app.py

from transformers import AutoModelForMaskedLM
from transformers import AutoTokenizer
from sklearn.metrics.pairwise import cosine_similarity
import streamlit as st
import torch
import pickle

model_checkpoint = "vives/distilbert-base-uncased-finetuned-cvent-2019_2022"
model = AutoModelForMaskedLM.from_pretrained(model_checkpoint, output_hidden_states=True)
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
text = st.text_input("Enter word or key-phrase")
exclude_text = st.radio("exclude_text",[True,False])
exclude_words = st.radio("exclude_words",[True,False])
k = st.number_input("Top k nearest key-phrases",1,10,5)

with open("kp_dict_merged.pickle",'rb') as handle:
  kp_dict = pickle.load(handle)
for key in kp_dict.keys():
  kp_dict[key] = kp_dict[key].detach().numpy()
  
def calculate_top_k(out, tokens,text,exclude_text=False,exclude_words=False, k=5):
  sim_dict = {}
  pools = pool_embeddings(out, tokens).detach().numpy()
  for key in kp_dict.keys():
    if key == text:
      continue
    if exclude_text and text in key:
      continue
    if exclude_words and True in [x in key for x in text.split(" ")]:
      continue
    sim_dict[key] = cosine_similarity(
        pools,
        [kp_dict[key]]
    )[0][0]
  sims = sorted(sim_dict.items(), key= lambda x: x[1], reverse = True)[:k]
  return {x:y for x,y in sims}
def concat_tokens(sentences):
  tokens = {'input_ids': [], 'attention_mask': [], 'KPS': []}
  for sentence in sentences:
      # encode each sentence and append to dictionary
      new_tokens = tokenizer.encode_plus(sentence, max_length=64,
                                         truncation=True, padding='max_length',
                                         return_tensors='pt')
      tokens['input_ids'].append(new_tokens['input_ids'][0])
      tokens['attention_mask'].append(new_tokens['attention_mask'][0])
      tokens['KPS'].append(sentence)
  # reformat list of tensors into single tensor
  tokens['input_ids'] = torch.stack(tokens['input_ids'])
  tokens['attention_mask'] = torch.stack(tokens['attention_mask'])
  return tokens

def pool_embeddings(out, tok):
  embeddings = out["hidden_states"][-1]
  attention_mask = tok['attention_mask']
  mask = attention_mask.unsqueeze(-1).expand(embeddings.size()).float()
  masked_embeddings = embeddings * mask
  summed = torch.sum(masked_embeddings, 1)
  summed_mask = torch.clamp(mask.sum(1), min=1e-9)
  mean_pooled = summed / summed_mask
  return mean_pooled
  
if text:
  new_tokens = concat_tokens([text])
  new_tokens.pop("KPS")
  with torch.no_grad():
    outputs = model(**new_tokens)
  sim_dict = calculate_top_k(outputs, new_tokens, text, exclude_text=exclude_text,exclude_words=exclude_words,k=k)
  st.json(sim_dict)