import streamlit as st from transformers import pipeline @st.cache_resource(show_spinner="Loading model...") def load_pipe(): pipe = pipeline(task="text-classification", model="cnicu/tweet_emotions_classifier") return pipe def classify_emotion(text: str, pipe: pipeline) -> str: prediction = pipe(text) return prediction st.title("Tweet emotions classification") text = st.text_area("Tweet to classify", label_visibility='hidden') if st.button("Classify tweet", disabled= text == ''): with st.spinner("In progress..."): prediction = classify_emotion(text, load_pipe()) st.success(f"Tweet's emotion: **{prediction[0]['label']}**")