app.py

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

MODEL = "jwang2373/UW-SBEL-ChronoPhi-4b-it"

TITLE = "<h1><center>UW-SBEL-ChronoPhi-4b</center></h1>"

PLACEHOLDER = """
<center>
<p>Hi! I'm a PyChrono Digital Twin expert. How can I assist 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" if torch.cuda.is_available() else "cpu"

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

@spaces.GPU()
def stream_chat(
    message: str,
    history: list,
    system_prompt: str,
    temperature: float = 0.1,
    max_new_tokens: int = 32768,
    top_p: float = 1.0,
    top_k: int = 50,
):
    print(f'message: {message}')
    print(f'history: {history}')

    full_prompt = f"<<SYS>>\n{system_prompt}\n<</SYS>>\n\n"
    for prompt, answer in history:
        full_prompt += f"[INST]{prompt}[/INST]{answer}"
    full_prompt += f"[INST]{message}[/INST]"

    inputs = tokenizer(full_prompt, truncation=False, return_tensors="pt").to(device)
    context_length = inputs.input_ids.shape[-1]

    streamer = TextIteratorStreamer(tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True)

    generate_kwargs = dict(
        inputs=inputs.input_ids,
        max_new_tokens=max_new_tokens,
        do_sample=True,
        top_p=top_p,
        top_k=top_k,
        temperature=temperature,
        num_beams=1,
        streamer=streamer,
    )

    thread = Thread(target=model.generate, kwargs=generate_kwargs)
    thread.start()

    buffer = ""
    for new_text in streamer:
        buffer += new_text
        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="You are a PyChrono expert.",
                label="System Prompt",
                render=False,
            ),
            gr.Slider(
                minimum=0,
                maximum=1,
                step=0.1,
                value=0.5,
                label="Temperature",
                render=False,
            ),
            gr.Slider(
                minimum=1024,
                maximum=4096,
                step=1024,
                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=100,
                step=1,
                value=100,
                label="Top k",
                render=False,
            ),
        ],
        examples=[
            ["Run a PyChrono simulation of a sedan driving on a flat surface with a detailed vehicle dynamics model."],
            ["Run a real-time simulation of an HMMWV vehicle on a bumpy and textured road."],
            ["Set up a Curiosity rover driving simulation on flat, rigid ground in PyChrono."],
            ["Simulate a FEDA vehicle driving on rigid terrain in PyChrono."],
        ],
        cache_examples=False,
    )

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