app.py

import os
import streamlit as st
import transformers
import torch
import tokenizers
from typing import List, Dict

st.subheader('Эта демонстрация позволяет поэксперементировать с моделями, которые оценивают, насколько предлагаемый ответ подходит к контексту диалога.')
model_name = st.selectbox(
    'Выберите модель',
    ('tinkoff-ai/response-quality-classifier-tiny', 'tinkoff-ai/response-quality-classifier-base', 'tinkoff-ai/response-quality-classifier-large')
)
auth_token = os.environ.get('TOKEN') or True

@st.cache(hash_funcs={tokenizers.Tokenizer: lambda tokenizer: hash(tokenizer.to_str())}, allow_output_mutation=True)
def load_model(model_name: str):
    with st.spinner('Loading models...'):
        tokenizer = transformers.AutoTokenizer.from_pretrained(model_name, use_auth_token=auth_token)
        model = transformers.AutoModelForSequenceClassification.from_pretrained(model_name, use_auth_token=auth_token)
        if torch.cuda.is_available():
            model = model.cuda()
        return tokenizer, model

context_3 = 'привет'
context_2 = 'привет!'
context_1 = 'как дела?'

st.markdown('👱🏻‍♀️ **Настя**: ' + context_3)
st.markdown('🤖 **Диалоговый агент**: ' + context_2)
st.markdown('👱🏻‍♀️ **Настя**: ' + context_1)
response = st.text_input('🤖 Диалоговый агент:', 'норм')
sample = {
    'context_3': context_3,
    'context_2': context_2,
    'context_1': context_1,
    'response': response
}

SEP_TOKEN = '[SEP]'
CLS_TOKEN = '[CLS]'
RESPONSE_TOKEN = '[RESPONSE_TOKEN]'
MAX_SEQ_LENGTH = 128
sorted_dialog_columns = ['context_3', 'context_2', 'context_1', 'response']


def tokenize_dialog_data(
    tokenizer: transformers.PreTrainedTokenizer,
    sample: Dict,
    max_seq_length: int,
    sorted_dialog_columns: List,
):
    """
    Tokenize both contexts and response of dialog data separately
    """
    len_message_history = len(sorted_dialog_columns)
    max_seq_length = min(max_seq_length, tokenizer.model_max_length)
    max_each_message_length = max_seq_length // len_message_history - 1
    messages = [sample[k] for k in sorted_dialog_columns]
    result = {model_input_name: [] for model_input_name in tokenizer.model_input_names}
    messages = [str(message) if message is not None else '' for message in messages]
    tokens = tokenizer(
        messages, padding=False, max_length=max_each_message_length, truncation=True, add_special_tokens=False
    )
    for model_input_name in tokens.keys():
        result[model_input_name].extend(tokens[model_input_name])
    return result


def merge_dialog_data(
    tokenizer: transformers.PreTrainedTokenizer, 
    sample: Dict
):
    cls_token = tokenizer(CLS_TOKEN, add_special_tokens=False)
    sep_token = tokenizer(SEP_TOKEN, add_special_tokens=False)
    response_token = tokenizer(RESPONSE_TOKEN, add_special_tokens=False)
    model_input_names = tokenizer.model_input_names
    result = {}
    for model_input_name in model_input_names:
        tokens = []
        tokens.extend(cls_token[model_input_name])
        for i, message in enumerate(sample[model_input_name]):
            tokens.extend(message)
            if i < len(sample[model_input_name]) - 2:
                tokens.extend(sep_token[model_input_name])
            elif i == len(sample[model_input_name]) - 2:
                tokens.extend(response_token[model_input_name])
        result[model_input_name] = torch.tensor([tokens])
        if torch.cuda.is_available():
           result[model_input_name] = result[model_input_name].cuda() 
    return result


@st.cache
def inference(model_name: str, sample: dict):
    tokenizer, model = load_model(model_name)
    tokenized_dialog = tokenize_dialog_data(tokenizer, sample, MAX_SEQ_LENGTH, sorted_dialog_columns)
    tokens = merge_dialog_data(tokenizer, tokenized_dialog)
    with torch.inference_mode():
        logits = model(**tokens).logits
        probas = torch.sigmoid(logits)[0].cpu().detach().numpy().tolist()
    return probas

with st.spinner('Running inference...'):
    probas = inference(model_name, sample)
st.metric(
    label='Вероятность того, что последний ответ диалогового агента релевантный',
    value=round(probas[0], 3)
)
st.metric(
    label='Вероятность того, что последний ответ диалогового агента вовлечённый',
    value=round(probas[1], 3)
)