109 lines
3.8 KiB
Python
109 lines
3.8 KiB
Python
import streamlit as st
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import torch
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from transformers import AutoModel, AutoTokenizer
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from api import get_docs
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from memory import MyConversationBufferWindowMemory
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# 设置页面标题、图标和布局
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st.set_page_config(
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page_title="ChatGLM3-6B 演示",
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page_icon=":robot:",
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layout="wide"
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)
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# 设置为模型ID或本地文件夹路径
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model_path = "../THUDM/chatglm3-6b"
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@st.cache_resource
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def get_model():
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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model = AutoModel.from_pretrained(model_path, trust_remote_code=True).cuda()
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# 多显卡支持,使用下面两行代替上面一行,将num_gpus改为你实际的显卡数量
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# from utils import load_model_on_gpus
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# model = load_model_on_gpus("THUDM/chatglm3-6b", num_gpus=2)
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model = model.eval()
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return tokenizer, model
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# 加载Chatglm3的model和tokenizer
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tokenizer, model = get_model()
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# 初始化历史记录和past key values
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if "history" not in st.session_state:
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st.session_state.history = []
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if "past_key_values" not in st.session_state:
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st.session_state.past_key_values = None
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# 设置max_length、top_p和temperature
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max_length = st.sidebar.slider("max_length", 0, 32768, 8192, step=1)
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top_p = st.sidebar.slider("top_p", 0.0, 1.0, 0.8, step=0.01)
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temperature = st.sidebar.slider("temperature", 0.0, 1.0, 0.6, step=0.01)
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# 清理会话历史
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buttonClean = st.sidebar.button("清理会话历史", key="clean")
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if buttonClean:
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st.session_state.history = []
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st.session_state.past_key_values = None
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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st.rerun()
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# 渲染聊天历史记录
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for i, message in enumerate(st.session_state.history):
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if message["role"] == "user":
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with st.chat_message(name="user", avatar="user"):
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st.markdown(message["content"])
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else:
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with st.chat_message(name="assistant", avatar="assistant"):
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st.markdown(message["content"])
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# 输入框和输出框
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with st.chat_message(name="user", avatar="user"):
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input_placeholder = st.empty()
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with st.chat_message(name="assistant", avatar="assistant"):
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message_placeholder = st.empty()
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# system_prompt = "请根据上下文回答我的问题。答案必须是中文。"
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system_prompt = ""
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memory = MyConversationBufferWindowMemory(k=2)
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def build_prompt(prompt_text):
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h = memory.load_memory_variables({})['history']
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prompt = """你是一个聪明的AI助手，你需要通过已知信息和人类与AI助手之间的友好对话来回答人类的问题。\n"""
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prompt += f"<已知信息>：{get_docs(prompt_text)}</已知信息>\n"
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prompt += """下面是人类和AI助手之间的友好对话。AI助手很会健谈，并从其上下文中提供了许多具体细节。如果AI助手不知道问题的答案，它会如实地说它不知道。
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当前对话：\n"""
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prompt += h
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prompt += f"\n人类：{prompt_text}\n"
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prompt += f"\nAI助手："
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return prompt
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# 获取用户输入
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prompt_text = st.chat_input("请输入您的问题")
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flag = True
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# 如果用户输入了内容,则生成回复
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if prompt_text:
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input_placeholder.markdown(prompt_text)
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history = st.session_state.history
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past_key_values = st.session_state.past_key_values
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# 这里进行测试，将context输入进去
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prompt = build_prompt(prompt_text)
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print(prompt)
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for response, history, past_key_values in model.stream_chat(
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tokenizer,
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prompt_text,
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history,
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past_key_values=past_key_values,
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max_length=max_length,
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top_p=top_p,
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temperature=temperature,
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return_past_key_values=True,
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):
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message_placeholder.markdown(response)
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# 更新历史记录和past key values
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st.session_state.history = history
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st.session_state.past_key_values = past_key_values
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