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import streamlit as st
import pandas as pd
from streamlit import cli as stcli
from transformers import pipeline
from sentence_transformers import SentenceTransformer, util
import sys
HISTORY_WEIGHT = 100 # set history weight (if found any keyword from history, it will priorities based on its weight)
@st.cache(allow_output_mutation=True)
def get_model(model):
return pipeline("fill-mask", model=model, top_k=20)#set the maximum of tokens to be retrieved after each inference to model
def main(nlp, semantic_model):
data_load_state = st.text('Inference to model...')
result = nlp(text+' '+nlp.tokenizer.mask_token)
data_load_state.text('')
sem_list=[semantic_text.strip()]
if len(semantic_text):
predicted_seq=[rec['sequence'] for rec in result]
predicted_embeddings = semantic_model.encode(predicted_seq, convert_to_tensor=True)
semantic_history_embeddings = semantic_model.encode(sem_list, convert_to_tensor=True)
cosine_scores = util.cos_sim(predicted_embeddings, semantic_history_embeddings)
for index, r in enumerate(result):
if len(semantic_text):
if len(r['token_str'])>2: #skip spcial chars such as "?"
result[index]['score']+=float(sum(cosine_scores[index]))*HISTORY_WEIGHT
if r['token_str'].lower().strip() in history_keyword_text.lower().strip() and len(r['token_str'].lower().strip())>1:
#found from history, then increase the score of tokens
result[index]['score']*=HISTORY_WEIGHT
#sort the results
df=pd.DataFrame(result).sort_values(by='score', ascending=False)
# show the results as a table
st.table(df)
# print(df)
if __name__ == '__main__':
if st._is_running_with_streamlit:
st.markdown("""
# Introduction
This is an example of an auto-complete approach where the next token suggested based on users's history Keyword match & Semantic similarity of users's history (log).
The next token is predicted per probability and a weight if it is appeared in keyword user's history or there is a similarity to semantic user's history
""")
history_keyword_text = st.text_input("Enter users's history <keywords match> (optional, i.e., 'Gates')", value="")
text = st.text_input("Enter a text for auto completion...", value='Where is Bill')
semantic_text = st.text_input("Enter users's history <semantic> (optional, i.e., 'Microsoft or President')", value="Microsoft")
model = st.selectbox("Choose a model", ["roberta-base", "bert-base-uncased"])
data_load_state = st.text('Loading model...')
semantic_model = SentenceTransformer('all-MiniLM-L6-v2')
nlp = get_model(model)
main(nlp, semantic_model)
else:
sys.argv = ['streamlit', 'run', sys.argv[0]]
sys.exit(stcli.main()) |