#这个版本有个问题，如果在运行状况下，增删文件，不会重新装载文件并构建向量数据库！

import streamlit as st
from llama_index import VectorStoreIndex, SimpleDirectoryReader
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
from llama_index import LangchainEmbedding, ServiceContext
from llama_index import StorageContext, load_index_from_storage
from llama_index import LLMPredictor
#from transformers import HuggingFaceHub
from langchain import HuggingFaceHub
from streamlit.components.v1 import html
from pathlib import Path
from time import sleep
import random
import string

import os
from dotenv import load_dotenv
load_dotenv()

import timeit

st.set_page_config(page_title="Open AI Doc-Chat Assistant", layout="wide")
st.subheader("Open AI Doc-Chat Assistant: Life Enhancing with AI!")

css_file = "main.css"
with open(css_file) as f:
    st.markdown("<style>{}</style>".format(f.read()), unsafe_allow_html=True)
    
HUGGINGFACEHUB_API_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN")

def generate_random_string(length):
    letters = string.ascii_lowercase
    return ''.join(random.choice(letters) for i in range(length))  

if "query_engine" not in st.session_state:
    st.session_state.query_engine = None

with st.sidebar:
    st.subheader("Upload your pdf Documents Here: ")
    pdf_files = st.file_uploader("Choose your PDF Files and Press OK", type=['pdf'], accept_multiple_files=True)    
    if not pdf_files:
        st.warning("请上传文档文件")
        st.stop()      
    else:
        uploadedfile_path=generate_random_string(20)                
        os.makedirs(uploadedfile_path)
        for pdf_file in pdf_files:
            file_path = os.path.join(uploadedfile_path, pdf_file.name)
            with open(file_path, 'wb') as f:
                f.write(pdf_file.read())
            st.success(f"File '{pdf_file.name}' saved successfully.")
    if st.button('Process for QA'):        
        try:
            start_1 = timeit.default_timer() # Start timer
            st.write(f"QA文档加载开始：{start_1}")            
            documents = SimpleDirectoryReader(uploadedfile_path).load_data()
            end_1 = timeit.default_timer() # Start timer
            st.write(f"QA文档加载结束：{end_1}")
            st.write(f"QA文档加载耗时：{end_1 - start_1}")
        except Exception as e:
            print("文档加载出现问题/Waiting for path creation.")
            st.warning("文档加载出现问题/Waiting for path creation.")
            st.stop()                 
        start_2 = timeit.default_timer() # Start timer
        st.write(f"向量模型加载开始：{start_2}")
        if "embed_model" not in st.session_state:
            st.session_state.embed_model = LangchainEmbedding(HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2'))
        end_2 = timeit.default_timer() # Start timer
        st.write(f"向量模型加载结束：{end_2}")
        st.write(f"向量模型加载耗时：{end_2 - start_2}")
        if "llm_predictor" not in st.session_state:
            st.session_state.llm_predictor = LLMPredictor(HuggingFaceHub(repo_id="HuggingFaceH4/starchat-beta", model_kwargs={"min_length":100, "max_new_tokens":1024, "do_sample":True, "temperature":0.2,"top_k":50, "top_p":0.95, "eos_token_id":49155}))
        if "service_context" not in st.session_state:
            st.session_state.service_context = ServiceContext.from_defaults(llm_predictor=st.session_state.llm_predictor, embed_model=st.session_state.embed_model)
        start_3 = timeit.default_timer() # Start timer
        st.write(f"向量库构建开始：{start_3}")
        new_index = VectorStoreIndex.from_documents(documents, service_context=st.session_state.service_context)
        end_3 = timeit.default_timer() # Start timer
        st.write(f"向量库构建结束：{end_3}")
        st.write(f"向量库构建耗时：{end_3 - start_3}")
        directory_path = generate_random_string(20)
        os.makedirs(directory_path)
        new_index.storage_context.persist("directory_path")
        storage_context = StorageContext.from_defaults(persist_dir="directory_path")
        start_4 = timeit.default_timer() # Start timer
        st.write(f"向量库装载开始：{start_4}")
        loadedindex = load_index_from_storage(storage_context=storage_context, service_context=st.session_state.service_context)
        end_4 = timeit.default_timer() # Start timer
        st.write(f"向量库装载结束：{end_4}")
        st.write(f"向量库装载耗时：{end_4 - start_4}")
        query_engine = loadedindex.as_query_engine()
        st.session_state.query_engine = query_engine
    
user_question = st.text_input("Enter your query:")
if user_question !="" and not user_question.strip().isspace() and not user_question == "" and not user_question.strip() == "" and not user_question.isspace():
    print("user question: "+user_question)
    with st.spinner("AI Thinking...Please wait a while to Cheers!"):
        start_5 = timeit.default_timer() # Start timer
        st.write(f"Query Engine - AI QA开始：{start_5}") 
        initial_response = st.session_state.query_engine.query(user_question)
        end_5 = timeit.default_timer() # Start timer
        st.write(f"Query Engine - AI QA结束：{end_5}")
        st.write(f"Query Engine - AI QA耗时：{end_5 - start_5}")        
        temp_ai_response=str(initial_response)
        final_ai_response=temp_ai_response.partition('<|end|>')[0] 
        print("AI Response:\n"+final_ai_response)
        st.write("AI Response:\n\n"+final_ai_response)