app.py

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

HF_TOKEN = os.environ.get("HF_TOKEN", None)
MODEL = "evabyte/EvaByte-SFT"
MODEL_BASE = "evabyte/EvaByte"

TITLE = "<h1><center>EvaByte</center></h1>"

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, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    MODEL,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True).eval().to(device)

@spaces.GPU()
def stream_chat(
    message: str, 
    history: list,
    system_prompt: str,
    temperature: float = 0.8, 
    max_new_tokens: int = 512, 
    top_p: float = 1.0, 
):
    print(f'message: {message}')
    print(f'history: {history}')

    conversation = [
        {"role": "system", "content": system_prompt}
    ]
    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(device)
        
    gen_out = model.multi_byte_generate(
        input_ids=input_ids, 
        max_new_tokens = max_new_tokens,
        do_sample = False if temperature == 0 else True,
        top_p = top_p,
        temperature = temperature,
    )

    response = tokenizer.decode(
        gen_out[0][input_ids.shape[1]:], 
        skip_special_tokens=False,
        clean_up_tokenization_spaces=False
    )

    for i in range(len(response)):
        time.sleep(0.02)
        yield response[: i + 1]
                
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="You are a helpful assistant.",
                label="System Prompt",
                lines=5,
                render=False,
            ),
            gr.Slider(
                minimum=0,
                maximum=1,
                step=0.1,
                value=0.8,
                label="Temperature",
                render=False,
            ),
            gr.Slider(
                minimum=128,
                maximum=8192,
                step=1,
                value= 512,
                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,
            ),
        ],
        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()