app.py

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 langchain import HuggingFaceHub
from streamlit.components.v1 import html
from pathlib import Path
from time import sleep
import random
import string
import sys
import os
from dotenv import load_dotenv
load_dotenv()

st.set_page_config(page_title="Cheers! 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")
repo_id=os.getenv("LLM_RepoID")
model_name=os.getenv("model_name")

documents=[]
wechat_image= "WeChatCode.jpg"

def generate_random_string(length):
    letters = string.ascii_lowercase
    return ''.join(random.choice(letters) for i in range(length))  
random_string = generate_random_string(20)
directory_path=random_string

print(f"定义处理多余的Context文本的函数")
def remove_context(text):
    # 检查 'Context:' 是否存在
    if 'Context:' in text:
        # 找到第一个 '\n\n' 的位置
        end_of_context = text.find('\n\n')
        # 删除 'Context:' 到第一个 '\n\n' 之间的部分
        return text[end_of_context + 2:]  # '+2' 是为了跳过两个换行符
    else:
        # 如果 'Context:' 不存在，返回原始文本
        return text
print(f"处理多余的Context文本函数定义结束")      

st.sidebar.markdown(
    """
    <style>
    .blue-underline {
        text-decoration: bold;
        color: blue;
    }
    </style>
    """,
    unsafe_allow_html=True
)

st.markdown(
    """
    <style>
        [data-testid=stSidebar] [data-testid=stImage]{
            text-align: center;
            display: block;
            margin-left: auto;
            margin-right: auto;
            width: 50%;
        }
    </style>
    """, unsafe_allow_html=True
)

question = st.text_input("Enter your query here:")
display_output_text = st.checkbox("Check AI Repsonse", key="key_checkbox", help="Check me to get AI Response.")

with st.sidebar:    
    pdf_files = st.file_uploader("Upload file and start AI Doc-Chat.", type=['pdf'], accept_multiple_files=True)
    st.write("Disclaimer: This app is for information purpose only. NO liability could be claimed against whoever associated with this app in any manner. User should consult a qualified legal professional for legal advice.")
    st.sidebar.markdown("Contact: [[email protected]](mailto:[email protected])")
    st.sidebar.markdown('WeChat: <span class="blue-underline">pat2win</span>, or scan the code below.', unsafe_allow_html=True)
    st.image(wechat_image)
    st.sidebar.markdown('<span class="blue-underline">Life Enhancing with AI.</span>', unsafe_allow_html=True)      
    st.subheader("Enjoy chatting!")
    if pdf_files:
        os.makedirs(directory_path)
        for pdf_file in pdf_files:
            file_path = os.path.join(directory_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.")
        documents = SimpleDirectoryReader(directory_path).load_data()    
    else:
        print("waiting for path creation.")
        sys.exit()

embed_model = LangchainEmbedding(HuggingFaceEmbeddings(model_name=model_name))

llm = HuggingFaceHub(repo_id=repo_id,
                     model_kwargs={"min_length":512,
                                   "max_new_tokens":1024, "do_sample":True,
                                   "temperature":0.1,
                                   "top_k":50,
                                   "top_p":0.95, "eos_token_id":49155}) 

llm_predictor = LLMPredictor(llm)

service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, embed_model=embed_model)

new_index = VectorStoreIndex.from_documents(
    documents,
    service_context=service_context,
)

if question !="" and not question.strip().isspace() and not question == "" and not question.strip() == "" and not question.isspace():
    if display_output_text==True:
      with st.spinner("AI Thinking...Please wait a while to Cheers!"):
        new_index.storage_context.persist("directory_path")
        storage_context = StorageContext.from_defaults(persist_dir="directory_path")
        loadedindex = load_index_from_storage(storage_context=storage_context, service_context=service_context)
        query_engine = loadedindex.as_query_engine() 
        initial_response = query_engine.query(question)
        cleaned_initial_ai_response=st(initial_response)
        final_ai_response = cleaned_initial_ai_response.split('<|end|>\n<|system|>\n<|end|>\n<|user|>')[0].strip().replace('\n\n', '\n').replace('<|end|>', '').replace('<|user|>', '').replace('<|system|>', '').replace('<|assistant|>', '')
        #temp_ai_response=str(initial_response)
        #final_ai_response=temp_ai_response.partition('<|end|>')[0]
        st.write("AI Response:\n\n"+final_ai_response)
    #else:
     #   print("Check the Checkbox to get AI Response.")
      #  sys.exit()          
#else:
 #   print("Please enter your question first.")
  #  st.stop()