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

DESCRIPTION = """\
# Gemma 2 2B Neogenesis ITA 💎🌍🇮🇹

Fine-tuned version of Google/gemma-2-2b-it to improve the performance on the Italian language.
Small (2.6 B parameters) but good model, with 8k context length.

[🪪 **Model card**](https://huggingface.co/anakin87/gemma-2-2b-neogenesis-ita)

[📓 **Kaggle notebook**](https://www.kaggle.com/code/anakin87/post-training-gemma-for-italian-and-beyond) - Learn how this model was trained.
"""

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

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

model_id = "anakin87/gemma-2-2b-neogenesis-ita"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype=torch.bfloat16,
)
model.config.sliding_window = 4096
model.eval()


@spaces.GPU
def generate(
    message: str,
    chat_history: list[dict],
    max_new_tokens: int = 1024,
    temperature: float = 0.6,
    top_p: float = 0.9,
    top_k: int = 50,
    repetition_penalty: float = 1.2,
) -> Iterator[str]:
    conversation = chat_history.copy()
    conversation.append({"role": "user", "content": message})

    input_ids = tokenizer.apply_chat_template(conversation, add_generation_prompt=True, return_tensors="pt")
    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 {MAX_INPUT_TOKEN_LENGTH} tokens.")
    input_ids = input_ids.to(model.device)

    streamer = TextIteratorStreamer(tokenizer, timeout=20.0, skip_prompt=True, skip_special_tokens=True)
    generate_kwargs = dict(
        {"input_ids": input_ids},
        streamer=streamer,
        max_new_tokens=max_new_tokens,
        do_sample=True,
        top_p=top_p,
        top_k=top_k,
        temperature=temperature,
        num_beams=1,
        repetition_penalty=repetition_penalty,
    )
    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()

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


chat_interface = gr.ChatInterface(
    fn=generate,
    additional_inputs=[
        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=[
        ["Ciao! Come stai?"],
        ["Pro e contro di una relazione a lungo termine. Elenco puntato con max 3 pro e 3 contro sintetici."],
        ["Quante ore impiega un uomo per mangiare un elicottero?"],
        ["Come si apre un file JSON in Python?"],
        ["Fammi un elenco puntato dei pro e contro di vivere in Italia. Massimo 2 pro e 2 contro."],
        ["Inventa una breve storia con animali sul valore dell'amicizia."],
        ["Scrivi un articolo di 100 parole sui 'Benefici dell'open-source nella ricerca sull'intelligenza artificiale'"],
        ["Can you explain briefly to me what is the Python programming language?"],
        ["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'"],
    ],
    cache_examples=False,
    type="messages",
)

fonts = {"font":[gr.themes.GoogleFont("Source Sans Pro"), "ui-sans-serif", "system-ui", "sans-serif"],
        "font_mono": [gr.themes.GoogleFont("IBM Plex Mono"),  "ui-monospace", "Consolas", "monospace"]}

with gr.Blocks(css_paths="style.css", fill_height=True, theme=gr.themes.Soft(**fonts)) as demo:
    gr.Markdown(DESCRIPTION)
    gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button")
    chat_interface.render()

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