web_demo.py

# Adapted from https://github.com/THUDM/ChatGLM-6B/blob/main/web_demo.py

import argparse
from pathlib import Path

import chatglm_cpp
import gradio as gr
import logging

DEFAULT_MODEL_PATH = "/app/models/chatglm2-ggml-q4_1.bin"

parser = argparse.ArgumentParser()
parser.add_argument("-m", "--model", default=DEFAULT_MODEL_PATH, type=Path, help="model path")
parser.add_argument("--mode", default="chat", type=str, choices=["chat", "generate"], help="inference mode")
parser.add_argument("-l", "--max_length", default=2048, type=int, help="max total length including prompt and output")
parser.add_argument("-c", "--max_context_length", default=512, type=int, help="max context length")
parser.add_argument("--top_k", default=0, type=int, help="top-k sampling")
parser.add_argument("--top_p", default=0.7, type=float, help="top-p sampling")
parser.add_argument("--temp", default=0.95, type=float, help="temperature")
parser.add_argument("--repeat_penalty", default=1.0, type=float, help="penalize repeat sequence of tokens")
parser.add_argument("-t", "--threads", default=0, type=int, help="number of threads for inference")
parser.add_argument("--plain", action="store_true", help="display in plain text without markdown support")
args = parser.parse_args()

pipeline = chatglm_cpp.Pipeline(args.model)


def postprocess(text):
    if args.plain:
        return f"<pre>{text}</pre>"
    return text


def predict(input, chatbot, max_length, top_p, temperature, history):
    logging.info(f"start predict:{input}")
    chatbot.append((postprocess(input), ""))
    response = ""
    history.append(input)

    generation_kwargs = dict(
        max_length=max_length,
        do_sample=temperature > 0,
        top_k=args.top_k,
        top_p=top_p,
        temperature=temperature,
        repetition_penalty=args.repeat_penalty,
        num_threads=args.threads,
        stream=True,
    )
    generator = (
        pipeline.chat(history, **generation_kwargs)
        if args.mode == "chat"
        else pipeline.generate(input, **generation_kwargs)
    )
    for response_piece in generator:
        response += response_piece
        chatbot[-1] = (chatbot[-1][0], postprocess(response))

        yield chatbot, history

    history.append(response)
    yield chatbot, history


def reset_user_input():
    logging.info("start reset_user_input")
    return gr.update(value="")


def reset_state():
    return [], []

def main():
    CSS ="""
        .contain { display: flex; flex-direction: column;}
        #component-0 #component-2 #component-3 { height:65vh !important; }
        """
    with gr.Blocks(css=CSS) as demo:
        gr.Markdown("# chatGLM2-webui")

        with gr.Group():
            chatbot = gr.Chatbot()
            user_input = gr.Textbox(show_label=False, placeholder="Input...", lines=1,max_lines=1)
        with gr.Row():
            submitBtn = gr.Button("Submit", variant="primary")
            emptyBtn = gr.Button("🗑️  Clear", variant="secondary")
        with gr.Accordion(label="Advanced options", open=False):
            max_length = gr.Slider(0, 2048, value=args.max_length, step=1.0, label="Maximum Length", interactive=True)
            top_p = gr.Slider(0, 1, value=args.top_p, step=0.01, label="Top P", interactive=True)
            temperature = gr.Slider(0, 1, value=args.temp, step=0.01, label="Temperature", interactive=True)
                

        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])

        user_input.submit(fn=predict, inputs=[user_input, chatbot, max_length, top_p, temperature, history],outputs=[chatbot, history], 
                          api_name=False, show_progress=True
                          )
        user_input.submit(
            reset_user_input, [], [user_input]            
        )

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


    demo.queue().launch(server_name="0.0.0.0")
if __name__ == "__main__":
    logging.basicConfig(level="INFO", format='%(asctime)s %(levelname)s - %(filename)s - %(lineno)s - %(message)s')
    try:
        main()
    except Exception as e:
        logging.exception(e)