import os
from typing import List

# from langchain.embeddings.openai import OpenAIEmbeddings # ORIGINAL
from langchain_community.embeddings import FastEmbedEmbeddings # JB

from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.chains import (
    ConversationalRetrievalChain,
)
from langchain.document_loaders import PyPDFLoader
# from langchain.chat_models import ChatOpenAI # ORIGINAL
from langchain_groq import ChatGroq # JB

from langchain.prompts.chat import (
    ChatPromptTemplate,
    SystemMessagePromptTemplate,
    HumanMessagePromptTemplate,
)
from langchain.docstore.document import Document
from langchain.memory import ChatMessageHistory, ConversationBufferMemory
from chainlit.types import AskFileResponse

# import chainlit as cl # JB TEST

# JB
from dotenv import load_dotenv
import glob
load_dotenv()  #
groq_api_key = os.environ['GROQ_API_KEY']
# groq_api_key = "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" # os.environ['GROQ_API_KEY']
print"groq_api_key: ", groq_api_key)

text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)

system_template = """Use the following pieces of context to answer the users question.
If you don't know the answer, just say that you don't know, don't try to make up an answer.
ALWAYS return a "SOURCES" part in your answer.
The "SOURCES" part should be a reference to the source of the document from which you got your answer.

And if the user greets with greetings like Hi, hello, How are you, etc reply accordingly as well.

Example of your response should be:

The answer is foo
SOURCES: xyz


Begin!
----------------
{summaries}"""
messages = [
    SystemMessagePromptTemplate.from_template(system_template),
    HumanMessagePromptTemplate.from_template("{question}"),
]
prompt = ChatPromptTemplate.from_messages(messages)
chain_type_kwargs = {"prompt": prompt}


def process_file(file: AskFileResponse):
    import tempfile

    with tempfile.NamedTemporaryFile(mode="w", delete=False) as tempfile:
        with open(tempfile.name, "wb") as f:
            f.write(file.content)

    pypdf_loader = PyPDFLoader(tempfile.name)
    texts = pypdf_loader.load_and_split()
    texts = [text.page_content for text in texts]
    return texts


@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 PDF file to begin!",
            accept=["application/pdf"],
            max_size_mb=20,
            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_file(file)

    print(texts[0])

    # Create a metadata for each chunk
    metadatas = [{"source": f"{i}-pl"} for i in range(len(texts))]

    # Create a Chroma vector store
    # embeddings = OpenAIEmbeddings()  # ORIGINAL
    embeddings = FastEmbedEmbeddings # JB
    docsearch = await cl.make_async(Chroma.from_texts)(
        texts, embeddings, metadatas=metadatas
    )

    message_history = ChatMessageHistory()

    memory = ConversationBufferMemory(
        memory_key="chat_history",
        output_key="answer",
        chat_memory=message_history,
        return_messages=True,
    )



    # JB
    # llm = ChatGroq(temperature=0.2, groq_api_key=groq_api_key, model_name='mixtral-8x7b-32768')

    
    # Create a chain that uses the Chroma vector store
    chain = ConversationalRetrievalChain.from_llm(
        # ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0, streaming=True), # ORIGINAL
        ChatGroq(temperature=0.2, groq_api_key=groq_api_key, model_name='mixtral-8x7b-32768', streaming=True), # JB
        chain_type="stuff",
        retriever=docsearch.as_retriever(),
        memory=memory,
        return_source_documents=True,
    )

    # 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", chain)


@cl.on_message
async def main(message):
    chain = cl.user_session.get("chain")  # type: ConversationalRetrievalChain
    cb = cl.AsyncLangchainCallbackHandler()

    res = await chain.acall(message.content, callbacks=[cb])
    answer = res["answer"]
    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()