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import streamlit as st |
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import pandas as pd |
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from plms.language_model import TransformersQG |
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import time |
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import os |
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import numpy as np |
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st.set_page_config(page_icon='🧪', page_title='ViQAG for Vietnamese Education', layout='centered', initial_sidebar_state="collapsed") |
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with open(r"./static/styles.css") as f: |
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st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True) |
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st.markdown(f""" |
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<div class=logo_area> |
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<img src="./app/static/AlphaEdu_logo_trans.png"/> |
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</div> |
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""", unsafe_allow_html=True) |
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st.markdown("<h3 style='text-align: center;'>AlphaEdu</h3>", unsafe_allow_html=True) |
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if 'output' not in st.session_state: |
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st.session_state.output = '' |
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def file_selector(folder_path=r'./Resources/'): |
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filenames = os.listdir(folder_path) |
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return filenames |
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filenames = file_selector() |
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def load_grades(file_name, folder_path=r'./Resources/'): |
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file_path = f"{folder_path}{file_name}" |
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df = pd.read_csv(file_path) |
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list_grades = df['grade'].drop_duplicates().values |
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return list_grades, df |
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def load_chapters(df, grade_name): |
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df_raw = df[df['grade'] == grade_name] |
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list_chapters = df_raw['chapter'].drop_duplicates().values |
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return list_chapters, df |
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def load_lessons(df, grade_name, chapter_name): |
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df_raw = df[(df['grade'] == grade_name) & (df['chapter'] == chapter_name)] |
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return df_raw['lesson'].drop_duplicates().values |
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def load_context(df, grade_name, chapter_name, lesson_name): |
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context = df[(df['grade'] == grade_name) & (df['chapter'] == chapter_name) & (df['lesson'] == lesson_name)]['context'].values |
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return len(context), context |
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def generateQA(context, model_path = 'shnl/vit5-vinewsqa-qg-ae'): |
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unique_qa_pairs = set() |
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model = TransformersQG(model=model_path, max_length=512) |
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output = model.generate_qa(context) |
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qa_pairs = '' |
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for item in output: |
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question, answer = item |
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if (question, answer) not in unique_qa_pairs: |
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qa_pairs += f'question: {question} \nanswer: {answer} [SEP] ' |
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unique_qa_pairs.add((question, answer)) |
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qa = '\n\n'.join(qa_pairs.split(' [SEP] ')) |
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return qa |
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col_1, col_2 = st.sidebar.columns(spec=[1, 1]) |
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subject = col_1.selectbox(label='Select your subject:', options=filenames, label_visibility='visible') |
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list_grades, df = load_grades(file_name=subject) |
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grade = col_2.selectbox(label='Select your grade:', options=list_grades, label_visibility='visible') |
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list_chapters, df = load_chapters(df=df, grade_name=grade) |
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chapter = st.sidebar.selectbox(label='Select your chapter:', options=list_chapters, label_visibility='visible') |
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lesson_names = load_lessons(df=df, grade_name=grade, chapter_name=chapter) |
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lesson = st.sidebar.selectbox(label='Lesson:', options=lesson_names, label_visibility='visible') |
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total_paragraph, context_values = load_context(df=df, grade_name=grade, chapter_name=chapter, lesson_name=lesson) |
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col_12, col_22 = st.sidebar.columns(spec=[4, 6]) |
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paragraph_idx = col_12.selectbox(label='Paragraph:', options=list(np.arange(1, total_paragraph + 1)), label_visibility='visible') |
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paragraph = st.text_area(label='Paragraph content', label_visibility='visible', height=200, value=context_values[paragraph_idx - 1]) |
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col_22.selectbox(label='QAG model:', options=['ViT5-ViNewsQA'], label_visibility='visible') |
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btn_show_answer = st.sidebar.toggle(label='Show answers', disabled=False) |
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col_14, col_24, col_34, col_44, col_54 = st.columns(spec=[1, 1, 1, 1, 1]) |
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btn_generate = col_34.button(label='Generate', use_container_width=True) |
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if btn_generate == True: |
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with st.spinner(text='Generating QA pairs from the selected paragraph. Please wait ...'): |
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st.session_state.output = generateQA(context=paragraph) |
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if btn_show_answer: |
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if st.session_state.output != '': |
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st.markdown("<h8 style='text-align: left; font-weight: normal'>Generated QA pairs:</h8>", unsafe_allow_html=True) |
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st.code(body=st.session_state.output, language='latex') |
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else: |
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pass |
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else: |
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if st.session_state.output != '': |
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st.markdown("<h8 style='text-align: left; font-weight: normal'>Generated QA pairs:</h8>", unsafe_allow_html=True) |
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output_no_answer = st.session_state.output.split(' [SEP] ')[0].split(', answer: ')[0].replace('question: ', '') |
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st.code(body=output_no_answer, language='latex') |
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else: |
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pass |
