app.py

import streamlit as st
import transformers
from transformers import pipeline
from transformers import AutoTokenizer, AutoModelForMaskedLM
import pandas as pd
import numpy as np
import string


tokenizer = AutoTokenizer.from_pretrained("moussaKam/AraBART", max_length=128, padding=True, pad_to_max_length = True, truncation=True)
model = AutoModelForMaskedLM.from_pretrained("Hamda/test-1-finetuned-AraBART")

#@st.cache
def next_word(text, pipe):
    res_dict= {  
      'Word':[],
      'Score':[],
    }
    for e in pipe(text):
        if all(c not in list(string.punctuation) for c in e['token_str']):
            res_dict['Word'].append(e['token_str'])
            res_dict['Score'].append(e['score'])
    return res_dict

st.title("المساعدة اللغوية في التنبؤ بالمتلازمات والمتصاحبات والتعبيرات الاصطلاحية وتصحيحها")
default_value = "بيعت الأسلحة في السوق"
# sent is the variable holding the user's input
sent = st.text_area("مدخل", default_value, height=20)

st.checkbox('استعمال الرسم البياني', value=False)

text_st = sent+ ' <mask>'

pipe = pipeline("fill-mask", tokenizer=tokenizer, model=model, top_k=10)
dict_next_words = next_word(text_st, pipe)
df = pd.DataFrame.from_dict(dict_next_words)
df.reset_index(drop=True, inplace=True)

st.dataframe(df)


#st.table(df)