app.py

import time

import psutil
import streamlit as st
import torch
from langdetect import detect
from transformers import TextIteratorStreamer

from default_texts import default_texts
from generator import GeneratorFactory

device = torch.cuda.device_count() - 1

TRANSLATION_EN_TO_NL = "translation_en_to_nl"
TRANSLATION_NL_TO_EN = "translation_nl_to_en"

GENERATOR_LIST = [
    {
        "model_name": "yhavinga/ul2-base-en-nl",
        "desc": "UL2 base en->nl",
        "task": TRANSLATION_EN_TO_NL,
        "split_sentences": False,
    },
    {
        "model_name": "yhavinga/ul2-large-en-nl",
        "desc": "UL2 large en->nl",
        "task": TRANSLATION_EN_TO_NL,
        "split_sentences": False,
    },
    {
        "model_name": "Helsinki-NLP/opus-mt-en-nl",
        "desc": "Opus MT en->nl",
        "task": TRANSLATION_EN_TO_NL,
        "split_sentences": True,
    },
    {
        "model_name": "Helsinki-NLP/opus-mt-nl-en",
        "desc": "Opus MT nl->en",
        "task": TRANSLATION_NL_TO_EN,
        "split_sentences": True,
    },
    {
        "model_name": "yhavinga/t5-small-24L-ccmatrix-multi",
        "desc": "T5 small nl24 ccmatrix nl-en",
        "task": TRANSLATION_NL_TO_EN,
        "split_sentences": True,
    },
    {
        "model_name": "yhavinga/byt5-small-ccmatrix-en-nl",
        "desc": "ByT5 small ccmatrix en->nl",
        "task": TRANSLATION_EN_TO_NL,
        "split_sentences": True,
    },
    # {
    #     "model_name": "yhavinga/t5-base-36L-ccmatrix-multi",
    #     "desc": "T5 base nl36 ccmatrix en->nl",
    #     "task": TRANSLATION_EN_TO_NL,
    #     "split_sentences": True,
    # },
    # {
]


class StreamlitTextIteratorStreamer(TextIteratorStreamer):
    def __init__(
        self, output_placeholder, tokenizer, skip_prompt=False, **decode_kwargs
    ):
        super().__init__(tokenizer, skip_prompt, **decode_kwargs)
        self.output_placeholder = output_placeholder
        self.output_text = ""

    def on_finalized_text(self, text: str, stream_end: bool = False):
        self.output_text += text
        self.output_placeholder.markdown(self.output_text, unsafe_allow_html=True)
        super().on_finalized_text(text, stream_end)


def main():
    st.set_page_config(  # Alternate names: setup_page, page, layout
        page_title="Rosetta en/nl",  # String or None. Strings get appended with "• Streamlit".
        layout="wide",  # Can be "centered" or "wide". In the future also "dashboard", etc.
        initial_sidebar_state="auto",  # Can be "auto", "expanded", "collapsed"
        page_icon="📑",  # String, anything supported by st.image, or None.
    )

    if "generators" not in st.session_state:
        st.session_state["generators"] = GeneratorFactory(GENERATOR_LIST)
    generators = st.session_state["generators"]

    with open("style.css") as f:
        st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)

    st.sidebar.image("rosetta.png", width=200)
    st.sidebar.markdown(
        """# Rosetta
    Vertaal van en naar Engels"""
    )

    default_text = st.sidebar.radio(
        "Change default text",
        tuple(default_texts.keys()),
        index=0,
    )
    if default_text or "prompt_box" not in st.session_state:
        st.session_state["prompt_box"] = default_texts[default_text]["text"]

    # create a left and right column
    left, right = st.columns(2)
    text_area = left.text_area("Enter text", st.session_state.prompt_box, height=500)
    st.session_state["text"] = text_area

    # Sidebar parameters
    st.sidebar.title("Parameters:")
    num_beams = st.sidebar.number_input("Num beams", min_value=1, max_value=10, value=1)
    num_beam_groups = st.sidebar.number_input(
        "Num beam groups", min_value=1, max_value=10, value=1
    )
    length_penalty = st.sidebar.number_input(
        "Length penalty", min_value=0.0, max_value=2.0, value=1.2, step=0.1
    )
    st.sidebar.markdown(
        """For an explanation of the parameters, head over to the [Huggingface blog post about text generation](https://huggingface.co/blog/how-to-generate)
and the [Huggingface text generation interface doc](https://huggingface.co/transformers/main_classes/model.html?highlight=generate#transformers.generation_utils.GenerationMixin.generate).
"""
    )
    params = {
        "num_beams": num_beams,
        "num_beam_groups": num_beam_groups,
        "length_penalty": length_penalty,
        "early_stopping": True,
    }

    if left.button("Run"):
        memory = psutil.virtual_memory()

        language = detect(st.session_state.text)
        if language == "en":
            task = TRANSLATION_EN_TO_NL
        elif language == "nl":
            task = TRANSLATION_NL_TO_EN
        else:
            left.error(f"Language {language} not supported")
            return

        # Num beam groups should be a divisor of num beams
        if num_beams % num_beam_groups != 0:
            left.error("Num beams should be a multiple of num beam groups")
            return

        streaming_enabled = num_beams == 1
        if not streaming_enabled:
            left.markdown("*`num_beams > 1` so streaming is disabled*")

        for generator in generators.filter(task=task):
            model_container = right.container()
            model_container.markdown(f"🧮 **Model `{generator}`**")
            output_placeholder = model_container.empty()
            streamer = (
                StreamlitTextIteratorStreamer(output_placeholder, generator.tokenizer)
                if streaming_enabled
                else None
            )
            time_start = time.time()
            result, params_used = generator.generate(
                text=st.session_state.text, streamer=streamer, **params
            )
            time_end = time.time()
            time_diff = time_end - time_start

            if not streaming_enabled:
                right.write(result.replace("\n", "  \n"))
            text_line = ", ".join([f"{k}={v}" for k, v in params_used.items()])
            right.markdown(f"    🕙 *generated in {time_diff:.2f}s, `{text_line}`*")

        st.write(
            f"""
        ---
        *Memory: {memory.total / 10**9:.2f}GB, used: {memory.percent}%, available: {memory.available / 10**9:.2f}GB*
        """
        )


if __name__ == "__main__":
    main()