import streamlit as st
import logging

from transformers import AutoTokenizer, BertForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("AkshatSurolia/ICD-10-Code-Prediction")
model = BertForSequenceClassification.from_pretrained("AkshatSurolia/ICD-10-Code-Prediction")
config = model.config

from googletrans import Translator
translator = Translator()

text = st.text_area('enter some text!')
#text = "subarachnoid hemorrhage scalp laceration service: surgery major surgical or invasive"
#pipe = pipeline('sentiment-analysis')


if text:
    translated = translator.translate(text)
    encoded_input = tokenizer(translated.text, return_tensors='pt')
    output = model(**encoded_input)

    results = output.logits.detach().cpu().numpy()[0].argsort()[::-1][:5]
    rout = [ config.id2label[ids] for ids in results]
    st.write(rout)