app.py

import random
import re
from poems import SAMPLE_POEMS

import langid
import numpy as np
import streamlit as st
import torch

from icu_tokenizer import Tokenizer
from transformers import pipeline

MODELS = {
    "ALBERTI": "flax-community/alberti-bert-base-multilingual-cased",
    "mBERT": "bert-base-multilingual-cased"
}

TOPK = 50
st.set_page_config(layout="wide")


def mask_line(line, language="es", restrictive=True):
    tokenizer = Tokenizer(lang=language)
    token_list = tokenizer.tokenize(line)
    if lang != "zh":
        restrictive = not all([len(token) <= 3 for token in token_list])
    random_num = random.randint(0, len(token_list) - 1)
    random_word = token_list[random_num]
    if not restrictive:
        token_list[random_num] = "[MASK]"
        masked_l = " ".join(token_list)
        return masked_l
    elif len(random_word) > 3 or (lang == "zh" and random_word.isalpha()):
        token_list[random_num] = "[MASK]"
        masked_l = " ".join(token_list)
        return masked_l
    else:
        return mask_line(line, language)


def filter_candidates(candidates, get_any_candidate=False):
    cand_list = []
    score_list = []
    for candidate in candidates:
        if not get_any_candidate and candidate["token_str"][:2] != "##" and candidate["token_str"].isalpha():
            cand = candidate["sequence"]
            score = candidate["score"]
            cand_list.append(cand)
            score_list.append('{0:.5f}'.format(score))
        elif get_any_candidate:
            cand = candidate["sequence"]
            score = candidate["score"]
            cand_list.append(cand)
            score_list.append('{0:.5f}'.format(score))
        if len(score_list) == TOPK:
            break
    if len(cand_list) < 1:
        return filter_candidates(candidates, get_any_candidate=True)
    else:
        return cand_list[0]


def infer_candidates(nlp, line):
    line = re.sub("’", "'", line)
    line = re.sub("…", "...", line)
    inputs = nlp._parse_and_tokenize(line)
    outputs = nlp._forward(inputs, return_tensors=True)
    input_ids = inputs["input_ids"][0]
    masked_index = torch.nonzero(input_ids == nlp.tokenizer.mask_token_id,
                                 as_tuple=False)
    logits = outputs[0, masked_index.item(), :]
    probs = logits.softmax(dim=0)
    values, predictions = probs.topk(TOPK)
    result = []
    for v, p in zip(values.tolist(), predictions.tolist()):
        tokens = input_ids.numpy()
        tokens[masked_index] = p
        # Filter padding out:
        tokens = tokens[np.where(tokens != nlp.tokenizer.pad_token_id)]
        l = []
        token_list = [nlp.tokenizer.decode([token], skip_special_tokens=True) for token in tokens]
        for idx, token in enumerate(token_list):
            if token.startswith('##'):
                l[-1] += token[2:]
            elif idx == masked_index.item():
                l += ['<b style="color: #ff0000;">', token, "</b>"]
            else:
                l += [token]
        sequence = " ".join(l).strip()
        result.append(
            {
                "sequence": sequence,
                "score": v,
                "token": p,
                "token_str": nlp.tokenizer.decode(p),
                "masked_index": masked_index.item()
            }
        )
    return result


def rewrite_poem(poem, ml_model=MODELS["ALBERTI"], masking=True, language="es"):
    nlp = pipeline("fill-mask", model=ml_model)
    unmasked_lines = []
    masked_lines = []
    for line in poem:
        if line == "":
            unmasked_lines.append("")
            masked_lines.append("")
            continue
        if masking:
            masked_line = mask_line(line, language)
        else:
            masked_line = line
        masked_lines.append(masked_line)
        unmasked_line_candidates = infer_candidates(nlp, masked_line)
        unmasked_line = filter_candidates(unmasked_line_candidates)
        unmasked_lines.append(unmasked_line)
        unmasked_poem = "<br>".join(unmasked_lines)
    return unmasked_poem, masked_lines


instructions_text_0 = st.sidebar.markdown(
    """# ALBERTI vs BERT 🥊

We present ALBERTI, our BERT-based multilingual model for poetry.""")

instructions_text_1 = st.sidebar.markdown(
    """We have trained bert on a huge (for poetry, that is) corpus of
multilingual poetry to try to get a more 'poetic' model. This is the result
of our work.

You can find more information on the [project's site](https://huggingface.co/flax-community/alberti-bert-base-multilingual-cased)""")

sample_chooser = st.sidebar.selectbox(
    "Choose a poem",
    list(SAMPLE_POEMS.keys())
)

instructions_text_2 = st.sidebar.markdown("""# How to use

You can choose from a list of example poems in Spanish, English, French, German,
Chinese and Arabic, but you can also paste a poem, or write it yourself!

Then click on 'Rewrite!' to do the masking and the fill-mask task on the chosen
poem and get the two new versions for each of the models.

The list of languages used on the training of ALBERTI are:

* Arabic
* Chinese
* Czech
* English
* Finnish
* French
* German
* Hungarian
* Italian
* Portuguese
* Russian
* Spanish""")

col1, col2, col3 = st.beta_columns(3)

st.markdown(
    """
    <style>
        label {
        font-size: 1rem !important;
        font-weight: bold !important;
        }
        .block-container {
        padding-left: 1rem !important;
        padding-right: 1rem !important;
        }
    </style>
    """, unsafe_allow_html=True)

if sample_chooser:
    model_list = set(MODELS.values())
    user_input = col1.text_area("Input poem",
                                "\n".join(SAMPLE_POEMS[sample_chooser]),
                                height=600)
    poem = user_input.split("\n")
    rewrite_button = col1.button("Rewrite!")
    if "[MASK]" in user_input or "<mask>" in user_input:
        col1.error("You don't have to mask the poem, we'll do it for you!")

if rewrite_button:
    lang = langid.classify(user_input)[0]
    unmasked_poem, masked_poem = rewrite_poem(poem, language=lang)
    user_input_2 = col2.write(f"""<b>Output poem from ALBERTI</b>


{unmasked_poem}""", unsafe_allow_html=True)
    unmasked_poem_2, _ = rewrite_poem(masked_poem, ml_model=MODELS["mBERT"],
                                      masking=False)
    user_input_3 = col3.write(f"""<b>Output poem from mBERT</b>

{unmasked_poem_2}""", unsafe_allow_html=True)