app.py

import os
import time
import spaces
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer, BitsAndBytesConfig
import gradio as gr
from threading import Thread

HF_TOKEN = os.environ.get("HF_TOKEN", None)
MODEL = "AGI-0/Art-v0-3B"

TITLE = """<h2>Link to the model: <a href="https://huggingface.co/AGI-0/Art-v0-3B">click here</a></h2>"""

PLACEHOLDER = """
<center>
<p>Hi! How can I help you today?</p>
</center>
"""

CSS = """
.duplicate-button {
    margin: auto !important;
    color: white !important;
    background: black !important;
    border-radius: 100vh !important;
}
h3 {
    text-align: center;
}
"""

device = "cuda"  # for GPU usage or "cpu" for CPU usage

tokenizer = AutoTokenizer.from_pretrained(MODEL)
model = AutoModelForCausalLM.from_pretrained(
    MODEL,
    torch_dtype=torch.bfloat16,
    device_map="auto")
end_of_sentence = tokenizer.convert_tokens_to_ids("<|im_end|>")

@spaces.GPU()
def stream_chat(
    message: str, 
    history: list,
    system_prompt: str,
    temperature: float = 0.2, 
    max_new_tokens: int = 4096, 
    top_p: float = 1.0, 
    top_k: int = 1, 
    penalty: float = 1.1,
):
    print(f'message: {message}')
    print(f'history: {history}')

    conversation = []
    for prompt, answer in history:
        conversation.extend([
            {"role": "user", "content": prompt}, 
            {"role": "assistant", "content": answer},
        ])

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

    input_ids = tokenizer.apply_chat_template(conversation, add_generation_prompt=True, return_tensors="pt").to(model.device)
    
    streamer = TextIteratorStreamer(tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True)
    
    generate_kwargs = dict(
        input_ids=input_ids, 
        max_new_tokens=max_new_tokens,
        do_sample=False if temperature == 0 else True,
        top_p=top_p,
        top_k=top_k,
        temperature=temperature,
        repetition_penalty=penalty,
        eos_token_id=[end_of_sentence],
        streamer=streamer,
    )

    with torch.no_grad():
        thread = Thread(target=model.generate, kwargs=generate_kwargs)
        thread.start()
    
    buffer = ""
    found_token = False
    
    for new_text in streamer:
        buffer += new_text
        
        if "<|end_reasoning|>" in buffer and not found_token:
            # Split at the token
            parts = buffer.split("<|end_reasoning|>")
            reasoning = parts[0]
            rest = parts[1] if len(parts) > 1 else ""
            
            # Format with markdown and continue
            buffer = f"<details><summary>Click to see reasoning</summary>\n\n{reasoning}\n\n</details>\n\n{rest}"
            found_token = True
            
        yield buffer

chatbot = gr.Chatbot(height=600, placeholder=PLACEHOLDER)

with gr.Blocks(css=CSS, theme="soft") as demo:
    gr.HTML(TITLE)
    gr.DuplicateButton(value="Duplicate Space for private use", elem_classes="duplicate-button")
    gr.ChatInterface(
        fn=stream_chat,
        chatbot=chatbot,
        fill_height=True,
        additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False),
        additional_inputs=[
            gr.Textbox(
                value="",
                label="",
                render=False,
            ),
            gr.Slider(
                minimum=0,
                maximum=1,
                step=0.1,
                value=0.2,
                label="Temperature",
                render=False,
            ),
            gr.Slider(
                minimum=128,
                maximum=8192,
                step=1,
                value=4096,
                label="Max new tokens",
                render=False,
            ),
            gr.Slider(
                minimum=0.0,
                maximum=1.0,
                step=0.1,
                value=1.0,
                label="top_p",
                render=False,
            ),
            gr.Slider(
                minimum=1,
                maximum=50,
                step=1,
                value=1,
                label="top_k",
                render=False,
            ),
            gr.Slider(
                minimum=0.0,
                maximum=2.0,
                step=0.1,
                value=1.1,
                label="Repetition penalty",
                render=False,
            ),
        ],
        examples=[
            ["Help me study vocabulary: write a sentence for me to fill in the blank, and I'll try to pick the correct option."],
            ["What are 5 creative things I could do with my kids' art? I don't want to throw them away, but it's also so much clutter."],
            ["Tell me a random fun fact about the Roman Empire."],
            ["Show me a code snippet of a website's sticky header in CSS and JavaScript."],
        ],
        cache_examples=False,
    )

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