from fastapi import FastAPI, Depends, HTTPException, status
from fastapi.security import APIKeyHeader
from pydantic import BaseModel
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import Chroma
from langchain_google_genai import GoogleGenerativeAIEmbeddings, ChatGoogleGenerativeAI
from langchain.chains import ConversationalRetrievalChain
import pdfplumber
import os
import google.generativeai as genai
from deep_translator import GoogleTranslator

# 初始化 FastAPI 應用
app = FastAPI()

print('程式初始化')

# 設定 Google API 金鑰
api_key = os.getenv("GOOGLE_API_KEY")
if not api_key:
    raise ValueError("GOOGLE_API_KEY is not set")
genai.configure(api_key=api_key)

# 選擇模型
llm_model = 'gemini-1.5-flash'
embeddings_model = "models/embedding-001"
pdf_dir = 'data'

# 讀取 PDF 檔案並初始化資料
print('-' * 21, '讀取資料', '-' * 21)
docs = ""
for filename in os.listdir(pdf_dir):
    if filename.endswith('.pdf'):
        print(filename)
        with pdfplumber.open(os.path.join(pdf_dir, filename)) as pdf:
            for page in pdf.pages:
                docs += page.extract_text()

print('-' * 21, '讀取完成', '-' * 21)

# 分割文本
if docs:
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
    texts = text_splitter.split_text(docs)

    # 建立嵌入模型和檢索器
    embeddings = GoogleGenerativeAIEmbeddings(
        model=embeddings_model, google_api_key=api_key
    )
    retriever = Chroma.from_texts(texts, embeddings).as_retriever(search_kwargs={"k": 1})
    print('分割文本完成')

    # 初始化 Gemini 模型
    llm = ChatGoogleGenerativeAI(
        model=llm_model, temperature=0.1, google_api_key=api_key
    )
    print('模型載入完成')

else:
    raise ValueError("No documents found in the 'data' directory.")


# 定義翻譯函數
def translate_to_english(text):
    return GoogleTranslator(source='auto', target='en').translate(text)

def translate_to_chinese(text):
    return GoogleTranslator(source='auto', target='zh-TW').translate(text)





# 初始化 chat_history 作為全局變數
chat_history = []

# 定義 invoke 函數
def invoke(question: str):
    print('invoke 函數觸發')
    try:
        # 初始化 ConversationalRetrievalChain
        qa_chain = ConversationalRetrievalChain.from_llm(
            llm=llm, retriever=retriever
        )

        # 呼叫 QA chain 並處理回應
        question = translate_to_english(question)
        response = qa_chain.invoke({"question": question, "chat_history": chat_history})
        response = translate_to_chinese(response['answer'])
        # return response['answer']
        return response

    except Exception as e:
        print(f"Error during invoke: {e}")
        return "An error occurred while processing the request."


# 定義請求資料模型
class InvokeRequest(BaseModel):
    question: str

# API 金鑰設定
API_KEY_NAME = "X-API-Key"
API_KEY = os.getenv("API_KEY", "your_secret_api_key")
api_key_header = APIKeyHeader(name=API_KEY_NAME)

# 定義 API 金鑰驗證的依賴函數
def get_api_key(api_key: str = Depends(api_key_header)):
    if api_key != API_KEY:
        raise HTTPException(
            status_code=status.HTTP_403_FORBIDDEN,
            detail="Invalid API Key",
        )
    return api_key

# 根端點，用於檢查服務是否運行正常
@app.get("/")
async def read_root():
    return {"message": "Hello, World!"}

# 定義 POST 端點，讓使用者發送問題並獲得模型回應
@app.post("/invoke")
async def rag_endpoint(request: InvokeRequest, api_key: str = Depends(get_api_key)):
    result = invoke(request.question)
    return {"result": result}

# 啟動應用程式：執行命令 `uvicorn main:app --reload`
if __name__ == "__main__":
    import uvicorn
    uvicorn.run("main:app", host="0.0.0.0", port=8000, reload=True)