from llama_index import (ServiceContext, StorageContext,
                         load_index_from_storage, Document, set_global_service_context)
from llama_index.node_parser import SimpleNodeParser
from llama_index import VectorStoreIndex
from llama_index.llms import OpenAI, ChatMessage, MessageRole
from llama_index.prompts import ChatPromptTemplate
# from llama_index import set_global_handler
from llama_index.chat_engine.types import ChatMode
from dotenv import load_dotenv
import os
import re

# set_global_handler("simple")

# load .env
load_dotenv()
OPEN_API_KEY = os.getenv('OPEN_API_KEY')

# config llm context
llm = OpenAI(model="gpt-4", temperature=0, max_tokens=256, api_key=OPEN_API_KEY)
service_context = ServiceContext.from_defaults(llm=llm)
set_global_service_context(service_context)

if __name__ == '__main__':
    if not os.path.exists("./index/lock"):
        documents = []
        for filename in os.listdir("./transcripts"):
            episode_number = re.search(r'\d+', filename).group()
            with open("./transcripts/" + filename, 'r') as f:
                title = f.readline().strip()
                downloads = f.readline().strip()
                content = f.read()
            document = Document(
                text=content,
                doc_id=filename,
                metadata={
                    "episode_number": episode_number,
                    "episode_title": title,
                    "episode_downloads": downloads,
                    "episode_url": f"https://darknetdiaries.com/episode/{episode_number}/"
                }
            )
            documents.append(document)

        parser = SimpleNodeParser.from_defaults()
        nodes = parser.get_nodes_from_documents(documents)

        index = VectorStoreIndex(nodes, show_progress=True)
        index.storage_context.persist(persist_dir="./index")
        open("./index/lock", 'a').close()
    else:
        print("Loading index...")
        storage_context = StorageContext.from_defaults(persist_dir="./index")
        index = load_index_from_storage(storage_context)

    chat_text_qa_msgs = [
        ChatMessage(
            role=MessageRole.SYSTEM,
            content=(
                "You have been trained on the Darknet Diaries podcast transcripts with data from october 6 2023."
                "You are an expert about it and will answer as such. You know about every episode up to number 138."
                "Always answer the question, even if the context isn't helpful."
                "Mention the number and title of the episodes you are referring to."
            )
        ),
        ChatMessage(
            role=MessageRole.USER,
            content=(
                "Context information is below.\n"
                "---------------------\n"
                "{context_str}\n"
                "---------------------\n"
                "Given the context information and not prior knowledge,"
                "answer the question: {query_str}\n"
            )
        )
    ]
    text_qa_template = ChatPromptTemplate(chat_text_qa_msgs)

    chat_refine_msgs = [
        ChatMessage(
            role=MessageRole.SYSTEM,
            content="Always answer the question, even if the context isn't helpful.",
        ),
        ChatMessage(
            role=MessageRole.USER,
            content=(
                "We have the opportunity to refine the original answer "
                "(only if needed) with some more context below.\n"
                "------------\n"
                "{context_msg}\n"
                "------------\n"
                "Given the new context, refine the original answer to better "
                "answer the question: {query_str}. "
                "If the context isn't useful, output the original answer again.\n"
                "Original Answer: {existing_answer}"
            ),
        ),
    ]
    refine_template = ChatPromptTemplate(chat_refine_msgs)

    chat_engine = index.as_chat_engine(
        text_qa_template=text_qa_template,
        refine_template=refine_template,
        chat_mode=ChatMode.OPENAI
    )

    while True:
        try:
            chat_engine.chat_repl()
        except KeyboardInterrupt:
            break