower() != predicted_emotion.lower(): # Exclude the predicted emotion
st.write(f"{label}: {score:.4f}")
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 Image {i+1}:")
st.write(f"{predicted_emotion}: {result[0]['score']:.4f}")
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