app.py

#!/usr/bin/env python

import os
import re
from collections.abc import Iterator
from threading import Thread

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

DESCRIPTION = "# ICONN Lite Chat"

if not torch.cuda.is_available():
    DESCRIPTION += "\n<p class='warning'>Running on CPU 🥶 This demo does not work on CPU.</p>"

top_k: int = 50
MAX_MAX_NEW_TOKENS = 100000000
DEFAULT_MAX_NEW_TOKENS = 10240
MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))

if torch.cuda.is_available():
    model_id = "ICONNAI/ICONN-1-Mini-Beta"
    model = AutoModelForCausalLM.from_pretrained(
        model_id, torch_dtype=torch.float16, device_map="auto", trust_remote_code=True
    )
    tokenizer = AutoTokenizer.from_pretrained(model_id)


def wrap_thinking_blocks(text: str) -> str:
    def replacer(match):
        content = match.group(1).strip()
        return (
            "<details class='thinking-block'>"
            "<summary>💭 Thinking...</summary>"
            f"<div class='thinking-content'><pre>{content}</pre></div>"
            "</details>"
        )
    return re.sub(r"<think>\s*(.*?)\s*</think>", replacer, text, flags=re.DOTALL)


@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, {"role": "user", "content": message}]

    input_ids = tokenizer.apply_chat_template(
        conversation, return_tensors="pt", enable_thinking=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 {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:
        wrapped = wrap_thinking_blocks("".join(outputs + [text]))
        yield wrapped
        outputs.append(text)


demo = gr.ChatInterface(
    fn=generate,
    additional_inputs=[
        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=[
        ["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'"],
    ],
    type="messages",
    description=DESCRIPTION,
    css_paths="style.css",
)

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