app.py

import sys
import json

import gradio as gr
import torch
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer

sys.path.insert(0, './petals/')
sys.path.insert(0, './personalized-chat-bot/')


from petals.client.remote_model import DistributedBloomForCausalLM
from models.personality_clustering import PersonalityClustering

MODEL_NAME = "bigscience/bloom-petals"
tokenizer_bloomd = transformers.BloomTokenizerFast.from_pretrained(MODEL_NAME)
model_bloomd = DistributedBloomForCausalLM.from_pretrained(MODEL_NAME, low_cpu_mem_usage=True)

tokenizer_DialoGPT_small = AutoTokenizer.from_pretrained("microsoft/DialoGPT-small")
model_DialoGPT_small = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-small")

tokenizer_DialoGPT_medium = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
model_DialoGPT_medium = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")

tokenizer_DialoGPT_large = AutoTokenizer.from_pretrained("microsoft/DialoGPT-large")
model_DialoGPT_large = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-large")


def predict_common_bloom(model, tokenizer, input_text, history, person_description, number_of_new_tokens):
    new_user_input_ids = tokenizer.encode(input_text + '\n', return_tensors='pt')
    person_description_ids = tokenizer.encode(person_description + '\n', return_tensors='pt')
    print('Started predict_common_bloom')
    print(f'history: {history}')
    if history != []:
        bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)
    else:
        bot_input_ids = new_user_input_ids
    print(f'bot_input_ids: {bot_input_ids}')
    input_with_desc_ids = torch.cat([person_description_ids, bot_input_ids], dim=-1)

    history = model.generate(
        input_with_desc_ids,
        max_new_tokens=number_of_new_tokens,
        pad_token_id=tokenizer.eos_token_id
    ).tolist()
    print(f'history: {history}')
    history[0] = history[0][len(person_description_ids[0]):]

    decode_all = tokenizer.decode(history[0][:len(bot_input_ids[0])])
    all_responses = tokenizer.decode(history[0][len(bot_input_ids[0]):]).split('\n')
    if all_responses[0]:
        decode_all += all_responses[0] + '\n'
    else:
        decode_all += all_responses[1] + '\n'
    print(f'decode_all: {decode_all}')

    history_new = tokenizer.encode(decode_all, return_tensors='pt')
    print(f'history_new: {history_new}')

    decode_all_split = decode_all.split('\n')
    print(f'decode_all_split: {decode_all_split}')

    response_new = [(decode_all_split[i], decode_all_split[i + 1]) for i in range(0, len(decode_all_split) - 1, 2)]
    print(f'response_new: {response_new}')

    return response_new, history_new


def predict_cluster_bloom(model, tokenizer, input_text, history, person_description, number_of_new_tokens):
    new_user_input_ids = tokenizer.encode(input_text + '\n', return_tensors='pt')
    print('Started predict_common_bloom')
    print(f'history: {history}')
    if history != []:
        bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)
    else:
        bot_input_ids = new_user_input_ids
    print(f'bot_input_ids: {bot_input_ids}')

    history = model.generate(
        bot_input_ids,
        max_new_tokens=number_of_new_tokens,
        pad_token_id=tokenizer.eos_token_id
    ).tolist()
    print(f'history: {history}')

    decode_all = tokenizer.decode(history[0][:len(bot_input_ids[0])])
    all_responses = tokenizer.decode(history[0][len(bot_input_ids[0]):]).split('\n')
    if all_responses[0]:
        decode_all += all_responses[0] + '\n'
    else:
        decode_all += all_responses[1] + '\n'
    print(f'decode_all: {decode_all}')

    history_new = tokenizer.encode(decode_all, return_tensors='pt')
    print(f'history_new: {history_new}')

    decode_all_split = decode_all.split('\n')
    print(f'decode_all_split: {decode_all_split}')

    response_new = [(decode_all_split[i], decode_all_split[i + 1]) for i in range(0, len(decode_all_split) - 1, 2)]
    print(f'response_new: {response_new}')

    return response_new, history_new


def predict_dialo_gpt(model, tokenizer, input_text, history, person_description, number_of_new_tokens):
    person_description_ids = tokenizer.encode(person_description + tokenizer.eos_token, return_tensors='pt')
    new_user_input_ids = tokenizer.encode(input_text + tokenizer.eos_token, return_tensors='pt')

    bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)
    input_with_desc_ids = torch.cat([person_description_ids, bot_input_ids], dim=-1)
    history = model.generate(
        input_with_desc_ids,
        max_new_tokens=number_of_new_tokens,
        pad_token_id=tokenizer.eos_token_id
    ).tolist()
    history[0] = history[0][len(person_description_ids[0]):]
    response = tokenizer.decode(history[0]).split("<|endoftext|>")
    response = [(response[i], response[i + 1]) for i in range(0, len(response) - 1, 2)]

    return response, history


def predict(
        input_text,
        history=None,
        person_description=None,
        number_of_new_tokens=10,
        model_name=None,
        del_hist=None
):

    if history is None or del_hist == 'delete history':
        history = []

    if model_name == 'DialoGPT-small':
        model = model_DialoGPT_small
        tokenizer = tokenizer_DialoGPT_small
        return predict_dialo_gpt(model, tokenizer, input_text, history, person_description, number_of_new_tokens)
    elif model_name == 'DialoGPT-medium':
        model = model_DialoGPT_medium
        tokenizer = tokenizer_DialoGPT_medium
        return predict_dialo_gpt(model, tokenizer, input_text, history, person_description, number_of_new_tokens)
    elif model_name == 'DialoGPT-large':
        model = model_DialoGPT_large
        tokenizer = tokenizer_DialoGPT_large
        return predict_dialo_gpt(model, tokenizer, input_text, history, person_description, number_of_new_tokens)
    elif model_name == 'bloom-petals':
        model = model_bloomd
        tokenizer = tokenizer_bloomd
        print(f'Lets go history: {history}')
        return predict_common_bloom(model, tokenizer, input_text, history, person_description, number_of_new_tokens)
    elif model_name == 'bloom-petals-cluster':
        model = model_bloomd
        tokenizer = tokenizer_bloomd
        print(f'Lets go history: {history}')
        return predict_cluster_bloom(model, tokenizer, input_text, history, person_description, number_of_new_tokens)
    else:
        model_name = 'DialoGPT-medium'
        model = model_DialoGPT_medium
        tokenizer = tokenizer_DialoGPT_medium
        return predict_dialo_gpt(model, tokenizer, input_text, history, person_description, number_of_new_tokens)


gr.Interface(
    fn=predict,
    inputs=[
        gr.Textbox(label='Input message', lines=1, placeholder="Enter your message..."),
        "state",
        gr.Textbox(label='Person Description', lines=2, placeholder="Enter a description of the person..."),
        gr.Slider(label='Number of new tokens', minimum=2, maximum=100, value=10),
        gr.Radio(
            label='Model name',
            choices=[
                'DialoGPT-small',
                'DialoGPT-medium',
                'DialoGPT-large',
                'bloom-petals',
                'bloom-petals-cluster',
            ]
        ),
        gr.Radio(
            label='Delete history',
            value="Don't delete history",
            choices=[
                'delete history',
                "Don't delete history"
            ]),
    ],
    outputs=[gr.Chatbot(label='History of the dialogue'), "state"],
).launch(),