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README.md

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----
-title: FeinbergQuizNotes
-emoji: 💻
-colorFrom: gray
-colorTo: green
-sdk: gradio
-sdk_version: 3.0.26
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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-import os
-import gradio as gr
-import pandas as pd
-from question_generation.pipelines import pipeline
-import docx2txt
-
-qa_list = []
-def process_file(Notes):
-
-  os.system("pip install -U transformers==3.0.0")
-  os.system("python -m nltk.downloader punkt")
-  nlp = pipeline("question-generation", model="valhalla/t5-small-qg-prepend", qg_format="prepend")
-  
-  target_word_doc = Notes.name
-  raw_word_file = docx2txt.process(target_word_doc)
-
-  #remove empty lines
-  preprocessed_sentence_list = [i for i in raw_word_file.splitlines() if i != ""]
-
-  #grab content
-  processed_sentence_list = []
-  content = False
-  for i in preprocessed_sentence_list:
-    if "Outline" in i:
-      content = True
-      continue
-    if "Summary Learning Points" in i:
-      content = False
-      continue
-    if "Learning Activity" in i:
-      content = False
-      continue
-    if content == True:
-      processed_sentence_list.append(i.lstrip())
-
-  qa_list.extend(nlp(" ".join(processed_sentence_list)))
-  formatted_questions = "\n".join([str(idx+1) + ". " + i["question"] for idx, i in enumerate(qa_list)])
-  formatted_answers = "\n".join([str(idx+1) + ". " + i["answer"] for idx, i in enumerate(qa_list)])
-  return [formatted_questions, formatted_answers]
-
-def reveal_answer():
-  global qa_list
-  
-  qa_list = []
-  return formatted_answers
-
-io = gr.Interface(process_file, "file", outputs=
-                  [gr.Textbox(lines=1, label="Questions"), 
-                   gr.Textbox(lines=1, label="Answers")])
-io.launch()

question_generation

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-Subproject commit c30e2976d65c4ef6200c7504097e8e07545fb240