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

from transformers import AlbertTokenizer

from custom_modeling_albert_flax import CustomFlaxAlbertForMaskedLM

@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)
    return tokenizer,model

if __name__=='__main__':

    # Config
    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)

    tokenizer,model = load_model()
    mask_id = tokenizer('[MASK]').input_ids[1:-1][0]

    input_ids = tokenizer('This is a sample sentence.',return_tensors='np').input_ids
    input_ids[0][4] = mask_id

    outputs = model(input_ids)
    logprobs = jax.nn.log_softmax(outputs.logits, axis = -1)
    st.write(logprobs.shape)
    preds = [np.random.choice(np.arange(len(probs)),p=np.exp(probs)/np.sum(np.exp(probs))) for probs in logprobs[0]]
    st.write([tokenizer.decode([token]) for token in preds])