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import os | |
os.system("pip install -U transformers==3.0.0") | |
os.system("pip install nltk torch docx2txt") | |
os.system("python -m nltk.downloader punkt") | |
import gradio as gr | |
import pandas as pd | |
from question_generation.pipelines import pipeline | |
import docx2txt | |
def process_file(Notes): | |
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 = nlp(" ".join(preprocessed_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] | |
io = gr.Interface(process_file, "file", outputs= | |
[gr.Textbox(lines=1, label="Questions"), | |
gr.Textbox(lines=1, label="Answers")]) | |
io.launch() | |