app.py

from transformers import AutoModelForCausalLM, AutoTokenizer
import gradio as gr
import mdtex2html
import torch

"""Override Chatbot.postprocess"""


model_path = 'THUDM/BPO'

device = 'cuda:0'

if torch.cuda.is_available():
    tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True, add_prefix_space=True)
    model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True, device_map=device, load_in_8bit=True)
    model = model.eval()


DESCRIPTION = """This Space demonstrates model [BPO](https://huggingface.co/THUDM/BPO), which is built on LLaMA-2-7b-chat.
BPO aims to improve the alignment of LLMs with human preferences by optimizing user prompts.

Feel free to play with it, or duplicate to run generations without a queue! 🔎 For more details about the BPO model, take a look [at our paper](https://arxiv.org/pdf/2311.04155.pdf).
"""

LICENSE = """
---
As BPO is a fine-tuned version of [Llama-2-7b-chat](https://huggingface.co/meta-llama/Llama-2-7b-chat) by Meta,
this demo is governed by the original [license](https://huggingface.co/spaces/CCCCCC/BPO_demo/blob/main/LICENSE.txt) and [acceptable use policy](https://huggingface.co/spaces/CCCCCC/BPO_demo/blob/main/USE_POLICY.md).
"""

if not torch.cuda.is_available():
    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"

prompt_template = "[INST] You are an expert prompt engineer. Please help me improve this prompt to get a more helpful and harmless response:\n{} [/INST]"


def postprocess(self, y):
    if y is None:
        return []
    for i, (message, response) in enumerate(y):
        y[i] = (
            None if message is None else mdtex2html.convert((message)),
            None if response is None else mdtex2html.convert(response),
        )
    return y


gr.Chatbot.postprocess = postprocess


def parse_text(text):
    """copy from https://github.com/GaiZhenbiao/ChuanhuChatGPT/"""
    lines = text.split("\n")
    lines = [line for line in lines if line != ""]
    count = 0
    for i, line in enumerate(lines):
        if "```" in line:
            count += 1
            items = line.split('`')
            if count % 2 == 1:
                lines[i] = f'<pre><code class="language-{items[-1]}">'
            else:
                lines[i] = f'<br></code></pre>'
        else:
            if i > 0:
                if count % 2 == 1:
                    line = line.replace("`", "\`")
                    line = line.replace("<", "&lt;")
                    line = line.replace(">", "&gt;")
                    line = line.replace(" ", "&nbsp;")
                    line = line.replace("*", "&ast;")
                    line = line.replace("_", "&lowbar;")
                    line = line.replace("-", "&#45;")
                    line = line.replace(".", "&#46;")
                    line = line.replace("!", "&#33;")
                    line = line.replace("(", "&#40;")
                    line = line.replace(")", "&#41;")
                    line = line.replace("$", "&#36;")
                lines[i] = "<br>"+line
    text = "".join(lines)
    return text


def predict(input, chatbot, max_length, top_p, temperature, history):

    if input.strip() == "":
        chatbot = [(parse_text(input), parse_text("Please input a valid user prompt. Empty string is not supported."))]
        return chatbot, history

    prompt = prompt_template.format(input)
    model_inputs = tokenizer(prompt, return_tensors="pt").to(device)
    output = model.generate(**model_inputs, max_length=max_length, do_sample=True, top_p=top_p, 
                            temperature=temperature, num_beams=1)
    resp = tokenizer.decode(output[0], skip_special_tokens=True).split('[/INST]')[1].strip()

    optimized_prompt = """Here are several optimized prompts:

====================Stable Optimization====================
"""
    optimized_prompt += resp
    chatbot = [(parse_text(input), parse_text(optimized_prompt))]
    yield chatbot, history

    optimized_prompt += "\n\n====================Aggressive Optimization===================="

    texts = [input] * 5  
    responses = []
    num = 0
    for text in texts:
        num += 1
        seed = torch.seed()
        torch.manual_seed(seed)
        prompt = prompt_template.format(text)
        min_length = len(tokenizer(prompt)['input_ids']) + len(tokenizer(text)['input_ids']) + 5
        model_inputs = tokenizer(prompt, return_tensors="pt").to(device)
        bad_words_ids = [tokenizer(bad_word, add_special_tokens=False).input_ids for bad_word in ["[PROTECT]", "\n\n[PROTECT]", "[KEEP", "[INSTRUCTION]"]]
        # eos and \n
        eos_token_ids = [tokenizer.eos_token_id, 13]
        output = model.generate(**model_inputs, max_new_tokens=1024, do_sample=True, top_p=0.9, temperature=0.9, bad_words_ids=bad_words_ids, num_beams=1, eos_token_id=eos_token_ids, min_length=min_length)
        resp = tokenizer.decode(output[0], skip_special_tokens=True).split('[/INST]')[1].split('[KE')[0].split('[INS')[0].split('[PRO')[0].strip()
        
        optimized_prompt += f"\n{num}. {resp}"

        chatbot = [(parse_text(input), parse_text(optimized_prompt))]
        yield chatbot, history
    # return chatbot, history


def reset_user_input():
    return gr.update(value='')


def reset_state():
    return [], []

def update_textbox_from_dropdown(selected_example):
    return selected_example

with gr.Blocks(css="sty.css") as demo:
    gr.HTML("""<h1 align="center">Prompt Preference Optimizer</h1>""")

    gr.Markdown(DESCRIPTION)

    gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button")
    
    chatbot = gr.Chatbot(label="Prompt Optimization Chatbot")
    with gr.Row():
        with gr.Column(scale=4):
            with gr.Column(scale=12):
                dropdown = gr.Dropdown(["tell me about harry potter", "give me 3 tips to learn English", "write a story about love"], label="Choose an example input")
                user_input = gr.Textbox(show_label=False, placeholder="User Prompt...", lines=5).style(
                    container=False)
            with gr.Column(min_width=32, scale=1):
                submitBtn = gr.Button("Submit", variant="primary")
        with gr.Column(scale=1):
            emptyBtn = gr.Button("Clear History")
            max_length = gr.Slider(0, 4096, value=2048, step=1.0, label="Maximum length", interactive=True)
            top_p = gr.Slider(0, 1, value=0.9, step=0.01, label="Top P", interactive=True)
            temperature = gr.Slider(0, 1, value=0.6, step=0.01, label="Temperature", interactive=True)

    gr.Markdown(LICENSE)
    
    dropdown.change(update_textbox_from_dropdown, dropdown, user_input)
    
    history = gr.State([])

    submitBtn.click(predict, [user_input, chatbot, max_length, top_p, temperature, history], [chatbot, history],
                    show_progress=True)
    submitBtn.click(reset_user_input, [], [user_input])

    emptyBtn.click(reset_state, outputs=[chatbot, history], show_progress=True)

demo.queue().launch(share=False, inbrowser=True)