import os
from typing import List
from langchain.document_loaders import PyPDFLoader, TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores.pinecone import Pinecone
from langchain.chains import RetrievalQA
from langchain.chat_models import ChatOpenAI
from langchain.memory import ChatMessageHistory, ConversationBufferMemory
from langchain.docstore.document import Document
import pinecone
import chainlit as cl
from chainlit.types import AskFileResponse
from langchain.prompts import PromptTemplate

from dotenv import load_dotenv
load_dotenv()

openai_api_key = os.getenv("OPENAI_API_KEY")


pinecone.init(
    api_key="2b6aa6bf-2e20-4445-a560-f7dd4952e59e",
    environment="gcp-starter",
)

index_name = "skandhaar"
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)

namespaces = set()

welcome_message = """Welcome to the Chainlit PDF QA demo! To get started:
1. Upload a PDF or text file
"""


def process_file(file: AskFileResponse):
    import tempfile

    if file.type == "text/plain":
        Loader = TextLoader
    elif file.type == "application/pdf":
        Loader = PyPDFLoader

    with tempfile.NamedTemporaryFile(mode="wb", delete=False) as tempfile:
        if file.type == "text/plain":
            tempfile.write(file.content)
        elif file.type == "application/pdf":
            with open(tempfile.name, "wb") as f:
                f.write(file.content)

        loader = Loader(tempfile.name)
        documents = loader.load()
        docs = text_splitter.split_documents(documents)
        for i, doc in enumerate(docs):
            doc.metadata["source"] = f"source_{i}"
        return docs


def get_docsearch(file: AskFileResponse):
    docs = process_file(file)

    # Save data in the user session
    cl.user_session.set("docs", docs)

    # Create a unique namespace for the file
    namespace = str(hash(file.content))

    if namespace in namespaces:
        docsearch = Pinecone.from_existing_index(
            index_name=index_name, embedding=embeddings
        )
    else:
        docsearch = Pinecone.from_documents(
            docs, embeddings, index_name=index_name
        )
        namespaces.add(namespace)

    return docsearch


@cl.on_chat_start
async def start():
    await cl.Avatar(
        name="Chatbot",
        url="https://avatars.githubusercontent.com/u/128686189?s=400&u=a1d1553023f8ea0921fba0debbe92a8c5f840dd9&v=4",
    ).send()
    
    files = None
    while files is None:
        files = await cl.AskFileMessage(
            content=welcome_message,
            accept=["text/plain", "application/pdf"],
            max_size_mb=20,
            timeout=180,
            disable_human_feedback=True,
        ).send()

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

        # No async implementation in the Pinecone client, fallback to sync
        docsearch = await cl.make_async(get_docsearch)(file)

        message_history = ChatMessageHistory()

        memory = ConversationBufferMemory(
            memory_key="chat_history",
            output_key="result",
            chat_memory=message_history,
            return_messages=True,
        )
        PROMPT = PromptTemplate(
            template="""Your name is Skandhaar docchat and you are working for Skandhaar org. and your job is to answer the user question from the given context. You are not allowed make an answer and create something that's not there in the context. You strictly follow the context and give extractive answers.
            Respond for user greetings. If you encounter with out of context questions reply with I'm here to help you with given knowledge source, i can't assist with that.
            
            context:{context}
            
            question:{question}
            
            Answer in the Markdown.
            
            """, 
            input_variables=["context", "question"]
        )

        chain_type_kwargs = {"prompt": PROMPT}
        chain = RetrievalQA.from_chain_type(
            ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0, streaming=True, openai_api_key=openai_api_key),
            chain_type="stuff",
            retriever=docsearch.as_retriever(),
            return_source_documents=True,
            chain_type_kwargs=chain_type_kwargs
        )

        # Let the user know that the system is ready
        msg.content = f"`{file.name}` processed. You can now ask questions!"
        await msg.update()

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


@cl.on_message
async def main(message: cl.Message):
    chain = cl.user_session.get("chain")  # type: ConversationalRetrievalChain
    cb = cl.AsyncLangchainCallbackHandler()
    res = await chain.acall(message.content, callbacks=[cb])
    answer = res["result"]
    source_documents = res["source_documents"]  # type: List[Document]

    text_elements = []  # type: List[cl.Text]

    if source_documents:
        for source_idx, source_doc in enumerate(source_documents):
            source_name = f"source_{source_idx}"
            # Create the text element referenced in the message
            text_elements.append(
                cl.Text(content=source_doc.page_content, name=source_name)
            )
        source_names = [text_el.name for text_el in text_elements]

        if source_names:
            answer += f"\nSources: {', '.join(source_names)}"
        else:
            answer += "\nNo sources found"

    await cl.Message(content=answer, elements=text_elements).send()