# -*- coding: utf-8 -*-
"""app.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/14JJlKx1Oj4px4gdVwHn55FstUl2Dvh9z
"""



#|export
import os

from langchain.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.chains import ConversationalRetrievalChain
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.llms import HuggingFacePipeline
from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory
from langchain.llms import HuggingFaceHub
import pandas as pd
from pathlib import Path
import chromadb
import gradio as gr
from transformers import AutoTokenizer
import transformers
import torch
import tqdm
import accelerate

#|export
def initialize_database(file_path):
    # Create list of documents (when valid)
    collection_name = Path(file_path).stem
    # Fix potential issues from naming convention
    ## Remove space
    collection_name = collection_name.replace(" ","-")
    ## Limit lenght to 50 characters
    collection_name = collection_name[:50]
    ## Enforce start and end as alphanumeric character
    if not collection_name[0].isalnum():
        collection_name[0] = 'A'
    if not collection_name[-1].isalnum():
        collection_name[-1] = 'Z'
    # print('list_file_path: ', list_file_path)
    print('Collection name: ', collection_name)
    # Load document and create splits
    doc_splits = load_doc(file_path)
    # Create or load vector database
    # global vector_db
    vector_db = create_db(doc_splits, collection_name)
    return vector_db, collection_name, "Complete!"

#|export
def load_doc(file_path):
    loader = PyPDFLoader(file_path)
    pages = loader.load()
    text_splitter = RecursiveCharacterTextSplitter(chunk_size = 600, chunk_overlap = 50)
    doc_splits = text_splitter.split_documents(pages)
    return doc_splits

#|export
def create_db(splits, collection_name):
    embedding = HuggingFaceEmbeddings()
    new_client = chromadb.EphemeralClient()
    vectordb = Chroma.from_documents(
        documents=splits,
        embedding=embedding,
        client=new_client,
        collection_name=collection_name,
        # persist_directory=default_persist_directory
    )
    return vectordb

#|export
splt = load_doc('/content/data.pdf')

#|export
vec = initialize_database('/content/data.pdf')

#|export
vec_cre = create_db(splt, 'data')
vec_cre

#|export
def initialize_llmchain(temperature, max_tokens, top_k, vector_db):
    memory = ConversationBufferMemory(
        memory_key="chat_history",
        output_key='answer',
        return_messages=True
    )

    llm = HuggingFaceHub(
            repo_id='mistralai/Mixtral-8x7B-Instruct-v0.1',
            model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k, "load_in_8bit": True}
        )
    retriever=vector_db.as_retriever()
    qa_chain = ConversationalRetrievalChain.from_llm(
        llm,
        retriever=retriever,
        chain_type="stuff",
        memory=memory,
        # combine_docs_chain_kwargs={"prompt": your_prompt})
        return_source_documents=True,
        #return_generated_question=False,
        verbose=False,
    )
    return qa_chain

#|export
qa = initialize_llmchain(0.7, 1024, 1, vec_cre)

#|export
def format_chat_history(message, chat_history):
    formatted_chat_history = []
    for user_message, bot_message in chat_history:
        formatted_chat_history.append(f"User: {user_message}")
        formatted_chat_history.append(f"Assistant: {bot_message}")
    return formatted_chat_history

#|export
def conversation(message, history):
    formatted_chat_history = format_chat_history(message, history)
    response = qa({"question": message, "chat_history": formatted_chat_history})
    response_answer = response["answer"]
    if response_answer.find("Helpful Answer:") != -1:
        response_answer = response_answer.split("Helpful Answer:")[-1]
    return response_answer

#|export
gr.ChatInterface(conversation).launch()