AlGe commited on
Commit
7a3ed1a
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1 Parent(s): e48c8a8

Update app.py

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Files changed (1) hide show
  1. app.py +3 -2
app.py CHANGED
@@ -109,7 +109,6 @@ def generate_charts(ner_output_ext: dict) -> Tuple[go.Figure, np.ndarray]:
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  return fig1, wordcloud_image
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-
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  def generate_wordcloud(entities: List[Dict], color_map: Dict[str, str]) -> np.ndarray:
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  token_texts = []
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  token_scores = []
@@ -117,9 +116,11 @@ def generate_wordcloud(entities: List[Dict], color_map: Dict[str, str]) -> np.nd
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  for entity in entities:
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  for token in entity['tokens']:
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- token_texts.append(token)
 
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  token_scores.append(entity['score'])
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  token_types.append(entity['entity'])
 
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  # Create a dictionary for word cloud
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  word_freq = {text: score for text, score in zip(token_texts, token_scores)}
 
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  return fig1, wordcloud_image
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  def generate_wordcloud(entities: List[Dict], color_map: Dict[str, str]) -> np.ndarray:
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  token_texts = []
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  token_scores = []
 
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  for entity in entities:
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  for token in entity['tokens']:
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+ cleaned_token = token.lstrip('_')
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+ token_texts.append(cleaned_token)
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  token_scores.append(entity['score'])
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  token_types.append(entity['entity'])
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+ print(f"{cleaned_token} ({entity['entity']}): {entity['score']}")
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  # Create a dictionary for word cloud
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  word_freq = {text: score for text, score in zip(token_texts, token_scores)}