app.py

import gradio as gr
import os
import httpx
import openai
from openai import OpenAI
from openai import AsyncOpenAI

from datasets import load_dataset

dataset = load_dataset("silk-road/50-Chinese-Novel-Characters")


novel_list = []

novel2roles = {}

role2datas = {}

from tqdm import tqdm
for data in tqdm(dataset['train']):
    novel = data['book']
    role = data['role']
    if novel not in novel_list:
        novel_list.append(novel)

    if novel not in novel2roles:
        novel2roles[novel] = []

    if role not in novel2roles[novel]:
        novel2roles[novel].append(role)

    role_tuple = (novel, role)

    if role_tuple not in role2datas:
        role2datas[role_tuple] = []

    role2datas[role_tuple].append(data)


from ChatHaruhi.utils import base64_to_float_array

from tqdm import tqdm

for novel in tqdm(novel_list):
    for role in novel2roles[novel]:
        for data in role2datas[(novel, role)]:
            data["vec"] = base64_to_float_array(data["bge_zh_s15"])

def conv2story( role, conversations ):
    lines = [conv["value"] if conv["from"] == "human" else role + ": " + conv["value"] for conv in conversations]
    return "\n".join(lines)

for novel in tqdm(novel_list):
    for role in novel2roles[novel]:
        for data in role2datas[(novel, role)]:
            data["story"] = conv2story( role, data["conversations"] )


from ChatHaruhi import ChatHaruhi
from ChatHaruhi.response_openai import get_response as get_response_openai
from ChatHaruhi.response_zhipu import get_response as get_response_zhipu
from ChatHaruhi.response_qwen_base import get_response as get_response_qwen_base


get_response = get_response_zhipu

narrators = ["叙述者", "旁白","文章作者","作者","Narrator","narrator"]


def package_persona( role_name, world_name ):
    if role_name in narrators:
        return package_persona_for_narrator( role_name, world_name )

    return f"""I want you to act like {role_name} from {world_name}.
If others‘ questions are related with the novel, please try to reuse the original lines from the novel.
I want you to respond and answer like {role_name} using the tone, manner and vocabulary {role_name} would use."""

def package_persona_for_narrator( role_name, world_name ):
    return f"""I want you to act like narrator {role_name} from {world_name}.
当角色行动之后，继续交代和推进新的剧情."""

role_tuple2chatbot = {}


def initialize_chatbot( novel, role ):
    global role_tuple2chatbot
    if (novel, role) not in role_tuple2chatbot:
        persona = package_persona( role, novel )
        persona += "\n{{RAG对话}}\n{{RAG对话}}\n{{RAG对话}}\n"
        stories = [data["story"] for data in role2datas[(novel, role)] ]
        vecs = [data["vec"] for data in role2datas[(novel, role)] ]
        chatbot = ChatHaruhi( role_name = role, persona = persona , stories = stories, story_vecs= vecs,\
                             llm = get_response)
        chatbot.verbose = False

        role_tuple2chatbot[(novel, role)] = chatbot

from tqdm import tqdm
for novel in tqdm(novel_list):
    for role in novel2roles[novel]:
        initialize_chatbot( novel, role )

readme_text = """# 使用说明

选择小说角色

如果你有什么附加信息，添加到附加信息里面就可以

比如"韩立会炫耀自己刚刚学会了Python"

然后就可以开始聊天了

因为这些角色还没有增加Greeting信息，所以之后再开发个随机乱聊功能

# 开发细节

- 采用ChatHaruhi3.0的接口进行prompting
- 这里的数据是用一个7B的tuned qwen模型进行抽取的
- 想看数据可以去看第三个tab
- 抽取模型用了40k左右的GLM蒸馏数据
- 抽取模型是腾讯大哥BPSK训练的

# 总结人物性格

第三个Tab里面，可以显示一个prompt总结人物的性格

复制到openai或者GLM或者Claude进行人物总结


# 这些小说数据从HaruhiZero 0.4模型开始，被加入训练

openai太慢了 今天试试GLM的

不过当前demo是openai的

"""

# from transformers import AutoTokenizer, AutoModel, AutoModelForCausalLM
# tokenizer = AutoTokenizer.from_pretrained("silk-road/Haruhi-Zero-1_8B", trust_remote_code=True)
# model = AutoModelForCausalLM.from_pretrained("silk-road/Haruhi-Zero-1_8B", device_map="auto", trust_remote_code=True)
# model = model.eval()

# def get_response_qwen18(message):
#     from ChatHaruhi.utils import normalize2uaua
#     message_ua = normalize2uaua(message, if_replace_system = True)
#     import json
#     message_tuples = []
#     for i in range(0, len(message_ua)-1, 2):
#         message_tuple = (message_ua[i]["content"], message_ua[i+1]["content"])
#         message_tuples.append(message_tuple)
#     response, _ = model.chat(tokenizer, message_ua[-1]["content"], history=message_tuples)
#     return response

from ChatHaruhi.response_openai import get_response, async_get_response
import gradio as gr

def get_role_list( novel ):
    new_list = novel2roles[novel]
    new_value = new_list[0]
    return gr.update(choices = new_list, value = new_value)

# save_log = "/content/output.txt"

def get_chatbot( novel, role ):
    if (novel, role) not in role_tuple2chatbot:
        initialize_chatbot( novel, role )

    return role_tuple2chatbot[(novel, role)]

import json

def random_chat_callback( novel, role, chat_history):
    datas = role2datas[(novel, role)]

    reesponse_set = set()

    for chat_tuple in chat_history:
        if chat_tuple[1] is not None:
            reesponse_set.add(chat_tuple[1])

    for _ in range(5):
        random_data = random.choice(datas)
        convs = random_data["conversations"]
        n = len(convs)
        index = [x for x in range(0,n,2)]

        for i in index:
            query = convs[i]['value']
            response = convs[i+1]['value']
            if response not in reesponse_set:
                chat_history.append( (query, response) )
                return chat_history

    return chat_history



async def submit_chat( novel, role, user_name, user_text, chat_history, persona_addition_info,model_sel):

    if len(user_text) > 400:
        user_text = user_text[:400]

    if_user_in_text = True

    chatbot = get_chatbot( novel, role )
    chatbot.persona = initialize_persona( novel, role,  persona_addition_info)
    # chatbot.llm_async = async_get_response

    if model_sel == "openai":
        chatbot.llm = get_response_openai
    elif model_sel == "Zhipu":
        chatbot.llm = get_response_zhipu
    else:
        chatbot.llm = get_response_qwen_base

    history = []

    for chat_tuple in chat_history:
        if chat_tuple[0] is not None:
            history.append( {"speaker":"{{user}}","content":chat_tuple[0]} )
        if chat_tuple[1] is not None:
            history.append( {"speaker":"{{role}}","content":chat_tuple[1]} )

    chatbot.history = history

    input_text = user_text

    if if_user_in_text:
        input_text = user_name + " : " + user_text
        response = chatbot.chat(user = "", text = input_text )
        # response = await chatbot.async_chat(user = "", text = input_text )
    else:
        response = chatbot.chat(user = user_name, text = input_text)
        # response = await chatbot.async_chat(user = user_name, text = input_text)
    chat_history.append( (input_text, response) )

    print_data = {"novel":novel, "role":role, "user_text":input_text, "response":response}

    print(json.dumps(print_data, ensure_ascii=False))

    # with open(save_log, "a",encoding = "utf-8") as f:
    #     f.write(json.dumps(print_data, ensure_ascii=False) + "\n")

    return chat_history


def initialize_persona( novel, role,  persona_addition_info):
    whole_persona = package_persona( role, novel )
    whole_persona += "\n" + persona_addition_info
    whole_persona += "\n{{RAG对话}}\n{{RAG对话}}\n{{RAG对话}}\n"

    return whole_persona

def clean_history( ):
    return []

def clean_input():
    return ""

import random

def generate_summarize_prompt( novel, role_name ):
    whole_prompt = f'''
你在分析小说{novel}中的角色{role_name}
结合小说{novel}中的内容，以及下文中角色{role_name}的对话
判断{role_name}的人物设定、人物特点以及语言风格

{role_name}的对话:
'''
    stories = [data["story"] for data in role2datas[(novel, role_name)] ]

    sample_n = 5

    sample_stories = random.sample(stories, sample_n)

    for story in sample_stories:
        whole_prompt += story + "\n\n"

    return whole_prompt.strip()


with gr.Blocks() as demo:
    gr.Markdown("""# 50本小说的人物测试

    这个interface由李鲁鲁实现，主要是用来看语料的

    增加了随机聊天，支持GLM，openai切换
    
    米唯实接入了qwen1.8B并布置于huggingface上""")

    with gr.Tab("聊天"):
        with gr.Row():
            novel_sel = gr.Dropdown( novel_list, label = "小说", value = "悟空传" , interactive = True)
            role_sel = gr.Dropdown( novel2roles[novel_sel.value], label = "角色", value = "孙悟空", interactive = True )

        with gr.Row():
            chat_history = gr.Chatbot(height = 600)

        with gr.Row():
            user_name = gr.Textbox(label="user_name", scale = 1, value = "鲁鲁", interactive = True)
            user_text = gr.Textbox(label="user_text", scale = 20)
            submit = gr.Button("submit", scale = 1)

        with gr.Row():
            random_chat = gr.Button("随机聊天", scale = 1)
            clean_message = gr.Button("清空聊天", scale = 1)

        with gr.Row():
            persona_addition_info = gr.TextArea( label = "额外人物设定", value = "",  interactive = True  )

        with gr.Row():
            update_persona = gr.Button("补充人物设定到prompt", scale = 1)
            model_sel = gr.Radio(["Zhipu","openai","qwen1.8B"], interactive = True, scale = 5, value = "qwen1.8B", label = "模型选择")

        with gr.Row():
            whole_persona = gr.TextArea( label = "完整的system prompt", value = "",  interactive = False  )

        novel_sel.change(fn = get_role_list, inputs = [novel_sel], outputs = [role_sel]).then(fn = initialize_persona, inputs = [novel_sel, role_sel, persona_addition_info], outputs = [whole_persona])

        role_sel.change(fn = initialize_persona, inputs = [novel_sel, role_sel, persona_addition_info], outputs = [whole_persona])

        update_persona.click(fn = initialize_persona, inputs = [novel_sel, role_sel, persona_addition_info], outputs = [whole_persona])

        random_chat.click(fn = random_chat_callback, inputs = [novel_sel, role_sel, chat_history], outputs = [chat_history])

        user_text.submit(fn = submit_chat, inputs = [novel_sel, role_sel, user_name, user_text, chat_history, persona_addition_info,model_sel], outputs = [chat_history]).then(fn = clean_input, inputs = [], outputs = [user_text])
        submit.click(fn = submit_chat, inputs = [novel_sel, role_sel, user_name, user_text, chat_history, persona_addition_info,model_sel], outputs = [chat_history]).then(fn = clean_input, inputs = [], outputs = [user_text])

        clean_message.click(fn = clean_history, inputs = [], outputs = [chat_history])

    with gr.Tab("README"):
        gr.Markdown(readme_text)

    with gr.Tab("辅助人物总结"):
        with gr.Row():
            generate_prompt = gr.Button("生成人物总结prompt", scale = 1)

        with gr.Row():
            whole_prompt = gr.TextArea( label = "复制这个prompt到Openai或者GLM或者Claude进行总结", value = "",  interactive = False  )

        generate_prompt.click(fn = generate_summarize_prompt, inputs = [novel_sel, role_sel], outputs = [whole_prompt])





demo.launch(share=True, debug = True)