kambris commited on
Commit
c0f831c
·
verified ·
1 Parent(s): c709d8a

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +7 -13
app.py CHANGED
@@ -208,13 +208,8 @@ def classify_emotion(text, classifier):
208
  all_scores = []
209
  for chunk in chunks:
210
  try:
211
- inputs = classifier.tokenizer(
212
- chunk,
213
- truncation=True,
214
- max_length=512,
215
- return_tensors="pt"
216
- )
217
- result = classifier(chunk, truncation=True, max_length=512)
218
  scores = result[0]
219
  all_scores.append(scores)
220
  except Exception as chunk_error:
@@ -226,11 +221,10 @@ def classify_emotion(text, classifier):
226
  count = len(all_scores)
227
 
228
  for scores in all_scores:
229
- for score in scores:
230
- label = score['label']
231
- if label not in label_scores:
232
- label_scores[label] = 0
233
- label_scores[label] += score['score']
234
 
235
  avg_scores = {label: score/count for label, score in label_scores.items()}
236
  final_emotion = max(avg_scores.items(), key=lambda x: x[1])[0]
@@ -241,7 +235,7 @@ def classify_emotion(text, classifier):
241
  except Exception as e:
242
  st.warning(f"Error in emotion classification: {str(e)}")
243
  return "LABEL_2"
244
-
245
  def get_embedding_for_text(text, tokenizer, model):
246
  """Get embedding for complete text."""
247
  chunks = split_text(text)
 
208
  all_scores = []
209
  for chunk in chunks:
210
  try:
211
+ # Direct classification without additional tokenization
212
+ result = classifier(chunk)
 
 
 
 
 
213
  scores = result[0]
214
  all_scores.append(scores)
215
  except Exception as chunk_error:
 
221
  count = len(all_scores)
222
 
223
  for scores in all_scores:
224
+ label = scores['label']
225
+ if label not in label_scores:
226
+ label_scores[label] = 0
227
+ label_scores[label] += scores['score']
 
228
 
229
  avg_scores = {label: score/count for label, score in label_scores.items()}
230
  final_emotion = max(avg_scores.items(), key=lambda x: x[1])[0]
 
235
  except Exception as e:
236
  st.warning(f"Error in emotion classification: {str(e)}")
237
  return "LABEL_2"
238
+
239
  def get_embedding_for_text(text, tokenizer, model):
240
  """Get embedding for complete text."""
241
  chunks = split_text(text)