app.py

import streamlit as st
from transformers import pipeline
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

today = datetime.datetime.now()
next_year = today.year + 1
jan_1 = datetime.date(next_year, 1, 1)
dec_31 = datetime.date(next_year, 12, 31)

d = st.date_input(
    "Select the date range",
    (jan_1, datetime.date(next_year, 1, 7)),
    jan_1,
    dec_31,
    format="MM.DD.YYYY",
)

tokenizer = AutoTokenizer.from_pretrained("nickmuchi/sec-bert-finetuned-finance-classification")
model = AutoModelForSequenceClassification.from_pretrained("nickmuchi/sec-bert-finetuned-finance-classification")


pipe = pipeline("text-classification", model=model, tokenizer=tokenizer, device=device)
text = st.text_area("Enter some text")

if text:
    out = pipe(text)
    st.json(out)