Update app.py

app.py

@@ -86,49 +86,34 @@ class GradioInference:
  progress(0.40, desc="Summarizing")
  
  # Perform summarization on the transcription
- # transcription_summary = self.summarizer(
- # results["text"], max_length=150, min_length=30, do_sample=False
- # )
+ transcription_summary = self.summarizer(
+ results["text"], max_length=150, min_length=30, do_sample=False
+ )
 
- #### Prueba
- # WHITESPACE_HANDLER = lambda k: re.sub('\s+', ' ', re.sub('\n+', ' ', k.strip()))
- 
- # input_ids_sum = self.tokenizer(
- # [WHITESPACE_HANDLER(results["text"])],
- # return_tensors="pt",
- # padding="max_length",
- # truncation=True,
- # max_length=512
- # )["input_ids"]
- 
- # output_ids_sum = self.model.generate(
- # input_ids=input_ids_sum,
- # max_length=130,
- # no_repeat_ngram_size=2,
- # num_beams=4
- # )[0]
+ #### Resumen multilingue
+ WHITESPACE_HANDLER = lambda k: re.sub('\s+', ' ', re.sub('\n+', ' ', k.strip()))
  
- # summary = self.tokenizer.decode(
- # output_ids_sum,
- # skip_special_tokens=True,
- # clean_up_tokenization_spaces=False
- # )
- #### Fin prueba
+ input_ids_sum = self.tokenizer(
+ [WHITESPACE_HANDLER(results["text"])],
+ return_tensors="pt",
+ padding="max_length",
+ truncation=True,
+ max_length=512
+ )["input_ids"]
  
- ### Prueba con LLM ###
-
- template = """
- [INST] <<SYS>>
- You are a helpful, respectful and honest assistant that performs summaries of text. Write a concise summary of the following text.
- <</SYS>>
- {text}[/INST]
- """
+ output_ids_sum = self.model.generate(
+ input_ids=input_ids_sum,
+ max_length=130,
+ no_repeat_ngram_size=2,
+ num_beams=4
+ )[0]
  
- prompt = PromptTemplate(template=template, input_variables=["text"])
- llm_chain = LLMChain(prompt=prompt, llm=self.llm)
- summary2 = llm_chain.run(results["text"])
-
- ### Fin prueba LLM ###
+ summary = self.tokenizer.decode(
+ output_ids_sum,
+ skip_special_tokens=True,
+ clean_up_tokenization_spaces=False
+ )
+ #### Fin resumen multilingue
  
  progress(0.60, desc="Extracting Keywords")
  
@@ -171,8 +156,7 @@ class GradioInference:
  if lang == "english" or lang == "none":
  return (
  results["text"],
- summary2,
- # transcription_summary[0]["summary_text"],
+ transcription_summary[0]["summary_text"],
  formatted_keywords,
  formatted_sentiment,
  wordcloud_image,
@@ -180,7 +164,7 @@ class GradioInference:
  else:
  return (
  results["text"],
- summary2,
+ summary,
  formatted_keywords,
  formatted_sentiment,
  wordcloud_image,
@@ -229,7 +213,7 @@ class GradioInference:
  results["text"], max_length=150, min_length=30, do_sample=False
  )
  
- #### Prueba
+ #### Resumen multilingue
  WHITESPACE_HANDLER = lambda k: re.sub('\s+', ' ', re.sub('\n+', ' ', k.strip()))
  
  input_ids_sum = self.tokenizer(
@@ -252,7 +236,7 @@ class GradioInference:
  skip_special_tokens=True,
  clean_up_tokenization_spaces=False
  )
- #### Fin prueba
+ #### Fin resumen multilingue
 
  progress(0.50, desc="Extracting Keywords")