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app.py

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+import streamlit as lit
+import torch
+from transformers import BartForConditionalGeneration, PreTrainedTokenizerFast
+
+@lit.cache(allow_output_mutation = True)
+def loadModels():
+  repository = "rycont/biblify"
+  _model = BartForConditionalGeneration.from_pretrained(repository)
+  _tokenizer = PreTrainedTokenizerFast.from_pretrained(repository)
+  
+  print("Loaded :)")
+  
+  return _model, _tokenizer
+
+model, tokenizer = loadModels()
+
+lit.title("성경말투 생성기")
+text_input = lit.text_area("문장 입력")
+
+MAX_LENGTH = 128
+
+def biblifyWithBeams(beam, tokens, attention_mask):
+  generated = model.generate(
+    input_ids = torch.Tensor([ tokens ]).to(torch.int64),
+    attention_mask = torch.Tensor([ attentionMasks ]).to(torch.int64),
+    num_beams = beam,
+    max_length = MAX_LENGTH,
+    eos_token_id=tokenizer.eos_token_id,
+    bad_words_ids=[[tokenizer.unk_token_id]]
+   )[0]
+   
+  return tokenizer.decode(
+    generated,
+  ).replace('<s>', '').replace('</s>', '')
+
+if len(text_input.strip()) > 0:
+  print(text_input)
+  
+  text_input = "<s>" + text_input + "</s>"
+  tokens = tokenizer.encode(text_input)
+  
+  tokenLength = len(tokens)
+  attentionMasks = [ 1 ] * tokenLength + [ 0 ] * (MAX_LENGTH - tokenLength)
+  tokens = tokens + [ tokenizer.pad_token_id ] * (MAX_LENGTH - tokenLength)
+  
+  results = []
+  
+  for i in range(10)[5:]:
+    generated = biblifyWithBeams(
+      i + 1,
+      tokens,
+      attentionMasks
+    )
+    
+    if generated in results:
+      print("중복됨")
+      continue
+      
+    results.append(generated)
+    with lit.expander(str(len(results)) + "번째 결과 (" + str(i +1) + ")", True):
+      lit.write(generated)
+      lit.caption(
+        "및 " + str(5 - len(results)) + " 개의 중복된 결과")
+
+