app.py

import os
from threading import Thread
from typing import Iterator

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

MAX_MAX_NEW_TOKENS = 2048
DEFAULT_MAX_NEW_TOKENS = 1024
MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))

DESCRIPTION = """\
# L-MChat

This Space demonstrates L-MChat, a pair of chat-optimized language models:

- Fast-Model: `Artples/L-MChat-Small`
- Quality-Model: `Artples/L-MChat-7b`

By default the Quality-Model is used. You can switch to the Fast-Model if you prefer lower latency over maximum quality.
"""

if not torch.cuda.is_available():
    DESCRIPTION += "\n\n<p>Running on CPU – this demo is intended for GPU and may be extremely slow.</p>"

model_dict = {
    "Fast-Model": "Artples/L-MChat-Small",
    "Quality-Model": "Artples/L-MChat-7b",
}

_model_cache: dict[str, AutoModelForCausalLM] = {}
_tokenizer_cache: dict[str, AutoTokenizer] = {}


def get_model_and_tokenizer(model_id: str):
    """Lazy-load and cache model and tokenizer per model id."""
    if model_id not in _model_cache:
        model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
        tokenizer = AutoTokenizer.from_pretrained(model_id)
        tokenizer.use_default_system_prompt = False
        _model_cache[model_id] = model
        _tokenizer_cache[model_id] = tokenizer
    return _model_cache[model_id], _tokenizer_cache[model_id]


@spaces.GPU(enable_queue=True, duration=90)
def generate(
    message: str,
    chat_history: list[tuple[str, str]],
    system_prompt: str,
    model_choice: str,
    max_new_tokens: int = DEFAULT_MAX_NEW_TOKENS,
    temperature: float = 0.6,
    top_p: float = 0.9,
    top_k: int = 50,
    repetition_penalty: float = 1.2,
) -> Iterator[str]:
    model_id = model_dict[model_choice]
    model, tokenizer = get_model_and_tokenizer(model_id)

    conversation: list[dict[str, str]] = []
    if system_prompt:
        conversation.append({"role": "system", "content": system_prompt})

    for user, assistant in chat_history:
        conversation.append({"role": "user", "content": user})
        if assistant is not None:
            conversation.append({"role": "assistant", "content": assistant})

    conversation.append({"role": "user", "content": message})

    input_ids = tokenizer.apply_chat_template(
        conversation,
        return_tensors="pt",
        add_generation_prompt=True,
    )

    if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH:
        input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:]
        gr.Warning(
            f"Trimmed input from conversation as it was longer than "
            f"{MAX_INPUT_TOKEN_LENGTH} tokens."
        )

    input_ids = input_ids.to(model.device)

    streamer = TextIteratorStreamer(
        tokenizer,
        timeout=10.0,
        skip_prompt=True,
        skip_special_tokens=True,
    )

    generate_kwargs = dict(
        input_ids=input_ids,
        streamer=streamer,
        max_new_tokens=min(max_new_tokens, MAX_MAX_NEW_TOKENS),
        do_sample=True,
        temperature=temperature,
        top_p=top_p,
        top_k=top_k,
        repetition_penalty=repetition_penalty,
    )

    thread = Thread(target=model.generate, kwargs=generate_kwargs)
    thread.start()

    outputs: list[str] = []
    for text in streamer:
        outputs.append(text)
        yield "".join(outputs)


chat_interface = gr.ChatInterface(
    fn=generate,
    additional_inputs=[
        gr.Textbox(label="System prompt", lines=6),
        gr.Radio(
            ["Fast-Model", "Quality-Model"],
            label="Model",
            value="Quality-Model",
        ),
        gr.Slider(
            label="Max new tokens",
            minimum=1,
            maximum=MAX_MAX_NEW_TOKENS,
            step=1,
            value=DEFAULT_MAX_NEW_TOKENS,
        ),
        gr.Slider(
            label="Temperature",
            minimum=0.1,
            maximum=4.0,
            step=0.1,
            value=0.6,
        ),
        gr.Slider(
            label="Top-p (nucleus sampling)",
            minimum=0.05,
            maximum=1.0,
            step=0.05,
            value=0.9,
        ),
        gr.Slider(
            label="Top-k",
            minimum=1,
            maximum=1000,
            step=1,
            value=50,
        ),
        gr.Slider(
            label="Repetition penalty",
            minimum=1.0,
            maximum=2.0,
            step=0.05,
            value=1.2,
        ),
    ],
    stop_btn=None,
    examples=[
        ["Hello there! How are you doing?"],
        ["Can you explain briefly to me what is the Python programming language?"],
        ["Explain the plot of Cinderella in a sentence."],
        ["How many hours does it take a man to eat a Helicopter?"],
        ["Write a 100-word article on 'Benefits of Open-Source in AI research'"],
    ],
)

# Load external CSS from styles.css and inject it as an HTML <style> block
custom_css = ""
css_path = "styles.css"
if os.path.exists(css_path):
    try:
        with open(css_path, encoding="utf-8") as f:
            custom_css = f"<style>{f.read()}</style>"
    except Exception:
        custom_css = ""

with gr.Blocks() as demo:
    if custom_css:
        gr.HTML(custom_css)
    gr.Markdown(DESCRIPTION)
    chat_interface.render()

if __name__ == "__main__":
    demo.queue(max_size=20).launch()