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import streamlit as st
import transformers
from transformers import pipeline
from transformers import AutoTokenizer, AutoModelForMaskedLM
import pandas as pd
import numpy as np
tokenizer = AutoTokenizer.from_pretrained("moussaKam/AraBART", padding= True, truncation=True, max_length=128)
model = AutoModelForMaskedLM.from_pretrained("moussaKam/AraBART")
#@st.cache
def next_word(text, pipe):
res_dict= {
'Word':[],
'Score':[],
}
for e in pipe(text):
res_dict['Word'].append(e['token_str'])
res_dict['Score'].append(e['score'])
return res_dict
st.title("Predict Next Word")
st.write("Expand your query by leveraging various models")
default_value = "التاريخ هو تحليل و"
# sent is the variable holding the user's input
sent = st.text_area("Input", default_value, height=30)
if len(sent)>20:
text_st = sent[-20:]
text_st += ' <mask>'
else:
text_st = sent+ ' <mask>'
pipe = pipeline("fill-mask", tokenizer=tokenizer, model=model)
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.bar_chart(df)
#st.dataframe(df)
#st.bar_chart(df)
#st.table(df) |