import streamlit as st
from gensim.models import KeyedVectors

st.title('Historical Word Embeddings')

st.write("Welcome!")
st.write("This is an interactive web app that allows users to explore how the meaning of words change over time. Use the sidebar on the left to navigate.")
st.write("Please wait while the data is loading:")

@st.experimental_memo
def load_data():
    models_all = {
        1810: KeyedVectors.load('./data/vectors1810.kv'),
        1840: KeyedVectors.load('./data/vectors1840.kv'),
        1870: KeyedVectors.load('./data/vectors1870.kv'),
        1900: KeyedVectors.load('./data/vectors1900.kv'),
        1930: KeyedVectors.load('./data/vectors1930.kv'),
        1960: KeyedVectors.load('./data/vectors1960.kv'),
        1990: KeyedVectors.load('./data/vectors1990.kv')
    }
    return models_all

if 'models_all' not in st.session_state:
    st.session_state['models_all'] = load_data()

st.write("Data loaded!")

st.write("Creator: Simon Walo")
st.write("Data source: https://nlp.stanford.edu/projects/histwords/ (SGNS All English (1800s-1990s))")