app.py

import streamlit as st
from transformers import T5ForConditionalGeneration, T5Tokenizer

from fill_in_summary import FillInSummary
from paraphrase import PegasusParaphraser
import question_gen as q


default_text = "Apple was founded as Apple Computer Company on April 1, 1976, by Steve Jobs, Steve Wozniak and Ronald " \
               "Wayne to develop and sell Wozniak's Apple I personal computer. It was incorporated by Jobs and " \
               "Wozniak as Apple Computer, Inc. in 1977 and the company's next computer, the Apple II became a best " \
               "seller. Apple went public in 1980, to instant financial success. The company went onto develop new " \
               "computers featuring innovative graphical user interfaces, including the original Macintosh, " \
               "announced in a critically acclaimed advertisement, '1984', directed by Ridley Scott. By 1985, " \
               "the high cost of its products and power struggles between executives caused problems. Wozniak stepped " \
               "back from Apple amicably, while Jobs resigned to found NeXT, taking some Apple employees with him. "

default_text2 = "The board of directors instructed Sculley to contain Jobs and his ability to launch expensive forays " \
                "into untested products "



st.set_page_config(layout="centered")
st.title('Question Generator, Paraph/Summarizer by Eddevs')
st.write('The Goal of this Space is to help educators lower recognisability of the assessment questions, and enable students achieve higher learning goals, and enable students to achieve higher learning goals by doing so.')

"""
* You can Generate Question by inputing Context 
* Paraphrase the Questions you already have
* Summarise Section(s) of Course Materials to Generate Question by inputing that as Context 
* Remove Entities From Context and Generate Fill in the Blank Questions')
"""


select = st.selectbox('Type', ['Question Generator', 'Paraphrasing', 'Summarization', 'Fill in the blank'])

if select == "Question Generator":
    with st.form("question_gen"):
        left_column, right_column = st.columns(2)
        num_seq = left_column.slider('Question Count', 0, 10, 3)
        beams = right_column.slider('Beams', 0, 10, 5)
        max_length = st.slider('Max Length', 0, 1024, 300)
        text_input = st.text_area("Input Text", value=default_text)

        submitted = st.form_submit_button("Generate")
        if submitted:
            with st.spinner('Wait for it...'):
                question_model = T5ForConditionalGeneration.from_pretrained('ramsrigouthamg/t5_squad_v1')
                question_tokenizer = T5Tokenizer.from_pretrained('ramsrigouthamg/t5_squad_v1')

                result = q.get_question(text_input, "", question_model, question_tokenizer, num_seq, beams, max_length)
            st.write(result)


elif select == "Summarization":
    with st.form("summarization"):
        text_input = st.text_area("Input Text", value=default_text)

        submitted = st.form_submit_button("Generate")

        if submitted:
            with st.spinner('Wait for it...'):
                result = FillInSummary().summarize(text_input)
            st.write(text_input)


elif select == "Fill in the blank":
    with st.form("fill_in_the_blank"):
        text_input = st.text_area("Input Text", value=default_text)

        submitted = st.form_submit_button("Generate")

        if submitted:
            with st.spinner('Wait for it...'):
                fill = FillInSummary()
                result = fill.summarize(text_input)
                result = fill.blank_ne_out(result)
            st.write(result)


elif select == "Paraphrasing":
    with st.form("paraphrasing"):
        left_column, right_column = st.columns(2)
        count = left_column.slider('Count', 0, 10, 3)
        temperature = right_column.slider('Temperature', 0.0, 10.0, 1.5)
        text_input = st.text_area("Input Text", value=default_text2)

        submitted = st.form_submit_button("Generate")

        if submitted:
            with st.spinner('Wait for it...'):
                paraphrase_model = PegasusParaphraser(num_return_sequences=count, temperature=temperature)
                result = paraphrase_model.paraphrase(text_input)
            st.write(result)