# coding=utf-8
# Implements user interface in browser for LLaMA fine-tuned with PEFT.
# Usage: python web_demo.py --checkpoint_dir path_to_checkpoint


import torch
import mdtex2html
import gradio as gr

from utils import ModelArguments, auto_configure_device_map, load_pretrained
from transformers import HfArgumentParser


parser = HfArgumentParser(ModelArguments)
model_args, = parser.parse_args_into_dataclasses()
model, tokenizer = load_pretrained(model_args)
if torch.cuda.device_count() > 1:
    from accelerate import dispatch_model
    device_map = auto_configure_device_map(torch.cuda.device_count())
    model = dispatch_model(model, device_map)
else:
    model = model.cuda()
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(input, chatbot, max_length, top_p, temperature, history):
    chatbot.append((parse_text(input), ""))

    inputs = tokenizer([input], return_tensors="pt")
    inputs = inputs.to(model.device)
    gen_kwargs = {
        "do_sample": True,
        "top_p": top_p,
        "temperature": temperature,
        "num_beams": 1,
        "max_length": max_length,
        "repetition_penalty": 1.0
    }
    with torch.no_grad():
        generation_output = model.generate(**inputs, **gen_kwargs)
    outputs = generation_output.tolist()[0][len(inputs["input_ids"][0]):]
    response = tokenizer.decode(outputs, skip_special_tokens=True)
    history = history + [(input, response)]
    chatbot[-1] = (parse_text(input), parse_text(response))
    yield chatbot, history


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


def reset_state():
    return [], []


with gr.Blocks() as demo:
    gr.HTML("""<h1 align="center">ChatGLM-Efficient-Tuning</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, 4096, value=2048, step=1.0, label="Maximum length", interactive=True)
            top_p = gr.Slider(0, 1, value=0.7, step=0.01, label="Top P", interactive=True)
            temperature = gr.Slider(0, 1, value=0.95, 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])

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

demo.queue().launch(server_name="0.0.0.0", share=False, inbrowser=True)