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import os |
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import time |
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import spaces |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer |
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import gradio as gr |
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from threading import Thread |
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MODEL = "jwang2373/UW-SBEL-ChronoGemma-27b-it" |
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TITLE = "<h1><center>UW-SBEL-ChronoGemma-27b</center></h1>" |
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PLACEHOLDER = """ |
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<center> |
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<p>Hi! I'm a PyChrono Digital Twin expert. How can I assist you today?</p> |
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</center> |
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""" |
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CSS = """ |
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.duplicate-button { |
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margin: auto !important; |
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color: white !important; |
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background: black !important; |
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border-radius: 100vh !important; |
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} |
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h3 { |
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text-align: center; |
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} |
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""" |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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tokenizer = AutoTokenizer.from_pretrained(MODEL, trust_remote_code=True) |
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model = AutoModelForCausalLM.from_pretrained(MODEL, torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto") |
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model = model.eval() |
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@spaces.GPU() |
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def stream_chat( |
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message: str, |
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history: list, |
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system_prompt: str, |
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temperature: float = 0.5, |
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max_new_tokens: int = 32768, |
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top_p: float = 1.0, |
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top_k: int = 50, |
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): |
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print(f'message: {message}') |
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print(f'history: {history}') |
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full_prompt = f"<<SYS>>\n{system_prompt}\n<</SYS>>\n\n" |
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for prompt, answer in history: |
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full_prompt += f"[INST]{prompt}[/INST]{answer}" |
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full_prompt += f"[INST]{message}[/INST]" |
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inputs = tokenizer(full_prompt, truncation=False, return_tensors="pt").to(device) |
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context_length = inputs.input_ids.shape[-1] |
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streamer = TextIteratorStreamer(tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True) |
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generate_kwargs = dict( |
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inputs=inputs.input_ids, |
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max_new_tokens=max_new_tokens, |
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do_sample=True, |
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top_p=top_p, |
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top_k=top_k, |
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temperature=temperature, |
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num_beams=1, |
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streamer=streamer, |
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) |
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thread = Thread(target=model.generate, kwargs=generate_kwargs) |
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thread.start() |
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buffer = "" |
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for new_text in streamer: |
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buffer += new_text |
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yield buffer |
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chatbot = gr.Chatbot(height=600, placeholder=PLACEHOLDER) |
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with gr.Blocks(css=CSS, theme="soft") as demo: |
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gr.HTML(TITLE) |
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gr.DuplicateButton(value="Duplicate Space for private use", elem_classes="duplicate-button") |
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gr.ChatInterface( |
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fn=stream_chat, |
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chatbot=chatbot, |
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fill_height=True, |
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additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False), |
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additional_inputs=[ |
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gr.Textbox( |
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value="You are a PyChrono expert.", |
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label="System Prompt", |
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render=False, |
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), |
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gr.Slider( |
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minimum=0, |
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maximum=1, |
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step=0.1, |
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value=0.5, |
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label="Temperature", |
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render=False, |
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), |
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gr.Slider( |
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minimum=1024, |
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maximum=4096, |
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step=1024, |
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value=4096, |
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label="Max new tokens", |
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render=False, |
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), |
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gr.Slider( |
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minimum=0.0, |
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maximum=1.0, |
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step=0.1, |
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value=1.0, |
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label="Top p", |
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render=False, |
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), |
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gr.Slider( |
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minimum=1, |
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maximum=100, |
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step=1, |
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value=100, |
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label="Top k", |
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render=False, |
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), |
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], |
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examples=[ |
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["Run a PyChrono simulation of a sedan driving on a flat surface with a detailed vehicle dynamics model."], |
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["Run a real-time simulation of an HMMWV vehicle on a bumpy and textured road."], |
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["Set up a Curiosity rover driving simulation on flat, rigid ground in PyChrono."], |
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["Simulate a FEDA vehicle driving on rigid terrain in PyChrono."], |
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], |
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cache_examples=False, |
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) |
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if __name__ == "__main__": |
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demo.launch() |
