IvaElen commited on
Commit
49cf90e
1 Parent(s): 469c50b

Update pages/GPT.py

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Files changed (1) hide show
  1. pages/GPT.py +8 -3
pages/GPT.py CHANGED
@@ -5,7 +5,7 @@ import transformers
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  import random
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  import textwrap
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- @st.cache
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  def load_model():
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  model_finetuned = transformers.AutoModelWithLMHead.from_pretrained(
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  'tinkoff-ai/ruDialoGPT-small',
@@ -21,7 +21,9 @@ def preprocess_text(text_input, tokenizer):
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  return prompt
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  def predict_sentiment(model, prompt, temp, num_generate):
 
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  with torch.inference_mode():
 
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  result = model.generate(
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  input_ids=prompt,
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  max_length=100,
@@ -34,19 +36,22 @@ def predict_sentiment(model, prompt, temp, num_generate):
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  num_return_sequences=num_generate,
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  ).cpu().numpy()
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  print(result)
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- return result
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  st.title('Text generation with dreambook')
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  model, tokenizer = load_model()
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  text_input = st.text_input("Enter some text about movie")
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- max_len = st.slider('Length of sequence', 0, 500, 250)
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  temp = st.slider('Temperature', 1, 30, 1)
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  num_generate = st.text_input("Enter number of sequences")
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  if st.button('Generate'):
 
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  prompt = preprocess_text(text_input, tokenizer)
 
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  result = predict_sentiment(model, prompt, temp, int(num_generate))
 
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  for i in result:
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  st.write(textwrap.fill(tokenizer.decode(i), max_len))
 
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  import random
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  import textwrap
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+ # @st.cache
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  def load_model():
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  model_finetuned = transformers.AutoModelWithLMHead.from_pretrained(
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  'tinkoff-ai/ruDialoGPT-small',
 
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  return prompt
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  def predict_sentiment(model, prompt, temp, num_generate):
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+ print('1')
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  with torch.inference_mode():
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+ print('2')
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  result = model.generate(
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  input_ids=prompt,
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  max_length=100,
 
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  num_return_sequences=num_generate,
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  ).cpu().numpy()
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  print(result)
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+ return result
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  st.title('Text generation with dreambook')
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  model, tokenizer = load_model()
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  text_input = st.text_input("Enter some text about movie")
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+ max_len = st.slider('Length of sequence', 0, 100, 50)
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  temp = st.slider('Temperature', 1, 30, 1)
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  num_generate = st.text_input("Enter number of sequences")
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  if st.button('Generate'):
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+ print('uirhf')
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  prompt = preprocess_text(text_input, tokenizer)
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+ print('uirhf')
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  result = predict_sentiment(model, prompt, temp, int(num_generate))
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+ print('uirhf')
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  for i in result:
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  st.write(textwrap.fill(tokenizer.decode(i), max_len))