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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","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)

requirements.txt

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+datasets
+tiktoken
+tqdm
+openai
+zhipuai
+gradio
+wget
+erniebot
+erniebot-agent[all]
+
+scipy
+transformers
+accelerate
+peft
+bitsandbytes
+sentencepiece
+einops 
+transformers_stream_generator==0.0.4 
+deepspeed
+auto-gptq
+optimum