import os
import uuid
from dotenv import load_dotenv
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import PyMuPDFLoader
from qdrant_client import QdrantClient
from qdrant_client.http.models import Distance, VectorParams
from langchain_openai.embeddings import OpenAIEmbeddings
from langchain.storage import LocalFileStore
from langchain_qdrant import QdrantVectorStore
from langchain.embeddings import CacheBackedEmbeddings
from langchain_core.prompts import ChatPromptTemplate
from chainlit.types import AskFileResponse
from langchain_core.globals import set_llm_cache
from langchain_openai import ChatOpenAI
from langchain_core.caches import InMemoryCache
from operator import itemgetter
from langchain_core.runnables.passthrough import RunnablePassthrough
import chainlit as cl
from langchain_core.runnables.config import RunnableConfig
import time

load_dotenv()

os.environ["LANGCHAIN_PROJECT"] = f"AIM W8D1 - {uuid.uuid4().hex[0:8]}"
os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_ENDPOINT"] = "https://api.smith.langchain.com"

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

rag_system_prompt_template = """\
You are a helpful assistant. Think through your answers carefully using a step-by-step approach.
"""

rag_message_list = [
    {"role" : "system", "content" : rag_system_prompt_template},
]

rag_user_prompt_template = """\
Question:
{question}
Context:
{context}
"""

chat_prompt = ChatPromptTemplate.from_messages([
    ("system", rag_system_prompt_template),
    ("human", rag_user_prompt_template)
])

chat_model = ChatOpenAI(model="gpt-4o-mini")

core_embeddings = OpenAIEmbeddings(model="text-embedding-3-small")

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)

    Loader = PyMuPDFLoader

    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




@cl.on_chat_start
async def on_chat_start():

    files = None

    while files == None:
        files = await cl.AskFileMessage(
            content="Please upload a PDF file to begin!",
            accept=["application/pdf"],
            max_size_mb=20,
            timeout=180,
            max_files=1
        ).send()

    file = files[0]
    msg = cl.Message(
        content=f"Processing `{file.name}`...",
    )
    await msg.send()
    docs = process_file(file)

    # Typical QDrant Client Set-up
    collection_name = f"pdf_to_parse_{uuid.uuid4()}"
    client = QdrantClient(":memory:")
    client.create_collection(
        collection_name=collection_name,
        vectors_config=VectorParams(size=1536, distance=Distance.COSINE),
    )

    # Adding cache!
    store = LocalFileStore("./cache/")
    cached_embedder = CacheBackedEmbeddings.from_bytes_store(
        core_embeddings, store, namespace=core_embeddings.model
    )

    # Typical QDrant Vector Store Set-up
    vectorstore = QdrantVectorStore(
        client=client,
        collection_name=collection_name,
        embedding=cached_embedder)
    vectorstore.add_documents(docs)
    retriever = vectorstore.as_retriever(search_type="mmr", search_kwargs={"k": 3})

    retrieval_augmented_qa_chain = (
        {"context": itemgetter("question") | retriever, "question": itemgetter("question")}
        | RunnablePassthrough.assign(context=itemgetter("context"))
        | chat_prompt | chat_model
    )

    msg.content = f"Processing `{file.name}` done. You can now ask questions!"
    await msg.update()

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


@cl.author_rename
def rename(orig_author: str):

    rename_dict = {"ChatOpenAI": "the Generator...", "VectorStoreRetriever": "the Retriever..."}
    return rename_dict.get(orig_author, orig_author)


@cl.on_message
async def main(message: cl.Message):
    """
    MESSAGE CODE HERE
    """
    runnable = cl.user_session.get("chain")

    msg = cl.Message(content="")


    async for chunk in runnable.astream(
        {"question": message.content},
    ):
        await msg.stream_token(chunk.content)

    await msg.send()