from langchain import PromptTemplate
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.llms import CTransformers
from langchain.chains import RetrievalQA
import chainlit as cl

DB_FAISS_PATH = "vectorstores/db_faiss"

custom_prompt_template = """Use the following peices of information to answer the user's question. If you dont know the answer, please just say that you don't know the answer, don't try to make up an answer.

Context:{context}
Question: {question}
+ 

Only return the helpful answer below and nothing else.
Helpful answer: 
"""

def set_custom_prompt():
    """
    Prompt template for QA retrieval for each vectorstores
    """

    prompt = PromptTemplate(template = custom_prompt_template, input_variables = ['context', 'question'])
    return prompt

def load_llm():
    llm = CTransformers(
        model = "llama-2-7b-chat.ggmlv3.q8_0.bin", model_type = "llama",
        max_new_tokens = 512,
        temperature = 0.5,
    )
    return llm

def retrieval_qa_chain(llm, prompt, db):
    qa_chain = RetrievalQA.from_chain_type(
        llm = llm,
        chain_type = "stuff",
        retriever = db.as_retriever(search_kwargs = {'k': 2}),
        return_source_documents = True,
        chain_type_kwargs= {'prompt' : prompt}
    )

    return qa_chain

def qa_bot():
    embeddings = HuggingFaceEmbeddings(model_name = 'sentence-transformers/all-MiniLM-L6-v2', model_kwargs = {'device': 'cpu'})

    db = FAISS.load_local(DB_FAISS_PATH, embeddings)
    llm = load_llm()
    qa_prompt = set_custom_prompt()
    qa =retrieval_qa_chain(llm, qa_prompt, db)

    return qa

def final_result(query):
    qa_result = qa_bot()
    response = qa_result({'query':query})
    return response

#Chainlit
@cl.on_chat_start
async def start():
    chain = qa_bot()
    msg = cl.Message(content = 'Starting the bot....')
    await msg.send()
    msg.content = "Hello, welcome to the PillboxGPT. Write your query below."
    await msg.update()
    cl.user_session.set('chain', chain)

@cl.on_message
async def main(message: cl.Message):
    chain = cl.user_session.get("chain") 
    cb = cl.AsyncLangchainCallbackHandler(
        stream_final_answer=True, answer_prefix_tokens=["FINAL", "ANSWER"]
    )
    cb.answer_reached = True
    res = await chain.acall(message.content, callbacks=[cb])
    answer = res["result"]
    sources = res["source_documents"]

    if sources:
        answer += f"\nSources:" + str(sources)
    else:
        answer += "\nNo sources found"

    await cl.Message(content=answer).send()