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import streamlit as st
import transformers
from transformers import pipeline
from transformers import AutoTokenizer, AutoModelForMaskedLM
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
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':[],
}
res=pipe(text)
for e in res:
res_dict['Word'].append(e['token_str'])
res_dict['Score'].append(e['score'])
return res_dict
st.title("Predict Next Word")
st.write("Use our model to expand your query based on the DB content")
default_value = "التاريخ هو تحليل و"
# sent is the the variable holding the user's input
sent = st.text_area("Text", default_value, height = 30)
sent += ' <mask>'
pipe = pipeline("fill-mask", tokenizer = tokenizer, model = model)
dict_next_words = next_word(sent, pipe)
df = pd.DataFrame.from_dict(dict_next_words)
df.reset_index(drop=True, inplace=True)
#design the graph theme
sns.set_theme(style="whitegrid")
sns.set_color_codes("pastel")
fig = sns.barplot(data=df, orient = 'h')
st.pyplot(fig)
#st.table(df) |