kadirnar commited on
Commit
7a61e1e
1 Parent(s): 45972bb

Update app.py

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Files changed (1) hide show
  1. app.py +8 -2
app.py CHANGED
@@ -92,7 +92,13 @@ def biogpt_audio(
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  return en_prompt, output, output_text
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- examples = [["COVID-19 is", biogpt_model_list[0], lang_model_list[1], "English"]]
 
 
 
 
 
 
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  app = gr.Blocks()
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  with app:
@@ -127,7 +133,7 @@ with app:
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  output_text = gr.Textbox(lines=3, label="BioGpt Text")
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  translated_text = gr.Textbox(lines=3,label="Translated Text")
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- gr.Examples(examples, inputs=[input_text, text_biogpt, text_m2m100,text_lang], outputs=[prompt_text, output_text, translated_text], fn=biogpt_text, cache_examples=False)
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  text_button.click(biogpt_text, inputs=[input_text, text_biogpt, text_m2m100 ,text_lang], outputs=[prompt_text, output_text, translated_text])
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  audio_button.click(biogpt_audio, inputs=[input_audio, audio_biogpt, audio_whisper, audio_lang, audio_m2m100], outputs=[prompt_text, output_text, translated_text])
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  return en_prompt, output, output_text
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+ question_example = 'question: Should chest wall irradiation be included after mastectomy and negative node breast cancer? context: This study aims to evaluate local failure patterns in node negative breast cancer patients treated with post-mastectomy radiotherapy including internal mammary chain only. Retrospective analysis of 92 internal or central-breast node-negative tumours with mastectomy and external irradiation of the internal mammary chain at the dose of 50 Gy, from 1994 to 1998. Local recurrence rate was 5 % (five cases). Recurrence sites were the operative scare and chest wall. Factors associated with increased risk of local failure were age<or = 40 years and tumour size greater than 20mm, without statistical significance. answer: Post-mastectomy radiotherapy should be discussed for a sub-group of node-negative patients with predictors factors of local failure such as age<or = 40 years and larger tumour size. target: the answer to the question given the context is'
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+
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+ examples = [
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+ ["COVID-19 is", biogpt_model_list[0], lang_model_list[1], "English"],
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+ [question_example, biogpt_model_list[2], lang_model_list[1], "English"]
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+ ]
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+
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  app = gr.Blocks()
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  with app:
 
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  output_text = gr.Textbox(lines=3, label="BioGpt Text")
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  translated_text = gr.Textbox(lines=3,label="Translated Text")
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+ gr.Examples(examples, inputs=[input_text, text_biogpt, text_m2m100,text_lang], outputs=[prompt_text, output_text, translated_text], fn=biogpt_text, cache_examples=True)
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  text_button.click(biogpt_text, inputs=[input_text, text_biogpt, text_m2m100 ,text_lang], outputs=[prompt_text, output_text, translated_text])
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  audio_button.click(biogpt_audio, inputs=[input_audio, audio_biogpt, audio_whisper, audio_lang, audio_m2m100], outputs=[prompt_text, output_text, translated_text])
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