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import streamlit as st
import pandas as pd
#from streamlit import cli as stcli
from streamlit.web import cli as stcli
from streamlit import runtime
from transformers import pipeline
from sentence_transformers import SentenceTransformer, util
import sys
HISTORY_WEIGHT = 80 # set history weight (if found any keyword from history, it will priorities based on its weight)
@st.cache(allow_output_mutation=True, suppress_st_warning=True)
def get_model(model):
return pipeline("fill-mask", model=model, top_k=5)#s5t the maximum of tokens to be retrieved after each inference to model
def hash_func(inp):
return True
@st.cache(allow_output_mutation=True, suppress_st_warning=True)
def loading_models(model='roberta-base'):
return get_model(model), SentenceTransformer('paraphrase-multilingual-MiniLM-L12-v2')#'all-mpnet-base-v2')#'all-MiniLM-L6-v2')
@st.cache(allow_output_mutation=True,
suppress_st_warning=True,
hash_funcs={'tokenizers.Tokenizer': hash_func, 'tokenizers.AddedToken': hash_func})
def infer(text):
# global nlp
return nlp(text+' '+nlp.tokenizer.mask_token)
@st.cache(allow_output_mutation=True,
suppress_st_warning=True,
hash_funcs={'tokenizers.Tokenizer': hash_func, 'tokenizers.AddedToken': hash_func})
def sim(predicted_seq, sem_list):
return semantic_model.encode(predicted_seq, convert_to_tensor=True), \
semantic_model.encode(sem_list, convert_to_tensor=True)
@st.cache(allow_output_mutation=True,
suppress_st_warning=True,
hash_funcs={'tokenizers.Tokenizer': hash_func, 'tokenizers.AddedToken': hash_func})
def main(text,semantic_text,history_keyword_text):
global semantic_model, data_load_state
data_load_state.text('Inference from model...')
result = infer(text)
sem_list=[semantic_text.strip()]
data_load_state.text('Checking similarity...')
if len(semantic_text):
predicted_seq=[rec['sequence'] for rec in result]
predicted_embeddings, semantic_history_embeddings = sim(predicted_seq, sem_list)
cosine_scores = util.cos_sim(predicted_embeddings, semantic_history_embeddings)
data_load_state.text('similarity check completed...')
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
data_load_state.text('Score updated...')
#sort the results
df=pd.DataFrame(result).sort_values(by='score', ascending=False)
return df
if __name__ == '__main__':
#if st._is_running_with_streamlit:
if runtime.exists():
st.markdown("""
# Auto-Complete
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.
## Source
Forked from **[mbahrami/Auto-Complete_Semantic](https://huggingface.co/spaces/mbahrami/Auto-Complete_Semantic)** with *[osanseviero/fork_a_repo](https://huggingface.co/spaces/osanseviero/fork_a_repo)*.
## Disclaimer
Additionally, we include facebook/xlm-v-base model (it includes Guarani during pre-training),
for comparison reasons.
""")
history_keyword_text = st.text_input("Enter users's history <Keywords Match> (optional, i.e., 'Premio Cervantes')", value="")
semantic_text = st.text_input("Enter users's history <Semantic> (optional, i.e., 'hai')", value="hai")
text = st.text_input("Enter a text for auto completion...", value="Augusto Roa Bastos ha'e kuimba'e arandu")
model = st.selectbox("Choose a model",
["mmaguero/gn-bert-tiny-cased", "mmaguero/gn-bert-small-cased",
"mmaguero/gn-bert-base-cased", "mmaguero/gn-bert-large-cased",
"mmaguero/multilingual-bert-gn-base-cased", "mmaguero/beto-gn-base-cased",
"facebook/xlm-v-base"])
data_load_state = st.text('1.Loading model ...')
nlp, semantic_model = loading_models(model)
df=main(text,semantic_text,history_keyword_text)
#show the results as a table
st.table(df)
data_load_state.text('')
else:
sys.argv = ['streamlit', 'run', sys.argv[0]]
sys.exit(stcli.main())