app.py

import streamlit as st
import torch
import numpy as np
import views
from resources import load_corrector, load_data, load_model_and_tokenizer

use_cpu = not torch.cuda.is_available()
device = "cpu" if use_cpu else "cuda"

df = load_data()

encoder, tokenizer = load_model_and_tokenizer(device)

corrector = load_corrector()

# Caching the precomputed embeddings since they are stored locally and large
@st.cache_data
def load_embeddings():
    return np.load("syac-title-embeddings.npy")

embeddings = load_embeddings()


tab1, tab2 = st.tabs(["plot", "diffs"])

with tab1:
    views.plot()

with tab2:
    views.diffs()