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| import streamlit as st | |
| import pandas as pd | |
| from transformers import pipeline | |
| def get_model(model): | |
| return pipeline("fill-mask", model=model, top_k=100)#seto maximum of tokens to be retrieved after each inference to model | |
| HISTORY_WEIGHT = 100 # set history weight (if found any keyword from history, it will priorities based on its weight) | |
| st.caption("This is a simple auto-completion where the next token is predicted per probability and a weigh if appears in user's history") | |
| history_keyword_text = st.text_input("Enter users's history keywords (optional, i.e., 'Gates')", value="Gates") | |
| text = st.text_input("Enter a text for auto completion...", value='Where is Bill') | |
| model = st.selectbox("choose a model", ["roberta-base", "bert-base-uncased"]) | |
| data_load_state = st.text('Loading model...') | |
| nlp = get_model(model) | |
| if text: | |
| data_load_state = st.text('Inference to model...') | |
| result = nlp(text+' '+nlp.tokenizer.mask_token) | |
| data_load_state.text('') | |
| for index, r in enumerate(result): | |
| 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) |