app.py

# import gradio as gr

# model_name = "models/THUDM/chatglm2-6b-int4"
# gr.load(model_name).lauch()

# %%writefile demo-4bit.py

from textwrap import dedent

# credit to https://github.com/THUDM/ChatGLM2-6B/blob/main/web_demo.py
# while mistakes are mine
from transformers import AutoModel, AutoTokenizer
import gradio as gr
import mdtex2html

from loguru import logger

model_name = "THUDM/chatglm2-6b"
model_name = "THUDM/chatglm2-6b-int4"

tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)

# model = AutoModel.from_pretrained(model_name, trust_remote_code=True).cuda()

# 4/8 bit
# model = AutoModel.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True).quantize(4).cuda()

import torch

has_cuda = torch.cuda.is_available()
# has_cuda = False  # force cpu

if has_cuda:
    model = AutoModel.from_pretrained(model_name, trust_remote_code=True).cuda()  # 3.92G
else:
    model = AutoModel.from_pretrained(model_name, trust_remote_code=True).half()  # .float() .half().float()

model = model.eval()

_ = """Override Chatbot.postprocess"""

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(RETRY_FLAG, input, chatbot, max_length, top_p, temperature, history, past_key_values):
    print(f"Inside predict, user_input1 is - {input}") 
    chatbot.append((parse_text(input), ""))
    print(f"Inside predict, user_input2 is - {input}") 
    print(f"Inside predict, chatbot is - {chatbot}")
    print(f"Inside predict, history is - {history}")
    #if RETRY_FLAG:
    #    history.append()
        
    for response, history, past_key_values in model.stream_chat(tokenizer, input, history, past_key_values=past_key_values,
                                                                return_past_key_values=True,
                                                                max_length=max_length, top_p=top_p,
                                                                temperature=temperature):
        chatbot[-1] = (parse_text(input), parse_text(response))
        #print(f"Inside predict, chatbot2 is - {chatbot}")
        #print(f"Inside predict, history2 is - {history}")

        yield chatbot, history, past_key_values


def trans_api(input, max_length=4096, top_p=0.8, temperature=0.2):
    if max_length < 100:
        max_length = 4096
    if top_p < 0.1:
        top_p = 0.8
    if temperature <= 0:
        temperature = 0.01
    try:
        res, _ = model.chat(
            tokenizer, 
            input, 
            history=[], 
            past_key_values=None,
            max_length=max_length,
            top_p=top_p,
            temperature=temperature,
        )
        # logger.debug(f"{res=} \n{_=}")
    except Exception as exc:
        logger.error(f"{exc=}")
        res = str(exc)

    return res
        

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


def reset_state():
    return [], [], None


# Delete last turn
def delete_last_turn(chat, history):
    if chat and history:
        chat.pop(-1)
        history.pop(-1)
        #history.pop(-1)
    return chat, history


# Regenerate response
def retry_last_answer(
    user_input, 
    chatbot, 
    max_length, 
    top_p, 
    temperature, 
    history, 
    past_key_values
      ):
    print(f"chatbot is - {chatbot}") 
    print(f"history is - {history}") 
    print(f"user_input is - {user_input}") 
    print(f"max_length is - {max_length}") 
    print(f"top_p is - {top_p}") 
    print(f"temperature is - {temperature}") 
    print(f"past_key_values type is - {type(past_key_values)}") 
          
    if chatbot and history:
        # Removing the previous conversation from chat 
        chatbot.pop(-1)
        #chatbot[-1] = (chatbot[-1][0],)
        #history[-1] = (history[-1][0],)
        # Setting up a flag to capture a retry 
        RETRY_FLAG = True
        # Getting last message from user
        user_input = history[-1][0]
        # Removing bot response from the history 
        history.pop(-1)

        print(f"popped chatbot is - {chatbot}") 
        print(f"popped history is - {history}") 
        #print(f"user_input is - {user_input}") 

    yield from predict(
        RETRY_FLAG,
        user_input, 
        chatbot, 
        max_length, 
        top_p, 
        temperature, 
        history, 
        past_key_values
        )
          

with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.HTML("""<h1 align="center">ChatGLM2-6B-int4</h1>""")
    gr.HTML("""<center><a href="https://huggingface.co/spaces/ysharma/chatglm2-6b-4bit?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>To avoid the queue and for faster inference Duplicate this Space and upgrade to GPU</center>""")

    with gr.Accordion("Info", open=False):
        _ = """
            A query takes from 30 seconds to a few tens of seconds, dependent on the number of words/characters 
            the question and answer contain.

            * Low temperature: responses will be more deterministic and focused; High temperature: responses more creative.
            
            * Suggested temperatures -- translation: up to 0.3; chatting: > 0.4

            * Top P controls dynamic vocabulary selection based on context. 

            For a table of example values for different scenarios, refer to [this](https://community.openai.com/t/cheat-sheet-mastering-temperature-and-top-p-in-chatgpt-api-a-few-tips-and-tricks-on-controlling-the-creativity-deterministic-output-of-prompt-responses/172683)

            If the instance is not on a GPU (T4), it will be very slow. You can try to run the colab notebook [chatglm2-6b-4bit colab notebook](https://colab.research.google.com/drive/1WkF7kOjVCcBBatDHjaGkuJHnPdMWNtbW?usp=sharing) for a spin.

            The T4 GPU is sponsored by a community GPU grant from Huggingface. Thanks a lot!
            """
        gr.Markdown(dedent(_))
    chatbot = gr.Chatbot()
    with gr.Row():
        with gr.Column(scale=4):
            with gr.Column(scale=12):
                user_input = gr.Textbox(show_label=False, placeholder="Input...",).style(
                    container=False)
                RETRY_FLAG = gr.Checkbox(value=False, visible=False)
            with gr.Column(min_width=32, scale=1):
                with gr.Row():
                    submitBtn = gr.Button("Submit", variant="primary")
                    deleteBtn = gr.Button("Delete last turn", variant="secondary")
                    retryBtn = gr.Button("Regenerate", variant="secondary")
        with gr.Column(scale=1):
            emptyBtn = gr.Button("Clear History")
            max_length = gr.Slider(0, 32768, value=8192/2, step=1.0, label="Maximum length", interactive=True)
            top_p = gr.Slider(0, 1, value=0.8, step=0.01, label="Top P", interactive=True)
            temperature = gr.Slider(0.01, 1, value=0.95, step=0.01, label="Temperature", interactive=True)

    history = gr.State([])
    past_key_values = gr.State(None)

    user_input.submit(predict, [RETRY_FLAG, user_input, chatbot, max_length, top_p, temperature, history, past_key_values],
                    [chatbot, history, past_key_values], show_progress=True)
    submitBtn.click(predict, [RETRY_FLAG, user_input, chatbot, max_length, top_p, temperature, history, past_key_values],
                    [chatbot, history, past_key_values], show_progress=True, api_name="predict")
    submitBtn.click(reset_user_input, [], [user_input])

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

    retryBtn.click(
        retry_last_answer, 
        inputs = [user_input, chatbot, max_length, top_p, temperature, history, past_key_values],
        #outputs = [chatbot, history, last_user_message, user_message]
        outputs=[chatbot, history, past_key_values]
        )
    deleteBtn.click(delete_last_turn, [chatbot, history], [chatbot, history])
    
    with gr.Accordion("For Translation API", open=False):
        input_text = gr.Text()
        tr_btn = gr.Button("Go", variant="primary")
        out_text = gr.Text()
    tr_btn.click(trans_api, [input_text, max_length, top_p, temperature], out_text, show_progress=True, api_name="tr")       
    input_text.submit(trans_api, [input_text, max_length, top_p, temperature], out_text, show_progress=True, api_name="tr")      

    with gr.Accordion("Example inputs", open=True):
        examples = gr.Examples(
            examples=[["Explain the plot of Cinderella in a sentence."],
                     ["How long does it take to become proficient in French, and what are the best methods for retaining information?"],
                     ["What are some common mistakes to avoid when writing code?"],
                     ["Build a prompt to generate a beautiful portrait of a horse"],
                     ["Suggest four metaphors to describe the benefits of AI"],
                     ["Write a pop song about leaving home for the sandy beaches."],
                     ["Write a summary demonstrating my ability to do beat-boxing"]],
            inputs = [user_input],
            
        )
        
# demo.queue().launch(share=False, inbrowser=True)
# demo.queue().launch(share=True, inbrowser=True, debug=True)

demo.queue().launch(debug=True)