from turtledemo.penrose import start

from langchain.memory import ConversationBufferMemory
from langchain_core.messages import SystemMessage
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate, HumanMessagePromptTemplate, MessagesPlaceholder
from langchain_core.runnables import RunnablePassthrough
from langchain_ollama import ChatOllama

#提示詞寫法一：
template = ChatPromptTemplate.from_messages([
    SystemMessage(content=("你是一位專業(yè)的Java開發(fā)工程師，擅長解決各種java相關(guān)問題")),
    HumanMessagePromptTemplate.from_template("我的問題：{text}")
])

message = template.format_messages(text="java的三大特性是什么？")

llm_model_ollama = ChatOllama(model="qwen2.5:3b")

result = llm_model_ollama.invoke(message);

#提示詞寫法二：
template2 = ChatPromptTemplate.from_messages([
    ("system","你是一位專業(yè)的陪聊，名字叫{name}，擅長陪用戶聊天"),
    ("human","{question}")
])

question = input("請輸入您的問題")

message2 = template2.format_messages(name="小洋",question=question)

llm_model = ChatOllama(model="qwen2.5:3b")

#輸出解析器 （正常ai會返回token:xxxx content:xxxxx 現(xiàn)在只返回文字內(nèi)容）
strOutParser = StrOutputParser()
chain =  llm_model | strOutParser

# 注釋最終執(zhí)行語句
# result = chain.stream(message2)
# for chunk in result:
#     print(chunk, end="", flush=True)

#建議案例，持續(xù)對話閑聊機器人

def aiChat():
    llm_model_qwen = ChatOllama(model="qwen3:4b")

    index=1;

    # 初始化內(nèi)存，存儲對話歷史
    memory = ConversationBufferMemory()

    while True:

        inputMessage=""

        if index==1:
            inputMessage = input("很高興與您對話，請?zhí)岢瞿膯栴}吧：")
        else:
            inputMessage = input("請繼續(xù)提出您問題：")

        prompt = ChatPromptTemplate.from_messages([
            SystemMessage(content="你是一位心理咨詢師，可以給用戶提供各種情緒價值"),
            HumanMessagePromptTemplate.from_template("{input}"),
            MessagesPlaceholder(variable_name="chat_history"),  # 歷史對話占位符
        ])

        message = prompt.format_messages(input=inputMessage)

        strOutParser =  StrOutputParser()

        chain = memory | llm_model_qwen | strOutParser

        result = chain.stream(message)

        for val in result:
            print(val, sep="\n" ,end="", flush=True)

        index+=1


if __name__ == '__main__':
    aiChat()