Bram Vanroy commited on
Commit
f73827b
1 Parent(s): d5db8f1

improve progressbar

Browse files
Files changed (2) hide show
  1. app.py +7 -5
  2. utils.py +1 -1
app.py CHANGED
@@ -38,14 +38,15 @@ else:
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  def _get_increment_size(num_sents) -> int:
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- if BATCH_SIZE >= num_sents:
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  return 100
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  else:
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- return ceil(100 / (num_sents / BATCH_SIZE))
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  btn_col, results_col = st.columns(2)
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  btn_ct = btn_col.empty()
 
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  error_ct = st.empty()
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  simpl_ct = st.container()
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  if st.session_state["text_to_simplify"]:
@@ -56,7 +57,7 @@ if st.session_state["text_to_simplify"]:
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  ]
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  num_sentences = len(lines)
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- pbar = st.progress(0, text=f"Simplifying sentences in batches of {BATCH_SIZE}...")
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  increment = _get_increment_size(num_sentences)
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  percent_done = 0
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@@ -77,10 +78,11 @@ if st.session_state["text_to_simplify"]:
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  </li>"""
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  output_ct.markdown(html + "</ol>", unsafe_allow_html=True)
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  all_simplifications.extend(simplifications)
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- percent_done += increment
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- pbar.progress(min(percent_done, 100))
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  pbar.empty()
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  all_simplifications = "\n".join(all_simplifications) + "\n"
 
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  def _get_increment_size(num_sents) -> int:
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+ if num_sents == 1:
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  return 100
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  else:
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+ return ceil(100 / num_sents)
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  btn_col, results_col = st.columns(2)
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  btn_ct = btn_col.empty()
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+ pbar_ct = st.empty()
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  error_ct = st.empty()
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  simpl_ct = st.container()
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  if st.session_state["text_to_simplify"]:
 
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  ]
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  num_sentences = len(lines)
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+ pbar = pbar_ct.progress(0, text=f"Simplifying sentences in batches of {BATCH_SIZE}...")
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  increment = _get_increment_size(num_sentences)
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  percent_done = 0
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  </li>"""
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  output_ct.markdown(html + "</ol>", unsafe_allow_html=True)
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+ percent_done += increment
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+ pbar.progress(min(percent_done, 100))
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+
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  all_simplifications.extend(simplifications)
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  pbar.empty()
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  all_simplifications = "\n".join(all_simplifications) + "\n"
utils.py CHANGED
@@ -48,7 +48,7 @@ def simplify(
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  """
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  for batch_texts in batchify(texts, batch_size=batch_size):
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  nlg_batch_texts = ["[NLG] " + text for text in batch_texts]
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- encoded = tokenizer(nlg_batch_texts, return_tensors="pt", padding=True, truncation=True)
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  encoded = {k: v.to(model.device) for k, v in encoded.items()}
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  gen_kwargs = {
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  "max_new_tokens": 128,
 
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  """
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  for batch_texts in batchify(texts, batch_size=batch_size):
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  nlg_batch_texts = ["[NLG] " + text for text in batch_texts]
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+ encoded = tokenizer(nlg_batch_texts, return_tensors="pt", padding=True)
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  encoded = {k: v.to(model.device) for k, v in encoded.items()}
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  gen_kwargs = {
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  "max_new_tokens": 128,