app.py

import os
from threading import Thread
from typing import Iterator, List, Tuple

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

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

DESCRIPTION = """\
# DeepCode-6.7B-Chat
This Space demonstrates model [DeepCode-AI](https://huggingface.co/deepcode-ai/deepcode-ai-6.7b-instruct) 
by DeepCode, a code model with 6.7B parameters fine-tuned for chat instructions.
"""

if not torch.cuda.is_available():
    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"
    model = None
else:
    model_id = "deepcode-ai/deepcode-ai-6.7b-instruct"
    model = AutoModelForCausalLM.from_pretrained(
        model_id, torch_dtype=torch.bfloat16, device_map="auto"
    )
    tokenizer = AutoTokenizer.from_pretrained(model_id)
    tokenizer.use_default_system_prompt = False


def trim_input_ids(input_ids: torch.Tensor) -> torch.Tensor:
    """
    Trim input_ids to fit within the MAX_INPUT_TOKEN_LENGTH.
    """
    if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH:
        input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:]
        gr.Warning(f"Trimmed input as it exceeded {MAX_INPUT_TOKEN_LENGTH} tokens.")
    return input_ids


def build_conversation(message: str, chat_history: List[Tuple[str, str]], system_prompt: str) -> List[dict]:
    """
    Build the conversation structure for the chat model.
    """
    conversation = []
    if system_prompt:
        conversation.append({"role": "system", "content": system_prompt})
    for user, assistant in chat_history:
        conversation.extend([
            {"role": "user", "content": user},
            {"role": "assistant", "content": assistant}
        ])
    conversation.append({"role": "user", "content": message})
    return conversation


def generate(
    message: str,
    chat_history: List[Tuple[str, str]],
    system_prompt: 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 = 1.0,
) -> Iterator[str]:
    if model is None:
        yield "GPU is unavailable. This demo does not run on CPU."
        return

    conversation = build_conversation(message, chat_history, system_prompt)
    input_ids = tokenizer.apply_chat_template(
        conversation, return_tensors="pt", add_generation_prompt=True
    )
    input_ids = trim_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=max_new_tokens,
        do_sample=False,
        num_beams=1,
        repetition_penalty=repetition_penalty,
        eos_token_id=tokenizer.eos_token_id,
    )
    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()

    outputs = []
    try:
        for text in streamer:
            outputs.append(text)
            yield "".join(outputs).replace("<|EOT|>", "")
    except Exception as e:
        yield f"Error during generation: {e}"


# Gradio Interface
chat_interface = gr.ChatInterface(
    fn=generate,
    additional_inputs=[
        gr.Textbox(label="System prompt", lines=6),
        gr.Slider(
            label="Max new tokens",
            minimum=1,
            maximum=MAX_MAX_NEW_TOKENS,
            step=1,
            value=DEFAULT_MAX_NEW_TOKENS,
        ),
        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=1.0,
        ),
    ],
    examples=[
        ["Implement snake game using pygame"],
        ["Can you explain what the Python programming language is?"],
        ["Write a program to find the factorial of a number"],
    ],
)

with gr.Blocks(css="style.css") as demo:
    gr.Markdown(DESCRIPTION)
    chat_interface.render()

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