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Commit
e4e5030
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1 Parent(s): b0070ce

Update app.py

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Files changed (1) hide show
  1. app.py +30 -27
app.py CHANGED
@@ -1,11 +1,11 @@
1
  import streamlit as st
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  from transformers import pipeline
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  from PyPDF2 import PdfReader
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- import PyPDF2
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- import os
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  import nltk
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  nltk.download('punkt')
 
 
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  def get_pdf_text(pdf_docs):
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  text = ""
@@ -15,35 +15,38 @@ def get_pdf_text(pdf_docs):
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  text += page.extract_text()
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  return text
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- def main():
 
 
 
 
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  st.title('Question Generator from PDFs')
 
 
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  pipe = pipeline(
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  task = 'text2text-generation',
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  model = 'ramsrigouthamg/t5_squad_v1'
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  )
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  file = st.file_uploader(label='Upload',accept_multiple_files=True)
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- pr = st.button(label='Start')
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- if pr:
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- st.write('Hi')
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- raw_text = get_pdf_text(file)
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- sentences = nltk.sent_tokenize(text=raw_text)
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- # st.write(sts)
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- # for i in sentences:
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- # st.write(i)
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- questions = []
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- st.subheader("Generated Questions are: ")
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- s = pipe(sentences)
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- for i in s:
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- questions.append(i['generated_text'][10:])
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- st.write(i['generated_text'][10:])
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- if st.toggle(label='Show Pipeline Output'):
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- st.write(s)
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- if st.toggle(label='Show Questions list'):
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- st.write(questions)
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- # for i in sts:
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- # x = pipe(i)
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- # questions.append(x)
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- # st.write(x)
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- if __name__ == '__main__':
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- main()
 
 
 
 
 
 
 
 
 
 
 
1
  import streamlit as st
2
  from transformers import pipeline
3
  from PyPDF2 import PdfReader
 
 
4
  import nltk
5
 
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  nltk.download('punkt')
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+ st.title(body='7 - Question Generation')
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+
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  def get_pdf_text(pdf_docs):
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  text = ""
 
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  text += page.extract_text()
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  return text
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+
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+ ########################################################
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+ st.subheader(body='Proposition 1',divider='orange')
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+
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+ if st.toggle(label='Show Proposition 1'):
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  st.title('Question Generator from PDFs')
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+ if st.checkbox('Show Caption'):
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+ st.caption('Hugging Face Model used: ramsrigouthamg/t5_squad_v1')
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  pipe = pipeline(
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  task = 'text2text-generation',
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  model = 'ramsrigouthamg/t5_squad_v1'
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  )
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  file = st.file_uploader(label='Upload',accept_multiple_files=True)
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+ # pr = st.button(label='Process')
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+ raw_text = get_pdf_text(file)
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+ sentences = nltk.sent_tokenize(text=raw_text)
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+ s = pipe(sentences)
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+ questions = []
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+ for i in s:
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+ x = i['generated_text'][10:]
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+ questions.append(x)
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+ # st.write(f':blue[{x}]')
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ if st.toggle(label='Show Questions'):
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+ st.subheader("*Generated Questions are:*")
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+ for i in s:
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+ x = i['generated_text'][10:]
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+ questions.append(x)
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+ st.write(f':blue[{x}]')
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+ if st.toggle('Show Text'):
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+ st.write(raw_text)
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+ if st.toggle(label='Show Pipeline Output'):
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+ st.write(s)
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+ if st.toggle(label='Show Questions list'):
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+ st.write(questions)