'''
1.更新了llama-index的库。对应的函数名和用法都有所改变。
'''

# import gradio as gr
import openai
import requests
import csv
from llama_index import PromptHelper
# from llama_index import GPTSimpleVectorIndex ## renamed in the latest version. 
from llama_index import LLMPredictor
from llama_index import ServiceContext
from langchain.chat_models import ChatOpenAI
from langchain import OpenAI
# from fastapi import FastAPI #* 实现流式数据
# from fastapi.responses import StreamingResponse #* 实现流式数据
import sys
import os
import math
import pandas as pd
import numpy as np
import PyPDF2

##* in the latest version: GPTSimpleVectorIndex was renamed to GPTVectorStoreIndex, try removing it from the end of your imports
from llama_index import SimpleDirectoryReader, GPTListIndex, readers, GPTVectorStoreIndex, LLMPredictor, PromptHelper
from llama_index import StorageContext, load_index_from_storage
from llama_index import ServiceContext
from llama_index import download_loader
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
import sys
import os
from rich import print

## enironment settings. 
os.environ["OPENAI_API_KEY"] = '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'
openai.api_key = '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'
# file_path = "/Users/yunshi/Downloads/txt_dir/Sparks_of_AGI.pdf"
# file_path = "/Users/yunshi/Downloads/txt_dir/2023年百人会电动论坛 纪要 20230401.pdf"

## 建立index或者的过程。
def construct_index(directory_path):
    # file_path = f"{directory_path}/uploaded_file.pdf"

    file_path = directory_path

    # set maximum input si771006
    # max_input_size = 4096 #* working
    max_input_size = 4096
    # set number of output tokens
    # num_outputs = 3000 #* working
    num_outputs = 1000
    # set maximum chunk overlap
    max_chunk_overlap = -1000 #* working
    # set chunk size limit
    # chunk_size_limit = 600
    chunk_size_limit = 6000 #* working

    # ## add chunk_overlap_ratio according to github.
    # chunk_overlap_ratio= 0.1


    # define LLM
    # llm_predictor = LLMPredictor(llm=OpenAI(temperature=0.5, model_name="gpt-3.5-turbo", max_tokens=2000))
    llm_predictor = LLMPredictor(llm=ChatOpenAI(temperature=0.7, model_name="gpt-3.5-turbo-16k", max_tokens=512,streaming=True))
    
    ## 好像work了，2023.09.22, 注意这里的写法有调整。
    # prompt_helper = PromptHelper(max_input_s≈ize, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit)
    prompt_helper = PromptHelper(max_input_size, num_outputs, chunk_overlap_ratio= 0.1, chunk_size_limit=chunk_size_limit)
    service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, prompt_helper=prompt_helper)

    ## 如果是txt文件，那么需要用如下命令。注意与PDF文件的区别。
    # documents = SimpleDirectoryReader(directory_path).load_data() 
    
    ## 如果是PDF文件，那么需要用如下命令。注意与txt文件的区别。切需要from llama_index import download_loader。 
    #NOTE: 这里可以问：give me an example of GPT-4 solving math problem. 会回答关于这个PDF中的内容，所以可以确认这个程序调用了in-context learning的功能。
    # CJKPDFReader = download_loader("CJKPDFReader") ## 最新的版本好像不行了，需要用下面的命令。
    # loader = CJKPDFReader()
    PDFReader = download_loader("PDFReader") # working。
    loader = PDFReader()
    # documents = loader.load_data(file=directory_path) #! 注意这里是指向文件本身，而不同于txt文件的指文件夹。
    print('directory_path now:', directory_path)
    # print('111')
    # documents = loader.load_data(file="/Users/yunshi/Downloads/txt_dir/Sparks_of_AGI.pdf") #! 注意这里是指向文件本身，而不同于txt文件的指文件夹。
    documents = loader.load_data(file=directory_path) #! 注意这里是指向文件本身，而不同于txt文件的指文件夹。
    print('222')
    # index = GPTSimpleVectorIndex(
    #     documents, llm_predictor=llm_predictor, prompt_helper=prompt_helper
    # )

    # index = GPTSimpleVectorIndex.from_documents(documents, service_context=service_context) ## oringinal version, working. 
    # print('documents:', documents)
    index = GPTVectorStoreIndex.from_documents(documents, service_context=service_context) #* the funciton renamed. 
    print('333')

    # index.save_to_disk('/Users/yunshi/Downloads/txt_dir/index.json') ## in the latest version, this function is not working.

    return index, service_context

def process_file(file_path,username):
    print('process_file starts')
    # file_path = "/Users/yunshi/Downloads/txt_dir/Sparks_of_AGI.pdf"
    #! 第一次运行是需要开启这个function。如果测试通过index，因此不需要在运行了。记得上传PDF和JSON文件到云服务器上。
    index, service_context = construct_index(file_path)
    # index.storage_context.persist(persist_dir="/Users/yunshi/Downloads/txt_dir/") #* 存储到本地，为以后调用。
    index.storage_context.persist(persist_dir=f"./{username}/") #* 存储到本地，为以后调用。
    print(index)

# process_file(file_path)