import streamlit as st
import torch
from transformers import AutoModel, AutoTokenizer

from api import get_docs
from memory import MyConversationBufferWindowMemory

# 设置页面标题、图标和布局
st.set_page_config(
    page_title="ChatGLM3-6B 演示",
    page_icon=":robot:",
    layout="wide"
)

# 设置为模型ID或本地文件夹路径
model_path = "../THUDM/chatglm3-6b"


@st.cache_resource
def get_model():
    tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
    model = AutoModel.from_pretrained(model_path, trust_remote_code=True).cuda()
    # 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
    # from utils import load_model_on_gpus
    # model = load_model_on_gpus("THUDM/chatglm3-6b", num_gpus=2)
    model = model.eval()
    return tokenizer, model


# 加载Chatglm3的model和tokenizer
tokenizer, model = get_model()

# 初始化历史记录和past key values
if "history" not in st.session_state:
    st.session_state.history = []
if "past_key_values" not in st.session_state:
    st.session_state.past_key_values = None

# 设置max_length、top_p和temperature
max_length = st.sidebar.slider("max_length", 0, 32768, 8192, step=1)
top_p = st.sidebar.slider("top_p", 0.0, 1.0, 0.8, step=0.01)
temperature = st.sidebar.slider("temperature", 0.0, 1.0, 0.6, step=0.01)

# 清理会话历史
buttonClean = st.sidebar.button("清理会话历史", key="clean")
if buttonClean:
    st.session_state.history = []
    st.session_state.past_key_values = None
    if torch.cuda.is_available():
        torch.cuda.empty_cache()
    st.rerun()

# 渲染聊天历史记录
for i, message in enumerate(st.session_state.history):
    if message["role"] == "user":
        with st.chat_message(name="user", avatar="user"):
            st.markdown(message["content"])
    else:
        with st.chat_message(name="assistant", avatar="assistant"):
            st.markdown(message["content"])

# 输入框和输出框
with st.chat_message(name="user", avatar="user"):
    input_placeholder = st.empty()
with st.chat_message(name="assistant", avatar="assistant"):
    message_placeholder = st.empty()

# system_prompt = "请根据上下文回答我的问题。答案必须是中文。"
system_prompt = ""
memory = MyConversationBufferWindowMemory(k=2)
def build_prompt(prompt_text):
    h = memory.load_memory_variables({})['history']
    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"""
    prompt += f"<已知信息>：{get_docs(prompt_text)}</已知信息>\n"
    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。
    当前对话：\n"""
    prompt += h
    prompt += f"\n人类：{prompt_text}\n"
    prompt += f"\nAI助手："
    return prompt


# 获取用户输入
prompt_text = st.chat_input("请输入您的问题")
flag = True
# 如果用户输入了内容,则生成回复
if prompt_text:
    input_placeholder.markdown(prompt_text)
    history = st.session_state.history
    past_key_values = st.session_state.past_key_values
    # 这里进行测试，将context输入进去
    prompt = build_prompt(prompt_text)
    print(prompt)
    for response, history, past_key_values in model.stream_chat(
            tokenizer,
            prompt_text,
            history,
            past_key_values=past_key_values,
            max_length=max_length,
            top_p=top_p,
            temperature=temperature,
            return_past_key_values=True,
    ):
        message_placeholder.markdown(response)

    # 更新历史记录和past key values
    st.session_state.history = history
    st.session_state.past_key_values = past_key_values