app.py

import os 
import streamlit as st  
import re 
from tempfile import NamedTemporaryFile
import time
import pathlib
#from PyPDF2 import PdfReader

from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline
from langchain_community.llms import LlamaCpp
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain.chains import RetrievalQA
from langchain_community.vectorstores import FAISS
from langchain.chains.question_answering import load_qa_chain
from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT
from langchain_community.document_loaders import TextLoader
from langchain_community.document_loaders import PyPDFLoader
from langchain.memory import ConversationBufferWindowMemory
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from langchain.memory.chat_message_histories.streamlit import StreamlitChatMessageHistory
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.llms import HuggingFaceHub

SECRET_TOKEN = os.getenv("HF_TOKEN")
os.environ["HUGGINGFACEHUB_API_TOKEN"] = SECRET_TOKEN


# sidebar contents
with st.sidebar:
	st.title('DOC-QA DEMO ')
	st.markdown('''
	## About 	
	Detail this application:
	- LLM model: Phi-2-4bit 
	- Hardware resource : Huggingface space 8 vCPU 32 GB 
	''')
	
def split_docs(documents,chunk_size=1000):
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=200)
    sp_docs = text_splitter.split_documents(documents)
    return sp_docs

@st.cache_resource
def load_llama2_llamaCpp():
    core_model_name = "phi-2.Q4_K_M.gguf"
    #n_gpu_layers = 32
    n_batch = 512
    callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
    llm = LlamaCpp(
        model_path=core_model_name,
        #n_gpu_layers=n_gpu_layers,
        n_batch=n_batch,
        callback_manager=callback_manager,
        verbose=True,n_ctx = 4096, temperature = 0.1, max_tokens = 128
    )
    return llm

def set_custom_prompt():
    custom_prompt_template = """ Use the following pieces of information from context to answer the user's question.
    If you don't know the answer, don't try to make up an answer.
    Context : {context}
    Question : {question}
    Please answer the questions in a concise and straightforward manner. 
    Helpful answer:
    """
    prompt = PromptTemplate(template=custom_prompt_template, input_variables=['context',
                                                                              'question',
                                                                              ])
    return prompt


@st.cache_resource
def load_embeddings():
    embeddings = HuggingFaceEmbeddings(model_name = "thenlper/gte-base",
                                       model_kwargs = {'device': 'cpu'})
    return embeddings



def main():
    data = []
    sp_docs_list = []
    msgs = StreamlitChatMessageHistory(key="langchain_messages")
    print(msgs)
    if "messages" not in st.session_state:
        st.session_state.messages = []

    repo_id = "mistralai/Mistral-7B-Instruct-v0.2" 
    llm = HuggingFaceHub(
    repo_id=repo_id, model_kwargs={"temperature": 0.1, "max_length": 128})

    # llm = load_llama2_llamaCpp()
    qa_prompt = set_custom_prompt()
    embeddings = load_embeddings()

    uploaded_file = st.file_uploader('Choose your .pdf file', type="pdf")
    if uploaded_file is not None :
        with NamedTemporaryFile(dir='PDF', suffix='.pdf', delete=False) as f:
            f.write(uploaded_file.getbuffer())
            print(f.name)
            #filename = f.name
            loader = PyPDFLoader(f.name)
            pages = loader.load_and_split()
            data.extend(pages)
            #st.write(pages)
            f.close()
            os.unlink(f.name)
            os.path.exists(f.name)
    if len(data) > 0 :
        embeddings = load_embeddings()
        sp_docs = split_docs(documents = data)
        st.write(f"This document have {len(sp_docs)} chunks")
        sp_docs_list.extend(sp_docs)
    try:
        db = FAISS.from_documents(sp_docs_list, embeddings)
        memory = ConversationBufferMemory(memory_key="chat_history", 
                                  return_messages=True, 
                                  input_key="query", 
                                  output_key="result")
        qa_chain = RetrievalQA.from_chain_type(
            llm = llm,
            chain_type = "stuff",
            retriever = db.as_retriever(search_kwargs = {'k':3}), 
            return_source_documents = True,
            memory = memory,
            chain_type_kwargs = {"prompt":qa_prompt})
        for message in st.session_state.messages:
            with st.chat_message(message["role"]):
                st.markdown(message["content"])
    
            # Accept user input
        if query := st.chat_input("What is up?"):
            # Display user message in chat message container
            with st.chat_message("user"):
                st.markdown(query)
            # Add user message to chat history
            st.session_state.messages.append({"role": "user", "content": query})
    
            start = time.time()
    
            response = qa_chain({'query': query})
            
            with st.chat_message("assistant"):
                st.markdown(response['result'])
            
            end = time.time()
            st.write("Respone time:",int(end-start),"sec")
            print(response)
                    
            # Add assistant response to chat history
            st.session_state.messages.append({"role": "assistant", "content": response['result']})
    
            with st.expander("See the related documents"):
                for count, url in enumerate(response['source_documents']):
                    st.write(str(count+1)+":", url)
    
        clear_button = st.button("Start new convo")
        if clear_button :
            st.session_state.messages = []
            qa_chain.memory.chat_memory.clear() 

    except:
        st.write("Plaese upload your pdf file.")


if __name__ == '__main__':
	main()