src/app.py

"""Template Demo for IBM Granite Hugging Face spaces."""

from collections.abc import Iterator
from datetime import datetime
from pathlib import Path
from threading import Thread

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

from themes.carbon import carbon_theme

today_date = datetime.today().strftime("%B %-d, %Y")  # noqa: DTZ002

SYS_PROMPT = f"""Knowledge Cutoff Date: April 2024.
Today's Date: {today_date}.
You are Granite, developed by IBM. You are a helpful AI assistant"""
TITLE = "IBM Granite 3.1 8b Instruct"
DESCRIPTION = """
<p>Granite 3.1 is a general purpose large language model released in the open under an Apache 2.0 license. Granite
models support a 128k context length.</p>

<p>Try one of the sample prompts below or write your own. Remember, AI models can make mistakes.
<span class="gr_docs_link">
<a href="https://www.ibm.com/granite/docs/">View Documentation</a> <i class="fa fa-external-link"></i>
</span>
</p>
"""
MAX_INPUT_TOKEN_LENGTH = 128_000
MAX_NEW_TOKENS = 1024
TEMPERATURE = 0.7
TOP_P = 0.85
TOP_K = 50
REPETITION_PENALTY = 1.05

if not torch.cuda.is_available():
    DESCRIPTION += "\nThis demo does not work on CPU."

model = AutoModelForCausalLM.from_pretrained(
    "ibm-granite/granite-3.1-8b-instruct", torch_dtype=torch.float16, device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("ibm-granite/granite-3.1-8b-instruct")
tokenizer.use_default_system_prompt = False


@spaces.GPU
def generate(message: str, chat_history: list[dict]) -> Iterator[str]:
    """Generate function for chat demo."""
    # Build messages
    conversation = []
    conversation.append({"role": "system", "content": SYS_PROMPT})
    conversation += chat_history
    conversation.append({"role": "user", "content": message})

    # Convert messages to prompt format
    input_ids = tokenizer.apply_chat_template(
        conversation,
        return_tensors="pt",
        add_generation_prompt=True,
        truncation=True,
        max_length=MAX_INPUT_TOKEN_LENGTH,
    )

    input_ids = input_ids.to(model.device)
    streamer = TextIteratorStreamer(tokenizer, timeout=30.0, 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)


css_file_path = Path(Path(__file__).parent / "app.css")
head_file_path = Path(Path(__file__).parent / "app_head.html")


with gr.Blocks(
    fill_height=True, css_paths=css_file_path, head_paths=head_file_path, theme=carbon_theme, title=TITLE
) as demo:
    gr.HTML(
        f"<img src='https://www.ibm.com/granite/docs/images/granite-pictogram.svg'/><h1>{TITLE}</h1>",
        elem_classes=["gr_title"],
    )
    gr.HTML(DESCRIPTION)
    chat_interface = gr.ChatInterface(
        fn=generate,
        examples=[
            ["Explain quantum computing"],
            ["What is OpenShift?"],
            ["Importance of low latency inference"],
            ["Boosting productivity habits"],
        ],
        cache_examples=False,
        type="messages",
    )

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