app.py

import streamlit as st
import tweepy


@st.cache(show_spinner=False, allow_output_mutation=True)
def load_model(model_name: str) -> SentenceTransformer:
    embedder = model_name
    return SentenceTransformer(embedder)


client = tweepy.Client(bearer_token=st.secrets["tw_bearer_token"])
model_to_use = {
    "English": "all-MiniLM-L12-v2",
    "Use all the ones you know (~15 lang)": "paraphrase-multilingual-MiniLM-L12-v2"
}

st.title("Tweet-SNEst")
st.write("Visualize tweets embeddings in 2D using colors for topics labels.")
col1, col2 = st.columns(2)
with col1:
    tw_user = st.text_input("Twitter handle", "huggingface")
with col2:
    sample = st.number_input("Maximum number of tweets to use", 1, 300, 100, 10)
    
expected_lang = st.radio(
    "What language should be assumed to be found?",
    ('English', 'Use all the ones you know (~15 lang)'))


usr = client.get_user(username=tw_user)
 
st.write(usr.data.id)