"""
This script creates an interactive web demo for the ChatGLM3-6B model using Gradio, a Python library for building quick and easy UI components for machine learning models. It's designed to showcase the capabilities of the ChatGLM3-6B model in a user-friendly interface, allowing users to interact with the model through a chat-like interface.

Usage:
- Run the script to start the Gradio web server.
- Interact with the model by typing questions and receiving responses.

Requirements:
- Gradio (required with 3.39 version, not support for 4.x), Transformers, and other necessary Python libraries should be installed.
- The model checkpoint should be accessible at the specified paths.

Note: The script includes a modification to the Chatbot's postprocess method to handle markdown to HTML conversion, ensuring that the chat interface displays formatted text correctly.
"""

import os
import mdtex2html
from transformers import AutoModel, AutoTokenizer
import gradio as gr

MODEL_PATH = os.environ.get('MODEL_PATH', 'THUDM/chatglm3-6b')
TOKENIZER_PATH = os.environ.get("TOKENIZER_PATH", MODEL_PATH)

tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_PATH, trust_remote_code=True)
model = AutoModel.from_pretrained(MODEL_PATH, trust_remote_code=True, device_map="auto").eval()

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, past_key_values):
    chatbot.append((parse_text(input), ""))
    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))

        yield chatbot, history, past_key_values


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

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


with gr.Blocks() as demo:
    gr.HTML("""<h1 align="center">ChatGLM3-6B</h1>""")

    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...", lines=10).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, 32768, value=8192, 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, 1, value=0.6, step=0.01, label="Temperature", interactive=True)

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

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

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

demo.queue().launch(share=False, server_name="127.0.0.1", server_port=8501, inbrowser=True)