import os
from typing import List
from chainlit.types import AskFileResponse
from aimakerspace.text_utils import CharacterTextSplitter, TextFileLoader
from aimakerspace.openai_utils.prompts import (
    UserRolePrompt,
    SystemRolePrompt,
    AssistantRolePrompt,
)
from aimakerspace.openai_utils.embedding import EmbeddingModel
from aimakerspace.vectordatabase import VectorDatabase
from aimakerspace.openai_utils.chatmodel import ChatOpenAI
import chainlit as cl

system_template = """\
Use the following context to answer a users question. If you cannot find the answer in the context, say you don't know the answer."""
system_role_prompt = SystemRolePrompt(system_template)

user_prompt_template = """\
Context:
{context}

Question:
{question}
"""
user_role_prompt = UserRolePrompt(user_prompt_template)

class RetrievalAugmentedQAPipeline:
    def __init__(self, llm: ChatOpenAI(), vector_db_retriever: VectorDatabase) -> None:
        self.llm = llm
        self.vector_db_retriever = vector_db_retriever

    async def arun_pipeline(self, user_query: str):
        context_list = self.vector_db_retriever.search_by_text(user_query, k=4)

        context_prompt = ""
        for context in context_list:
            #context_prompt += context[0] + "\n" #add Source info to the context
            context_prompt += f"{context[0]} (Source: {context[2].get('filename')}, Chunk: {context[2].get('chunk_index')})\n"

        formatted_system_prompt = system_role_prompt.create_message()

        formatted_user_prompt = user_role_prompt.create_message(question=user_query, context=context_prompt)

        async def generate_response():
            async for chunk in self.llm.astream([formatted_system_prompt, formatted_user_prompt]):
                yield chunk

        return {"response": generate_response(), "context": context_list}

text_splitter = CharacterTextSplitter()


def process_text_file(file: AskFileResponse):
    import tempfile

    with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".txt") as temp_file:
        temp_file_path = temp_file.name

    with open(temp_file_path, "wb") as f:
        f.write(file.content)

    text_loader = TextFileLoader(temp_file_path)
    #documents = text_loader.load_documents() #YL edit 1 line below
    documents_with_metadata = text_loader.load_documents_with_metadata()
    #texts = text_splitter.split_texts(documents) #YL edit 1 line below
    texts_with_matadata = text_splitter.split_texts_with_metadata(documents_with_metadata)
    return texts_with_matadata


@cl.on_chat_start
async def on_chat_start():
    files = None

    # Wait for the user to upload a file
    while files == None:
        files = await cl.AskFileMessage(
            content="Please upload a Text File file to begin!",
            accept=["text/plain"],
            max_size_mb=2,
            timeout=180,
        ).send()

    file = files[0]

    msg = cl.Message(
        content=f"Processing `{file.name}`...", disable_human_feedback=True
    )
    await msg.send()

    # load the file
    #texts = process_text_file(file) #YL added 1 line below
    texts_with_matadata = process_text_file(file)

    #print(f"Processing {len(texts)} text chunks") #YL added 1 line below
    print(f"Processing {len(texts_with_matadata)} text chunks")

    # Create a dict vector store
    vector_db = VectorDatabase()
    #vector_db = await vector_db.abuild_from_list(texts) #YL added 3 lines below
    vector_db = await vector_db.abuild_from_list(
        [text["text"] for text in texts_with_matadata],
        [text["metadata"] for text in texts_with_matadata])
    
    chat_openai = ChatOpenAI()

    # Create a chain
    retrieval_augmented_qa_pipeline = RetrievalAugmentedQAPipeline(
        vector_db_retriever=vector_db,
        llm=chat_openai
    )
    
    # Let the user know that the system is ready
    msg.content = f"Processing `{file.name}` done. You can now ask questions!"
    await msg.update()

    cl.user_session.set("chain", retrieval_augmented_qa_pipeline)


@cl.on_message
async def main(message):
    chain = cl.user_session.get("chain")

    msg = cl.Message(content="")
    result = await chain.arun_pipeline(message.content)

    async for stream_resp in result["response"]:
        await msg.stream_token(stream_resp)

    #YL added someline as below to append the Source and Chunk info
    context_info = "\n\nSources:\n"
    for context in result["context"]:
        source_info = f"Source: {context[2].get('filename')}, Chunk: {context[2].get('chunk_index')}"
        context_info += f"{source_info}\n"

    msg.content += context_info
    await msg.send()