app.py

"""
app.py - the main file for the app. This builds the app and runs it. 

"""

import torch
from transformers import pipeline
from cleantext import clean
from pathlib import Path
import warnings
import time
import argparse
import logging
import gradio as gr
import os
import sys
from os.path import dirname
import nltk
from converse import discussion
from grammar_improve import (
    detect_propers,
    load_ns_checker,
    neuspell_correct,
    remove_repeated_words,
    remove_trailing_punctuation,
    build_symspell_obj,
    symspeller,
)

from utils import (
    cleantxt_wrap,
    corr,
)

nltk.download("stopwords")  # TODO: find where this requirement originates from

sys.path.append(dirname(dirname(os.path.abspath(__file__))))
warnings.filterwarnings(action="ignore", message=".*gradient_checkpointing*")
import transformers

transformers.logging.set_verbosity_error()
logging.basicConfig()
cwd = Path.cwd()
my_cwd = str(cwd.resolve())  # string so it can be passed to os.path() objects


def chat(trivia_query):
    history = []
    response = ask_gpt(message=trivia_query, chat_pipe=my_chatbot)
    history = [trivia_query, response]
    html = ""
    for item in history:
        html += f"<b>{item}</b> <br><br>"

    html += ""

    return html


def ask_gpt(
    message: str,
    chat_pipe,
    speaker="person alpha",
    responder="person beta",
    max_len=64,
    top_p=0.95,
    top_k=20,
    temperature=0.3,
):
    """

    ask_gpt - a function that takes in a prompt and generates a response using the pipeline. This interacts the discussion function.

    Parameters:
        message (str): the question to ask the bot
        chat_pipe (str): the chat_pipe to use for the bot (default: "pszemraj/Ballpark-Trivia-XL")
        speaker (str): the name of the speaker (default: "person alpha")
        responder (str): the name of the responder (default: "person beta")
        max_len (int): the maximum length of the response (default: 128)
        top_p (float): the top probability threshold (default: 0.95)
        top_k (int): the top k threshold (default: 50)
        temperature (float): the temperature of the response (default: 0.7)
    """

    st = time.perf_counter()
    prompt = clean(message)  # clean user input
    prompt = prompt.strip()  # get rid of any extra whitespace
    in_len = len(prompt)
    if in_len > 512:
        prompt = prompt[-512:]  # truncate to 512 chars
        print(f"Truncated prompt to last 512 chars: started with {in_len}")
        max_len = min(max_len, 512)

    resp = discussion(
        prompt_text=prompt,
        pipeline=chat_pipe,
        speaker=speaker,
        responder=responder,
        top_p=top_p,
        top_k=top_k,
        temperature=temperature,
        max_length=max_len,
        timeout=30,
    )
    gpt_et = time.perf_counter()
    gpt_rt = round(gpt_et - st, 2)
    rawtxt = resp["out_text"]
    # check for proper nouns
    if basic_sc and not detect_propers(rawtxt):
        cln_resp = symspeller(rawtxt, sym_checker=schnellspell)
    elif not detect_propers(rawtxt):
        cln_resp = neuspell_correct(rawtxt, checker=ns_checker)
    else:
        # no correction needed
        cln_resp = rawtxt.strip()
    bot_resp = corr(remove_repeated_words(cln_resp))
    print(f"\nthe prompt was:\n\t{message}\nand the response was:\n\t{bot_resp}\n")
    corr_rt = round(time.perf_counter() - gpt_et, 4)
    print(
        f"took {gpt_rt + corr_rt} sec to respond, {gpt_rt} for GPT, {corr_rt} for correction\n"
    )
    return remove_trailing_punctuation(bot_resp)


def get_parser():
    """
    get_parser - a helper function for the argparse module
    """
    parser = argparse.ArgumentParser(
        description="submit a question, GPT model responds "
    )
    parser.add_argument(
        "-m",
        "--model",
        required=False,
        type=str,
        default="pszemraj/Ballpark-Trivia-XL",  # default model
        help="the model to use for the chatbot on https://huggingface.co/models OR a path to a local model",
    )
    parser.add_argument(
        "--basic-sc",
        required=False,
        default=False,
        action="store_true",
        help="turn on symspell (baseline) correction instead of the more advanced neural net models",
    )

    parser.add_argument(
        "--verbose",
        action="store_true",
        default=False,
        help="turn on verbose logging",
    )
    return parser


if __name__ == "__main__":
    args = get_parser().parse_args()
    default_model = str(args.model)
    model_loc = Path(default_model)  # if the model is a path, use it
    basic_sc = args.basic_sc  # whether to use the baseline spellchecker
    basic_sc  = True # TODO: remove once neuspell fixed
    device = 0 if torch.cuda.is_available() else -1
    print(f"CUDA avail is {torch.cuda.is_available()}")

    my_chatbot = (
        pipeline("text-generation", model=model_loc.resolve(), device=device)
        if model_loc.exists() and model_loc.is_dir()
        else pipeline("text-generation", model=default_model, device=device)
    )  # if the model is a name, use it. stays on CPU if no GPU available
    print(f"using model {my_chatbot.model}")

    if basic_sc:
        print("Using the baseline spellchecker")
        schnellspell = build_symspell_obj()
    else:
        print("using Neuspell spell checker")
        ns_checker = load_ns_checker(fast=False)

    print(f"using model stored here: \n {model_loc} \n")
    iface = gr.Interface(
        chat,
        inputs=["text"],
        outputs="html",
        examples_per_page=10,
        examples=[
            "Which President gave us the metric system?",
            "Who let the dogs out?",
            "Where does the term \"ground floor\" come from?",
            "What is the highest point on the globe?",
            "Why do we wear white clothes on our wedding days?",
            "What does the oval and squiggle on a US passport represent?",
            "Why is an electrical socket called a \"socket\", and not, say, a \"bottle\"?",
            "Where are the most active volcanoes on the earth?",
            "What is a cold-blood or cold-blooded animal?",
            "Why do we play volleyball on August 20th?",
            "What is water?",
            "Difference between U, V and W",
            "What is the official language of Vatican City?",
            "In what city is the CDC located?",
            "What are the names of the two major political parties in France?",
            "Who was Charles de Gaulle?",
            "Where is Stonehenge located?",
            "How many moons does Saturn have?",
            "Who invented the telescope?",
            "Who is your daddy and what does he do?",
            "When did Christopher Columbus come to America?",
            "Why are there interstate highways that have only one lane on each side?",
            "Which flavor of ice cream is the most popular in Switzerland?",
            "Who wrote The Jungle?",
            "Where were Benedict Arnold and Gen. Washington when the war started?",
        ],
        title=f"Ballpark Trivia: {default_model} Model",
        description=f"Are you frequently asked google-able Trivia questions and annoyed by it? Well, this is the app for you! Ballpark Trivia Bot answers any trivia question with something that sounds plausible but is probably not 100% correct. \n\n One might say.. the answers are in the right ballpark.",
        article="Further details can be found in the [model card](https://huggingface.co/pszemraj/Ballpark-Trivia-XL).\n\n"
        "**Important Notes & About:**\n\n"
        "1. the model can take up to 60 seconds to respond sometimes, patience is a virtue.\n"
        "2. the model started from a pretrained checkpoint, and was trained on several different datasets. Anything it says should be fact-checked before being regarded as a true statement.\n"
        "3. Some params are still being tweaked (in future, will have them as inputs) any feedback is welcome   :)\n",
        css="""
            .chatbox {display:flex;flex-direction:column}
            .user_msg, .resp_msg {padding:4px;margin-bottom:4px;border-radius:4px;width:80%}
            .user_msg {background-color:cornflowerblue;color:white;align-self:start}
            .resp_msg {background-color:lightgray;align-self:self-end}
        """,
        allow_screenshot=True,
        allow_flagging="never",
        theme="dark",
    )

    # launch the gradio interface and start the server
    iface.launch(
        # prevent_thread_lock=True,
        # share=True,
        enable_queue=True,  # also allows for dealing with multiple users simultaneously (per newer gradio version)
    )