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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)
# 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)
# Display thumbnail images alongside file names and sizes in the sidebar
if uploaded_images:
for idx, img in enumerate(uploaded_images):
image = Image.open(img)
# Generate unique keys for image display and checkbox
image_key = f"{img.name}_image_{idx}"
checkbox_key = f"{img.name}_checkbox_{idx}"
st.sidebar.image(image, caption=f"{img.name} {img.size / 1024.0:.1f} KB", width=40, key=image_key)
st.sidebar.checkbox(f"Select {img.name}", value=False, key=checkbox_key)
# Collect selected images based on checkbox input
selected_images = []
for idx, img in enumerate(uploaded_images):
# Generate a unique key for each checkbox
checkbox_key = f"{img.name}_checkbox_{idx}"
selected = st.sidebar.checkbox(f"Select {img.name}", value=False, key=checkbox_key)
if selected:
selected_images.append(Image.open(img))
if st.button("Predict Emotions") and selected_images:
if len(selected_images) == 2:
# Predict emotion for each selected image using the pipeline
results = [pipe(image) for image in selected_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(selected_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: {uploaded_images[i].name}") # Display original file name
# Display the keys and values of all results
st.write("Keys and Values of all results:")
col1, col2 = st.columns(2)
for i, result in enumerate(results):
col = col1 if i == 0 else col2
col.write(f"Keys and Values of results[{i}]:")
for res in result:
label = res["label"]
score = res["score"]
col.write(f"{label}: {score:.4f}")
else:
# Predict emotion for each selected image using the pipeline
results = [pipe(image) for image in selected_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(selected_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
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