storresbusquets commited on
Commit
c0149cb
·
1 Parent(s): f443362

Update app.py

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Files changed (1) hide show
  1. app.py +5 -5
app.py CHANGED
@@ -27,7 +27,7 @@ class GradioInference:
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  self.yt = None
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  # Initialize summary model for English
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- self.summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
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  # Initialize VoiceLabT5 model and tokenizer
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  self.keyword_model = T5ForConditionalGeneration.from_pretrained(
@@ -41,7 +41,7 @@ class GradioInference:
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  self.classifier = pipeline("text-classification", model="lxyuan/distilbert-base-multilingual-cased-sentiments-student", return_all_scores=False)
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  # Initialize Multilingual summary model
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- self.tokenizer = AutoTokenizer.from_pretrained("csebuetnlp/mT5_multilingual_XLSum")
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  self.model = AutoModelForSeq2SeqLM.from_pretrained("csebuetnlp/mT5_multilingual_XLSum")
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  # self.llm_tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-7b-instruct")
@@ -92,7 +92,7 @@ class GradioInference:
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  # Perform summarization on the transcription
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  transcription_summary = self.summarizer(
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- results["text"], max_length=150, min_length=30, do_sample=False
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  )
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  #### Prueba
@@ -215,7 +215,7 @@ class GradioInference:
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  # Perform summarization on the transcription
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  transcription_summary = self.summarizer(
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- results["text"], max_length=150, min_length=30, do_sample=False
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  )
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  ########################## PRUEBA LLM #################################
@@ -428,7 +428,7 @@ with block as demo:
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  with block:
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  gr.Markdown("### Video Examples")
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- gr.Examples(["https://www.youtube.com/shorts/xDNzz8yAH7I","https://www.youtube.com/watch?v=kib6uXQsxBA&pp=ygURc3RldmUgam9icyBzcGVlY2g%3D"], inputs=link)
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  gr.Markdown("### Audio Examples")
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  gr.Examples(
 
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  self.yt = None
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  # Initialize summary model for English
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+ self.summarizer = pipeline("summarization", model="facebook/bart-large-cnn", truncation=True)
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  # Initialize VoiceLabT5 model and tokenizer
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  self.keyword_model = T5ForConditionalGeneration.from_pretrained(
 
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  self.classifier = pipeline("text-classification", model="lxyuan/distilbert-base-multilingual-cased-sentiments-student", return_all_scores=False)
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  # Initialize Multilingual summary model
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+ self.tokenizer = AutoTokenizer.from_pretrained("csebuetnlp/mT5_multilingual_XLSum", truncation=True)
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  self.model = AutoModelForSeq2SeqLM.from_pretrained("csebuetnlp/mT5_multilingual_XLSum")
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  # self.llm_tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-7b-instruct")
 
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  # Perform summarization on the transcription
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  transcription_summary = self.summarizer(
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+ results["text"], max_length=256, min_length=30, do_sample=False, truncation=True
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  )
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  #### Prueba
 
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  # Perform summarization on the transcription
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  transcription_summary = self.summarizer(
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+ results["text"], max_length=150, min_length=30, do_sample=False, truncation=True
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  )
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  ########################## PRUEBA LLM #################################
 
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  with block:
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  gr.Markdown("### Video Examples")
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+ gr.Examples(["https://www.youtube.com/shorts/xDNzz8yAH7I","https://www.youtube.com/watch?v=MnrJzXM7a6o&pp=ygURc3RldmUgam9icyBzcGVlY2g%3D"], inputs=link)
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  gr.Markdown("### Audio Examples")
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  gr.Examples(