app.py

import numpy as np
import pandas as pd
import time
import streamlit as st
import matplotlib.pyplot as plt
import seaborn as sns

import jax
import jax.numpy as jnp

import torch
import torch.nn.functional as F

from transformers import AlbertTokenizer, AlbertForMaskedLM

#from custom_modeling_albert_flax import CustomFlaxAlbertForMaskedLM
from skeleton_modeling_albert import SkeletonAlbertForMaskedLM

def wide_setup():
    max_width = 1500
    padding_top = 0
    padding_right = 2
    padding_bottom = 0
    padding_left = 2

    define_margins = f"""
    <style>
        .appview-container .main .block-container{{
            max-width: {max_width}px;
            padding-top: {padding_top}rem;
            padding-right: {padding_right}rem;
            padding-left: {padding_left}rem;
            padding-bottom: {padding_bottom}rem;
        }}
    </style>
    """
    hide_table_row_index = """
                <style>
                tbody th {display:none}
                .blank {display:none}
                </style>
                """
    st.markdown(define_margins, unsafe_allow_html=True)
    st.markdown(hide_table_row_index, unsafe_allow_html=True)

def load_css(file_name):
    with open(file_name) as f:
        st.markdown(f'<style>{f.read()}</style>', unsafe_allow_html=True)

@st.cache(show_spinner=True,allow_output_mutation=True)
def load_model():
    tokenizer = AlbertTokenizer.from_pretrained('albert-xxlarge-v2')
    #model = CustomFlaxAlbertForMaskedLM.from_pretrained('albert-xxlarge-v2',from_pt=True)
    model = AlbertForMaskedLM.from_pretrained('albert-xxlarge-v2')
    return tokenizer,model

def clear_data():
    for key in st.session_state:
        del st.session_state[key]

def show_annotated_sentence(sent,option_locs=[],mask_locs=[]):
    disp_style = '"font-family:san serif; color:Black; font-size: 20px"'
    prefix = f'<p style={disp_style}><span style="font-weight:bold">'
    style_list = []
    for i, word in enumerate(sent):
        if i in mask_locs:
            style_list.append(f'<span style="color:Red">{word}</span>')
        elif i in option_locs:
            style_list.append(f'<span style="color:Blue">{word}</span>')
        else:
            style_list.append(f'{word}')
    disp = ' '.join(style_list)
    suffix = '</span></p>'
    return prefix + disp + suffix

if __name__=='__main__':
    wide_setup()
    load_css('style.css')
    tokenizer,model = load_model()
    mask_id = tokenizer('[MASK]').input_ids[1:-1][0]

    main_area = st.empty()

    if 'page_status' not in st.session_state:
        st.session_state['page_status'] = 'type_in'

    if st.session_state['page_status']=='type_in':
        with main_area.container():
            st.write('1. Type in the sentences and click "Tokenize"')
            sent_1 = st.text_input('Sentence 1',value='It is better to play a prank on Samuel than Craig because he gets angry less often.')
            sent_2 = st.text_input('Sentence 2',value='It is better to play a prank on Samuel than Craig because he gets angry more often.')
            if st.button('Tokenize'):
                st.session_state['page_status'] = 'tokenized'
                st.session_state['sent_1'] = sent_1
                st.session_state['sent_2'] = sent_2
                main_area.empty()

    if st.session_state['page_status']=='tokenized':
        with main_area.container():
            sent_1 = st.session_state['sent_1']
            sent_2 = st.session_state['sent_2']
            if 'masked_pos_1' not in st.session_state:
                st.session_state['masked_pos_1'] = []
            if 'masked_pos_2' not in st.session_state:
                st.session_state['masked_pos_2'] = []

            st.write('2. Select sites to mask out and click "Confirm"')
            input_sent = tokenizer(sent_1).input_ids
            decoded_sent = [tokenizer.decode([token]) for token in input_sent[1:-1]]
            char_nums = [len(word)+2 for word in decoded_sent]
            cols = st.columns(char_nums)
            for word_id,(col,word) in enumerate(zip(cols,decoded_sent)):
                with col:
                    if st.button(word,key=f'word_{word_id}'):
                        if word_id not in st.session_state['masked_pos_1']:
                            st.session_state['masked_pos_1'].append(word_id)
                        else:
                            st.session_state['masked_pos_1'].remove(word_id)
            st.markdown(show_annotated_sentence(decoded_sent,mask_locs=st.session_state['masked_pos_1']), unsafe_allow_html = True)


    if st.session_state['page_status']=='analysis':
        sent_1 = st.sidebar.text_input('Sentence 1',value='It is better to play a prank on Samuel than Craig because he gets angry less often.',on_change=clear_data)
        sent_2 = st.sidebar.text_input('Sentence 2',value='It is better to play a prank on Samuel than Craig because he gets angry more often.',on_change=clear_data)
        input_ids_1 = tokenizer(sent_1).input_ids
        input_ids_2 = tokenizer(sent_2).input_ids
        input_ids = torch.tensor([input_ids_1,input_ids_2])

        outputs = SkeletonAlbertForMaskedLM(model,input_ids,interventions = {0:{'lay':[(8,1,[0,1])]}})
        logprobs = F.log_softmax(outputs['logits'], dim = -1)
        preds = [torch.multinomial(torch.exp(probs), num_samples=1).squeeze(dim=-1) for probs in logprobs[0]]
        st.write([tokenizer.decode([token]) for token in preds])