import streamlit as st
import pandas as pd
from plms.language_model import TransformersQG
import os
import numpy as np

st.set_page_config(page_icon='🧪', page_title='ViQAG for Vietnamese Education', layout='wide', initial_sidebar_state="collapsed")

with open(r"./static/styles.css") as f:
    st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)

st.markdown(f"""
    <div class=logo_area>
        <img src="./app/static/AlphaEdu_logo_trans.png"/>
    </div>
    """, unsafe_allow_html=True)
st.markdown("<h1 style='text-align: center;'>AlphaEdu</h1>", unsafe_allow_html=True)

# =====================================================================================================

if 'output' not in st.session_state:
    st.session_state.output = ''

def file_selector(folder_path=r'./Resources/'):
    filenames = os.listdir(folder_path)
    return filenames
filenames = file_selector()

def load_grades(file_name, folder_path=r'./Resources/'):
    file_path = f"{folder_path}{file_name}"
    df = pd.read_csv(file_path)
    list_grades = df['grade'].drop_duplicates().values
    return list_grades, df

def load_chapters(df, grade_name):
    df_raw = df[df['grade'] == grade_name]
    list_chapters = df_raw['chapter'].drop_duplicates().values
    return list_chapters, df

def load_lessons(df, grade_name, chapter_name):
    df_raw = df[(df['grade'] == grade_name) & (df['chapter'] == chapter_name)]
    return df_raw['lesson'].drop_duplicates().values

def load_context(df, grade_name, chapter_name, lesson_name):
    context = df[(df['grade'] == grade_name) & (df['chapter'] == chapter_name) & (df['lesson'] == lesson_name)]['context'].values
    return len(context), context

def generateQA(context, model_path = 'shnl/vit5-vinewsqa-qg-ae'):
    unique_qa_pairs = set()
    model = TransformersQG(model=model_path, max_length=512)
    output = model.generate_qa(context)
    qa_pairs = ''
    for item in output:
        question, answer = item
        if (question, answer) not in unique_qa_pairs:
            qa_pairs += f'question: {question} \nanswer: {answer} [SEP] '
            unique_qa_pairs.add((question, answer))
    qa = '\n\n'.join(qa_pairs.split(' [SEP] '))
    return qa
    
# =====================================================================================================

col_1, col_2, col_3, col_4, col_5 = st.columns(spec=[1, 1, 3, 3, 1])

col_1.markdown("<h8 style='text-align: left; font-weight: normal'>Select your subject:</h8>", unsafe_allow_html=True)
subject = col_1.selectbox(label='Select your subject:', options=filenames, label_visibility='collapsed')

col_2.markdown("<h8 style='text-align: left; font-weight: normal'>Select your grade:</h8>", unsafe_allow_html=True)
list_grades, df = load_grades(file_name=subject)
grade = col_2.selectbox(label='Select your grade:', options=list_grades, label_visibility='collapsed')

col_3.markdown("<h8 style='text-align: left; font-weight: normal'>Select your chapter:</h8>", unsafe_allow_html=True)
list_chapters, df = load_chapters(df=df, grade_name=grade)
chapter = col_3.selectbox(label='Select your chapter:', options=list_chapters, label_visibility='collapsed')

col_4.markdown("<h8 style='text-align: left; font-weight: normal'>Select your lesson:</h8>", unsafe_allow_html=True)
lesson_names = load_lessons(df=df, grade_name=grade, chapter_name=chapter)
lesson = col_4.selectbox(label='Select your lesson:', options=lesson_names, label_visibility='collapsed')

col_5.markdown("<h8 style='text-align: left; font-weight: normal'>Paragraph:</h8>", unsafe_allow_html=True)
total_paragraph, context_values = load_context(df=df, grade_name=grade, chapter_name=chapter, lesson_name=lesson)

paragraph_idx = col_5.selectbox(label='Select your lesson:', options=list(np.arange(1, total_paragraph + 1)), label_visibility='collapsed')

col_11, col_21 = st.columns(spec=[9, 1])
col_11.markdown("<h8 style='text-align: left; font-weight: normal'>Paragraph related:</h8>", unsafe_allow_html=True)
col_11.text_area(label='Paragraph related', label_visibility='collapsed', height=300, value=context_values[paragraph_idx - 1])

col_21.markdown("<h8 style='text-align: left; font-weight: normal'>QAG modes:</h8>", unsafe_allow_html=True)
col_21.selectbox(label='QAG model:', options=['ViT5-ViNewsQA'], label_visibility='collapsed')

col_21.markdown("<h8 style='text-align: left; font-weight: normal'>Options:</h8>", unsafe_allow_html=True)
btn_show_answer = col_21.toggle(label='Show answers', disabled=False)

btn_generate = col_21.button(label='Generate questions', use_container_width=True)

if btn_generate == True:
    with st.spinner(text='Generating QA pairs from the selected context. Please wait ...'):
        st.session_state.output = generateQA(context=context_values)

if btn_show_answer:
    if st.session_state.output != '':
        st.markdown("<h8 style='text-align: left; font-weight: normal'>Your questions and answers has been generated:</h8>", unsafe_allow_html=True)
        st.code(body=st.session_state.output, language='wiki')
    else:
        pass
else:
    if st.session_state.output != '':
        st.markdown("<h8 style='text-align: left; font-weight: normal'>Your questions and answers has been generated:</h8>", unsafe_allow_html=True)
        output_no_answer = st.session_state.output.split(' [SEP] ')[0].split(', answer: ')[0].replace('question: ', '')
        st.code(body=output_no_answer, language='wiki')
    else:
        pass