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Update app.py
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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_erniebot import get_response as get_response_erniebot
from ChatHaruhi.response_spark import get_response as get_response_spark
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
elif model_sel == "spark":
chatbot.llm = get_response_spark
else:
chatbot.llm = get_response_erniebot
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Spark,文心一言并布置于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","spark","erniebot"], interactive = True, scale = 5, value = "Zhipu", 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)