app.py

import streamlit as st
from sentence_transformers import SentenceTransformer
from langdetect import detect, DetectorFactory

st.set_page_config(page_title="Simple Text Analysis", layout="wide")

@st.cache_resource
def load_model():
    return SentenceTransformer('distiluse-base-multilingual-cased-v1')

DetectorFactory.seed = 0
model = load_model()

st.title("Simple Text Analysis")
user_input = st.text_area("Enter your text here:")

if st.button("Analyze"):
    if user_input:
        try:
            lang = detect(user_input)
            st.write(f"Detected language: {lang}")
            
            embedding = model.encode(user_input)
            st.write(f"Text embedding shape: {embedding.shape}")
            st.write("First few values of the embedding:")
            st.write(embedding[:5])
        except Exception as e:
            st.error(f"An error occurred: {str(e)}")
    else:
        st.warning("Please enter some text to analyze.")

st.sidebar.title("About")
st.sidebar.info("This is a simple text analysis app.")