app.py

import os
import streamlit as st
from langchain.text_splitter import RecursiveCharacterTextSplitter
import re
import pathlib

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
from langchain.llms import HuggingFacePipeline
from langchain.llms import LlamaCpp
from langchain import PromptTemplate, LLMChain
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.chains import RetrievalQA
from langchain.vectorstores import FAISS
from PyPDF2 import PdfReader
import os
import time
from langchain.chains.question_answering import load_qa_chain
from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT

from langchain.document_loaders import TextLoader
from langchain.document_loaders import PyPDFLoader
from langchain.document_loaders import Docx2txtLoader
from langchain.document_loaders.image import UnstructuredImageLoader
from langchain.document_loaders import UnstructuredHTMLLoader
from langchain.document_loaders import UnstructuredPowerPointLoader
from langchain.document_loaders import TextLoader
from langchain.memory import ConversationBufferWindowMemory

from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from langchain.memory.chat_message_histories.streamlit import StreamlitChatMessageHistory

# sidebar contents
with st.sidebar:
	st.title('DOC-QA DEMO ')
	st.markdown('''
	## About 	
	Detail this application:
	- LLM model: llama2-7b-chat-4bit 
	- Hardware resource : Huggingface space 8 vCPU 32 GB 
	''')

class UploadDoc:
    def __init__(self, path_data):
        self.path_data = path_data

    def prepare_filetype(self):
        extension_lists = {
            ".docx": [],
            ".pdf": [],
            ".html": [],
            ".png": [],
            ".pptx": [],
            ".txt": [],
        }

        path_list = []
        for path, subdirs, files in os.walk(self.path_data):
            for name in files:
                path_list.append(os.path.join(path, name))
                #print(os.path.join(path, name))

        # Loop through the path_list and categorize files
        for filename in path_list:
            file_extension = pathlib.Path(filename).suffix
            #print("File Extension:", file_extension)
            
            if file_extension in extension_lists:
                extension_lists[file_extension].append(filename)
        return extension_lists
    
    def upload_docx(self, extension_lists):
        #word
        data_docxs = []
        for doc in extension_lists[".docx"]:
            loader = Docx2txtLoader(doc)
            data = loader.load()
            data_docxs.extend(data)
        return data_docxs
    
    def upload_pdf(self, extension_lists):
        #pdf 
        data_pdf = []
        for doc in extension_lists[".pdf"]:
            loader = PyPDFLoader(doc)
            data = loader.load_and_split()
            data_pdf.extend(data)
        return data_pdf
    
    def upload_html(self, extension_lists):
        #html 
        data_html = []
        for doc in extension_lists[".html"]:
            loader = UnstructuredHTMLLoader(doc)
            data = loader.load()
            data_html.extend(data)
        return data_html
    
    def upload_png_ocr(self, extension_lists):
        #png ocr
        data_png = []
        for doc in extension_lists[".png"]:
            loader = UnstructuredImageLoader(doc)
            data = loader.load()
            data_png.extend(data)
        return data_png 
    
    def upload_pptx(self, extension_lists):
        #power point
        data_pptx = []
        for doc in extension_lists[".pptx"]:
            loader = UnstructuredPowerPointLoader(doc)
            data = loader.load()
            data_pptx.extend(data)
        return data_pptx
    
    def upload_txt(self, extension_lists):
        #txt 
        data_txt = []
        for doc in extension_lists[".txt"]:
            loader = TextLoader(doc)
            data = loader.load()
            data_txt.extend(data)
        return data_txt
    
    def count_files(self, extension_lists):
        file_extension_counts = {}
        # Count the quantity of each item
        for ext, file_list in extension_lists.items():
            file_extension_counts[ext] = len(file_list)
        return print(f"number of file:{file_extension_counts}")
        # Print the counts
        # for ext, count in file_extension_counts.items():
        #     return print(f"{ext}: {count} file")

    def create_document(self, dataframe=True):
        documents = []
        extension_lists = self.prepare_filetype()
        self.count_files(extension_lists)
        
        upload_functions = {
            ".docx": self.upload_docx,
            ".pdf": self.upload_pdf,
            ".html": self.upload_html,
            ".png": self.upload_png_ocr,
            ".pptx": self.upload_pptx,
            ".txt": self.upload_txt,
        }

        for extension, upload_function in upload_functions.items():
            if len(extension_lists[extension]) > 0:
                if extension == ".xlsx" or extension == ".csv":
                    data = upload_function(extension_lists, dataframe)
                else:
                    data = upload_function(extension_lists)
                documents.extend(data)
    
        return documents
    
def split_docs(documents,chunk_size=500):
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=100)
    sp_docs = text_splitter.split_documents(documents)
    return sp_docs

@st.cache_resource
def load_llama2_llamaCpp():
    core_model_name = "llama-2-7b-chat.Q4_0.gguf"
    #n_gpu_layers = 32
    n_batch = 32
    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 = 1024, temperature = 0.1, max_tokens = 256
    )
    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}

    Only returns the helpful answer below and nothing else.
    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():
    # msgs = StreamlitChatMessageHistory(key="langchain_messages")
    # print(msgs)
    # if "messages" not in st.session_state:
    #     st.session_state.messages = []

    # DB_FAISS_UPLOAD_PATH = "vectorstores/db_faiss"
    st.header("DOCUMENT QUESTION ANSWERING IS2")
    # directory = "data"
    # data_dir = UploadDoc(directory).create_document()
    # data.extend(data_dir)

    # #create vector from upload 
    # if len(data) > 0 :
    #     sp_docs = split_docs(documents = data)
    #     st.write(f"This document have {len(sp_docs)} chunks")
    #     embeddings = load_embeddings()
    #     with st.spinner('Wait for create vector'):
    #         db = FAISS.from_documents(sp_docs, embeddings)
    #         # db.save_local(DB_FAISS_UPLOAD_PATH)
    #         # st.write(f"Your model is already store in {DB_FAISS_UPLOAD_PATH}")

    llm = load_llama2_llamaCpp()
    qa_prompt = set_custom_prompt()
    #memory = ConversationBufferWindowMemory(k = 0, return_messages=True,  input_key= 'question', output_key='answer', memory_key="chat_history")
    #memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
    #doc_chain = load_qa_chain(llm, chain_type="stuff", prompt = qa_prompt)
    #question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)
    embeddings = load_embeddings()

    
    uploaded_file = st.file_uploader('Choose your .pdf file', type="pdf")
    print(uploaded_file)
    if uploaded_file is not None:
        pdf_reader = PdfReader(uploaded_file)
        text = ""
        for page in pdf_reader.pages:
            text += page.extract_text()
        print(text)
        db = FAISS.from_texts(text, 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}) 

        query = st.text_input("ASK ABOUT THE DOCS:")        
        if query:
            start = time.time()
            response = qa_chain({'query': query})
            st.write(response["result"])
            end = time.time()
            st.write("Respone time:",int(end-start),"sec")
    
        # qa_chain = ConversationalRetrievalChain(
        #     retriever =db.as_retriever(search_kwargs={'k':2}), 
        #     question_generator=question_generator,
        #     #condense_question_prompt=CONDENSE_QUESTION_PROMPT,
        #     combine_docs_chain=doc_chain,
        #     return_source_documents=True,
        #     memory = memory,
        #     #get_chat_history=lambda h :h
        # )

        # 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})

        #     # url_list = set([i.metadata['source']  for i in response['source_documents']])
        #     #print(f"condensed quesion : {question_generator.run({'chat_history': response['chat_history'], 'question' : 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(url_list):
        #     #         #url_reg = regex_source(url)
        #     #         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() 
            
  
if __name__ == '__main__':
	main()