import os
import asyncio
from langchain_community.document_loaders import TextLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain_google_genai import ChatGoogleGenerativeAI, GoogleGenerativeAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.chains import ConversationalRetrievalChain

google_api_key = os.getenv("GOOGLE_API_KEY")

# 🟢 Event loop safe embeddings initializer
def get_embeddings():
    try:
        asyncio.get_running_loop()
    except RuntimeError:
        loop = asyncio.new_event_loop()
        asyncio.set_event_loop(loop)

    return GoogleGenerativeAIEmbeddings(
        model="models/embedding-001",
        google_api_key=google_api_key
    )

# 🟢 Use loader safely
loader = TextLoader("data.txt")
docs = loader.load()

# 🟢 Split text into chunks
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
documents = text_splitter.split_documents(docs)

# 🟢 Create vectorstore with embeddings
embeddings = get_embeddings()
db = FAISS.from_documents(documents, embeddings)

# 🟢 Conversational chain
qa = ConversationalRetrievalChain.from_llm(
    ChatGoogleGenerativeAI(model="gemini-1.5-flash", google_api_key=google_api_key),
    db.as_retriever()
)

# 🟢 Function to interact with bot
chat_history = []

def ask_bot(query: str):
    global chat_history
    result = qa({"question": query, "chat_history": chat_history})
    chat_history.append((query, result["answer"]))
    return result["answer"]