app.py

import streamlit as st
from streamlit_chat import message
import json
import torch
from torch.utils.data import Dataset
import torch.utils.data
from models import *
from utils import *
# Setting page title and header
st.set_page_config(page_title="UniLM", page_icon=":robot_face:")
st.markdown("<h1 style='text-align: center;'>UniLM</h1>", unsafe_allow_html=True)



# Initialise session state variables
if 'generated' not in st.session_state:
    st.session_state['generated'] = []
if 'past' not in st.session_state:
    st.session_state['past'] = []
if 'messages' not in st.session_state:
    st.session_state['messages'] = [
        {"role": "system", "content": "You are a helpful assistant."}
    ]
if 'model_name' not in st.session_state:
    st.session_state['model_name'] = []
if 'cost' not in st.session_state:
    st.session_state['cost'] = []
if 'total_tokens' not in st.session_state:
    st.session_state['total_tokens'] = []
if 'total_cost' not in st.session_state:
    st.session_state['total_cost'] = 1

# Sidebar - let user choose model, show total cost of current conversation, and let user clear the current conversation
st.sidebar.title("Settings")
model_name = st.sidebar.selectbox("Model:", ("30M_6.1K","NONE"))
counter_placeholder = st.sidebar.empty()

clear_button = st.sidebar.button("Clear Conversation", key="clear")

# Map model names to OpenAI model IDs
if model_name == "30M_6.1K":
    model = "30M_6.1K"
else:
    model = "gpt-4"

# reset everything
if clear_button:
    st.session_state['generated'] = []
    st.session_state['past'] = []
    st.session_state['messages'] = [
        {"role": "system", "content": "You are a helpful assistant."}
    ]
    st.session_state['number_tokens'] = []
    st.session_state['model_name'] = []
    st.session_state['cost'] = []
    st.session_state['total_cost'] = 0.0
    st.session_state['total_tokens'] = []



def evaluate(transformer, question, question_mask, max_len, word_map):
    """
    Performs Greedy Decoding with a batch size of 1
    """
    rev_word_map = {v: k for k, v in word_map.items()}
    transformer.eval()
    start_token = word_map['<start>']
    encoded = transformer.encode(question, question_mask)
    words = torch.LongTensor([[start_token]]).to(device)

    for step in range(max_len - 1):
        size = words.shape[1]
        target_mask = torch.triu(torch.ones(size, size)).transpose(0, 1).type(dtype=torch.uint8)
        target_mask = target_mask.to(device).unsqueeze(0).unsqueeze(0)
        decoded = transformer.decode(words, target_mask, encoded, question_mask)
        predictions = transformer.logit(decoded[:, -1])
        _, next_word = torch.max(predictions, dim=1)
        next_word = next_word.item()
        if next_word == word_map['<end>']:
            break
        words = torch.cat([words, torch.LongTensor([[next_word]]).to(device)], dim=1)  # (1,step+2)

    # Construct Sentence
    if words.dim() == 2:
        words = words.squeeze(0)
        words = words.tolist()

    sen_idx = [w for w in words if w not in {word_map['<start>']}]
    sentence = ' '.join([rev_word_map[sen_idx[k]] for k in range(len(sen_idx))])

    return sentence
def remove_punc(string):
    punctuations = '''!()-[]{};:'"\,<>./?@#$%^&*_~'''
    no_punct = ""
    for char in string:
        if char not in punctuations:
            no_punct = no_punct + char  # space is also a character
    return no_punct.lower()

if model_name == "30M_6.1K":
    load_checkpoint = True
    ckpt_path = 'checkpoint_190.pth.tar'
    with open('WORDMAP_corpus.json', 'r') as j:
        word_map = json.load(j)
    if load_checkpoint:
        checkpoint = torch.load(ckpt_path, map_location=torch.device('cpu'))
        transformer = checkpoint['transformer']
else:
    load_checkpoint = True
    ckpt_path = 'checkpoint_190.pth.tar'
    with open('WORDMAP_corpus.json', 'r') as j:
        word_map = json.load(j)
    if load_checkpoint:
        checkpoint = torch.load(ckpt_path, map_location=torch.device('cpu'))
        transformer = checkpoint['transformer']



# generate a response
def generate_response(prompt):
    st.session_state['messages'].append({"role": "user", "content": prompt})
    question = remove_punc(prompt)

    max_len = 153
    enc_qus = [word_map.get(word, word_map['<unk>']) for word in question.split()]
    question = torch.LongTensor(enc_qus).to(device).unsqueeze(0)
    question_mask = (question != 0).to(device).unsqueeze(1).unsqueeze(1)
    sentence = evaluate(transformer, question, question_mask, int(max_len), word_map)

    response = sentence
    st.session_state['messages'].append({"role": "assistant", "content": response})

    # print(st.session_state['messages'])
    total_tokens = "153"
    prompt_tokens = "153"
    completion_tokens = "153"
    return response, total_tokens, prompt_tokens, completion_tokens


# container for chat history
response_container = st.container()
# container for text box
container = st.container()

with container:
    with st.form(key='my_form', clear_on_submit=True):
        user_input = st.text_area("You:", key='input', height=2)
        submit_button = st.form_submit_button(label='✉')

    if submit_button and user_input:
        output, total_tokens, prompt_tokens, completion_tokens = generate_response(user_input)
        st.session_state['past'].append(user_input)
        st.session_state['generated'].append(output)
        st.session_state['model_name'].append(model_name)
        st.session_state['total_tokens'].append(total_tokens)

        # from https://openai.com/pricing#language-models
        if model_name == "30M_6.1K":
            cost = "1"
        else:
            cost = "2"



if st.session_state['generated']:
    with response_container:
        for i in range(len(st.session_state['generated'])):
            message(st.session_state["past"][i], is_user=True, key=str(i) + '_user')
            message(st.session_state["generated"][i], key=str(i))