# coding=utf-8
# Copyright 2023 WisdomShell Inc. All Rights Reserved.

# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# This code is based on Qwen's web Demo. It has been modified from
# its original forms to accommodate CodeShell.

# Copyright (c) Alibaba Cloud.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

"""A simple web interactive chat demo based on gradio."""
import os
from argparse import ArgumentParser

import gradio as gr
import mdtex2html

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig


DEFAULT_CKPT_PATH = 'WisdomShell/CodeShell-7B-Chat-int4'


def _load_model_tokenizer(args):
    tokenizer = AutoTokenizer.from_pretrained(
        'WisdomShell/CodeShell-7B-Chat-int4', trust_remote_code=True, resume_download=True,
    )

    model = AutoModelForCausalLM.from_pretrained(
        'WisdomShell/CodeShell-7B-Chat-int4',
        trust_remote_code=True,
        resume_download=True,
        torch_dtype=torch.bfloat16
    ).eval()

    config = GenerationConfig.from_pretrained(
        'WisdomShell/CodeShell-7B-Chat-int4', trust_remote_code=True, resume_download=True,
    )

    return model, tokenizer, config


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):
    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("`", r"\`")
                    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 _gc():
    import gc
    gc.collect()
    if torch.cuda.is_available():
        torch.cuda.empty_cache()


def _launch_demo(args, model, tokenizer, config):

    def predict(_query, _chatbot, _task_history):
        print(f"User: {_parse_text(_query)}")
        _chatbot.append((_parse_text(_query), ""))
        full_response = ""

        for response in model.chat(_query, _task_history, tokenizer, generation_config=config, stream=True):
            response = response.replace('|end|', '')
            response = response.replace('|<end>|', '')
            _chatbot[-1] = (_parse_text(_query), _parse_text(response))

            yield _chatbot
            full_response = _parse_text(response)

        print(f"History: {_task_history}")
        _task_history.append((_query, full_response))
        print(f"CodeShell-Chat: {_parse_text(full_response)}")

    def regenerate(_chatbot, _task_history):
        if not _task_history:
            yield _chatbot
            return
        item = _task_history.pop(-1)
        _chatbot.pop(-1)
        yield from predict(item[0], _chatbot, _task_history)

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

    def reset_state(_chatbot, _task_history):
        _task_history.clear()
        _chatbot.clear()
        _gc()
        return _chatbot

    with gr.Blocks() as demo:
        gr.Markdown("""<center><font size=8>CodeShell-Chat Bot</center>""")
        
        chatbot = gr.Chatbot(label='CodeShell-Chat', elem_classes="control-height")
        query = gr.Textbox(lines=2, label='Input')
        task_history = gr.State([])

        with gr.Row():
            empty_btn = gr.Button("🧹 Clear History (清除历史)")
            submit_btn = gr.Button("🚀 Submit (发送)")
            regen_btn = gr.Button("🤔️ Regenerate (重试)")

        submit_btn.click(predict, [query, chatbot, task_history], [chatbot], show_progress=True)
        submit_btn.click(reset_user_input, [], [query])
        empty_btn.click(reset_state, [chatbot, task_history], outputs=[chatbot], show_progress=True)
        regen_btn.click(regenerate, [chatbot, task_history], [chatbot], show_progress=True)

        gr.Markdown("""\
<font size=2>Note: This demo is governed by the original license of CodeShell. \
We strongly advise users not to knowingly generate or allow others to knowingly generate harmful content, \
including hate speech, violence, pornography, deception, etc. \
(注：本演示受CodeShell的许可协议限制。我们强烈建议，用户不应传播及不应允许他人传播以下内容，\
包括但不限于仇恨言论、暴力、色情、欺诈相关的有害信息。)""")

    demo.queue().launch()


args = {}

print("Loading model...")

model, tokenizer, config = _load_model_tokenizer(args)

print("Model loaded, launching demo...")

_launch_demo(args, model, tokenizer, config)