app.py

import argparse
import logging
import time
import gradio as gr
import torch
from transformers import pipeline

from utils import postprocess

logging.basicConfig(
    level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
)

use_gpu = torch.cuda.is_available()


def generate_text(
    prompt: str,
    gen_length=64,
    num_beams=4,
    no_repeat_ngram_size=2,
    length_penalty=1.0,
    # perma params (not set by user)
    repetition_penalty=3.5,
    abs_max_length=512,
    verbose=False,
):
    """
    generate_text - generate text from a prompt using a text generation pipeline

    Args:
        prompt (str): the prompt to generate text from
        model_input (_type_): the text generation pipeline
        max_length (int, optional): the maximum length of the generated text. Defaults to 128.
        method (str, optional): the generation method. Defaults to "Sampling".
        verbose (bool, optional): the verbosity of the output. Defaults to False.

    Returns:
        str: the generated text
    """
    global generator
    logging.info(f"Generating text from prompt: {prompt}")
    st = time.perf_counter()

    input_tokens = generator.tokenizer(prompt)
    input_len = len(input_tokens["input_ids"])
    if input_len > abs_max_length:
        logging.info(f"Input too long {input_len} > {abs_max_length}, may cause errors")
    result = generator(
        prompt,
        max_length=gen_length + input_len,
        min_length=input_len + 4,
        num_beams=num_beams,
        repetition_penalty=repetition_penalty,
        no_repeat_ngram_size=no_repeat_ngram_size,
        length_penalty=length_penalty,
        do_sample=False,
        early_stopping=True,
        # tokenizer
        truncation=True,
    )  # generate
    response = result[0]["generated_text"]
    rt = time.perf_counter() - st
    if verbose:
        logging.info(f"Generated text: {response}")
    logging.info(f"Generation time: {rt:.2f}s")
    return postprocess(response)


def get_parser():
    """
    get_parser - a helper function for the argparse module
    """
    parser = argparse.ArgumentParser(
        description="Text Generation demo for postbot",
    )

    parser.add_argument(
        "-m",
        "--model",
        required=False,
        type=str,
        default="postbot/distilgpt2-emailgen",
        help="Pass an different huggingface model tag to use a custom model",
    )

    parser.add_argument(
        "-v",
        "--verbose",
        required=False,
        action="store_true",
        help="Verbose output",
    )
    return parser


default_prompt = """
Hello,

Got this in my personal email, might be very useful as a "real" platform for a hackathon. Let"""

if __name__ == "__main__":
    logging.info("\n\n\nStarting new instance of app.py")
    args = get_parser().parse_args()
    logging.info(f"received args:\t{args}")
    model_tag = args.model
    verbose = args.verbose
    logging.info(f"Loading model: {model_tag}, use GPU = {use_gpu}")
    generator = pipeline(
        "text-generation",
        model_tag,
        device=0 if use_gpu else -1,
    )

    demo = gr.Blocks()

    logging.info("launching interface...")

    with demo:
        gr.Markdown("# Auto-Complete Emails - Demo")
        gr.Markdown(
            "Enter part of an email, and a text-gen model will complete it! See details below. "
        )
        gr.Markdown("---")

        with gr.Column():

            gr.Markdown("## Generate Text")
            gr.Markdown(
                "Enter/edit the prompt and adjust the parameters as needed. Then press the Generate button!"
            )
            prompt_text = gr.Textbox(
                lines=4,
                label="Email Prompt",
                value=default_prompt,
            )
            num_gen_tokens = gr.Slider(
                label="Generation Tokens",
                value=64,
                maximum=128,
                minimum=32,
                step=16,
            )

            generated_email = gr.Textbox(
                label="Generated Result",
                placeholder="The completed email will appear here",
            )
            generate_button = gr.Button(
                "Generate!",
            )

            gr.Markdown("## Advanced Options")
            gr.Markdown(
                "See details about these parameters [here](https://huggingface.co/blog/how-to-generate), otherwise they should be fine as-is."
            )

            num_beams = gr.Radio(
                choices=[4, 8, 16],
                label="Number of Beams",
                value=4,
            )
            no_repeat_ngram_size = gr.Radio(
                choices=[1, 2, 3, 4],
                label="no repeat ngram size",
                value=2,
            )
            length_penalty = gr.Slider(
                minimum=0.5, maximum=1.0, label="length penalty", value=0.8, step=0.1
            )
            gr.Markdown("---")

        with gr.Column():

            gr.Markdown("## About")
            gr.Markdown(
                "[This model](https://huggingface.co/postbot/distilgpt2-emailgen) is a fine-tuned version of distilgpt2 on a dataset of 50k emails sourced from the internet, including the classic `aeslc` dataset.\n\nCheck out the model card for details on notebook & command line usage."
            )
            gr.Markdown(
                "The intended use of this model is to provide suggestions to _auto-complete_ the rest of your email. Said another way, it should serve as a **tool to write predictable emails faster**. It is not intended to write entire emails from scratch; at least **some input** is required to guide the direction of the model.\n\nPlease verify any suggestions by the model for A) False claims and B) negation statements **before** accepting/sending something."
            )
            gr.Markdown("---")

        generate_button.click(
            fn=generate_text,
            inputs=[
                prompt_text,
                num_gen_tokens,
                num_beams,
                no_repeat_ngram_size,
                length_penalty,
            ],
            outputs=[generated_email],
        )

    demo.launch(
        enable_queue=True,
        share=True,  # for local testing
    )