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)

@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]

if __name__=='__main__':
    wide_setup()

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

    if st.session_state['page_status']=='type_in':
        tokenizer,model = load_model()
        mask_id = tokenizer('[MASK]').input_ids[1:-1][0]

        st.write('1. Type in the sentences and click "Tokenize"')
        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.')
        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.')
        if st.sidebar.button('Tokenize'):
            st.session_state['page_status'] = 'tokenized'
            st.session_state['sent_1'] = sent_1
            st.session_state['sent_2'] = sent_2

    if st.session_state['page_status']=='tokenized':
        tokenizer,model = load_model()
        mask_id = tokenizer('[MASK]').input_ids[1:-1][0]
        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]
        char_nums = [len(word)+2 for word in decoded_sent]
        cols = st.columns(char_nums)
        with cols[0]:
            st.write(decoded_sent[0])
        with cols[-1]:
            st.write(decoded_sent[-1])
        for word_id,(col,word) in enumerate(zip(cols[1:-1],decoded_sent[1:-1])):
            with col:
                if st.button(word,key=f'word_{word_id}'):
                    st.session_state['masked_pos_1'].append(word_id)
        st.write(f'Masked words: {" ".join([decoded_sent[word_id+1] for word_id in st.session_state["masked_pos_1"])}')

    '''
    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])
    '''