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MYRAG

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import streamlit as st
from dotenv import load_dotenv
from PyPDF2 import PdfReader
from langchain.text_splitter import CharacterTextSplitter, RecursiveCharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings, HuggingFaceInstructEmbeddings
from langchain.vectorstores import FAISS, Chroma
from langchain.embeddings import HuggingFaceEmbeddings  # General embeddings from HuggingFace models.
from langchain.chat_models import ChatOpenAI
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from htmlTemplates import css, bot_template, user_template
from langchain.llms import HuggingFaceHub, LlamaCpp, CTransformers  # For loading transformer models.
from langchain.document_loaders import PyPDFLoader, TextLoader, JSONLoader, CSVLoader
import tempfile # 임시 파일을 생성하기 위한 라이브러리입니다.
import os


# PDF 문서로부터 텍스트를 추출하는 함수입니다.
def get_pdf_text(pdf_docs):
    temp_dir = tempfile.TemporaryDirectory() # 임시 디렉토리를 생성합니다.
    temp_filepath = os.path.join(temp_dir.name, pdf_docs.name) # 임시 파일 경로를 생성합니다.
    with open(temp_filepath, "wb") as f:  # 임시 파일을 바이너리 쓰기 모드로 엽니다.
        f.write(pdf_docs.getvalue()) # PDF 문서의 내용을 임시 파일에 씁니다.
    pdf_loader = PyPDFLoader(temp_filepath) # PyPDFLoader를 사용해 PDF를 로드합니다.
    pdf_doc = pdf_loader.load() # 텍스트를 추출합니다.
    return pdf_doc # 추출한 텍스트를 반환합니다.

# 과제
# 아래 텍스트 추출 함수를 작성

def get_text_file(text_docs):
    temp_dir = tempfile.TemporaryDirectory()
    temp_filepath = os.path.join(temp_dir.name, text_docs.name)
    with open(temp_filepath, "wb") as f:
        f.write(text_docs.getvalue())
    text_loader = TextLoader(temp_filepath)
    text_doc = text_loader.load()
    return text_doc


def get_csv_file(csv_docs):
    temp_dir = tempfile.TemporaryDirectory()
    temp_filepath = os.path.join(temp_dir.name, csv_docs.name)
    with open(temp_filepath, "wb") as f:
        f.write(csv_docs.getvalue())
    csv_loader = CSVLoader(temp_filepath)
    csv_doc = csv_loader.load()
    return csv_doc

def get_json_file(json_docs):
    temp_dir = tempfile.TemporaryDirectory()
    temp_filepath = os.path.join(temp_dir.name, json_docs.name)
    with open(temp_filepath, "wb") as f:
        f.write(json_docs.getvalue())
    json_loader = JSONLoader(temp_filepath)
    json_doc = json_loader.load()
    return json_doc

    
# 문서들을 처리하여 텍스트 청크로 나누는 함수입니다.
def get_text_chunks(documents):
    text_splitter = RecursiveCharacterTextSplitter(
        chunk_size=1000, # 청크의 크기를 지정합니다.
        chunk_overlap=200, # 청크 사이의 중복을 지정합니다.
        length_function=len # 텍스트의 길이를 측정하는 함수를 지정합니다.
    )

    documents = text_splitter.split_documents(documents) # 문서들을 청크로 나눕니다
    return documents # 나눈 청크를 반환합니다.


# 텍스트 청크들로부터 벡터 스토어를 생성하는 함수입니다.
def get_vectorstore(text_chunks):
    # OpenAI 임베딩 모델을 로드합니다. (Embedding models - Ada v2)

    # embeddings = OpenAIEmbeddings()
    # embeddings=HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")

    vectorstore = FAISS.from_documents(text_chunks, embeddings) # FAISS 벡터 스토어를 생성합니다.

    return vectorstore # 생성된 벡터 스토어를 반환합니다.


def get_conversation_chain(vectorstore):
    llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature":0.5, "max_length":512},huggingfacehub_api_token=os.getenv("TOKEN_API2"))
    
    # 대화 기록을 저장하기 위한 메모리를 생성합니다.
    memory = ConversationBufferMemory(
        memory_key='chat_history', return_messages=True)
    # 대화 검색 체인을 생성합니다.
    conversation_chain = ConversationalRetrievalChain.from_llm(
        llm=llm,
        retriever=vectorstore.as_retriever(),
        memory=memory
    )
    return conversation_chain

# 사용자 입력을 처리하는 함수입니다.
# def handle_userinput(user_question):
#     # 대화 체인을 사용하여 사용자 질문에 대한 응답을 생성합니다.
#     response = st.session_state.conversation({'question': user_question})
#     # 대화 기록을 저장합니다.
#     st.session_state.chat_history = response['chat_history']

#     for i, message in enumerate(st.session_state.chat_history):
#         if i % 2 == 0:
#             st.write(user_template.replace(
#                 "{{MSG}}", message.content), unsafe_allow_html=True)
#         else:
#             st.write(bot_template.replace(
#                 "{{MSG}}", message.content), unsafe_allow_html=True)


def handle_userinput(user_question):
    if not st.session_state.conversation:
        st.error("Please upload and process your documents first.")
        return

    try:
        response = st.session_state.conversation({'question': user_question})
        st.session_state.chat_history = response['chat_history']

        for i, message in enumerate(st.session_state.chat_history):
            if i % 2 == 0:
                st.write(user_template.replace(
                    "{{MSG}}", message.content), unsafe_allow_html=True)
            else:
                st.write(bot_template.replace(
                    "{{MSG}}", message.content), unsafe_allow_html=True)
    except Exception as e:
        st.error(f"An error occurred: {e}")


def main():
    load_dotenv()
    st.set_page_config(page_title="Chat with multiple Files",
                       page_icon=":books:")
    st.write(css, unsafe_allow_html=True)

    if "conversation" not in st.session_state:
        st.session_state.conversation = None
    if "chat_history" not in st.session_state:
        st.session_state.chat_history = None

    st.header("Chat with multiple Files:")
    user_question = st.text_input("Ask a question about your documents:")
    if user_question:
        handle_userinput(user_question)

    with st.sidebar:
        openai_key = st.text_input("Paste your OpenAI API key (sk-...)")
        if openai_key:
            os.environ["OPENAI_API_KEY"] = openai_key

        st.subheader("Your documents")
        docs = st.file_uploader(
            "Upload your PDFs here and click on 'Process'", accept_multiple_files=True)
        if st.button("Process"):
            if not docs:
                st.error("Please upload at least one document.")
                return

            with st.spinner("Processing..."):
                try:
                    doc_list = []

                    for file in docs:
                        if file.type == 'text/plain':
                            doc_list.extend(get_text_file(file))
                        elif file.type in ['application/octet-stream', 'application/pdf']:
                            doc_list.extend(get_pdf_text(file))
                        elif file.type == 'text/csv':
                            doc_list.extend(get_csv_file(file))
                        elif file.type == 'application/json':
                            doc_list.extend(get_json_file(file))

                    if not doc_list:
                        st.error("No valid documents processed. Please check your files.")
                        return

                    text_chunks = get_text_chunks(doc_list)

                    vectorstore = get_vectorstore(text_chunks)

                    st.session_state.conversation = get_conversation_chain(vectorstore)

                    st.success("Documents processed successfully!")
                except Exception as e:
                    st.error(f"An error occurred during processing: {e}")


if __name__ == '__main__':
    main()
# def main():
#     load_dotenv()
#     st.set_page_config(page_title="Chat with multiple Files",
#                        page_icon=":books:")
#     st.write(css, unsafe_allow_html=True)

#     if "conversation" not in st.session_state:
#         st.session_state.conversation = None
#     if "chat_history" not in st.session_state:
#         st.session_state.chat_history = None

#     st.header("Chat with multiple Files :")
#     user_question = st.text_input("Ask a question about your documents:")
#     if user_question:
#         handle_userinput(user_question)

#     with st.sidebar:
#         openai_key = st.text_input("Paste your OpenAI API key (sk-...)")
#         if openai_key:
#             os.environ["OPENAI_API_KEY"] = openai_key

#         st.subheader("Your documents")
#         docs = st.file_uploader(
#             "Upload your PDFs here and click on 'Process'", accept_multiple_files=True)
#         if st.button("Process"):
#             with st.spinner("Processing"):
#                 # get pdf text
#                 doc_list = []

#                 for file in docs:
#                     print('file - type : ', file.type)
#                     if file.type == 'text/plain':
#                         # file is .txt
#                         doc_list.extend(get_text_file(file))
#                     elif file.type in ['application/octet-stream', 'application/pdf']:
#                         # file is .pdf
#                         doc_list.extend(get_pdf_text(file))
#                     elif file.type == 'text/csv':
#                         # file is .csv
#                         doc_list.extend(get_csv_file(file))
#                     elif file.type == 'application/json':
#                         # file is .json
#                         doc_list.extend(get_json_file(file))

#                 # get the text chunks
#                 text_chunks = get_text_chunks(doc_list)

#                 # create vector store
#                 vectorstore = get_vectorstore(text_chunks)

#                 # create conversation chain
#                 st.session_state.conversation = get_conversation_chain(
#                     vectorstore)


# import streamlit as st
# # from dotenv import load_dotenv
# from PyPDF2 import PdfReader
# from langchain.text_splitter import CharacterTextSplitter
# from langchain_community.embeddings import HuggingFaceInstructEmbeddings
# from langchain_community.vectorstores import FAISS
# # from langchain.chat_models import ChatOpenAI
# from langchain.memory import ConversationBufferMemory
# from langchain.chains import ConversationalRetrievalChain
# from htmlTemplates import css, bot_template, user_template
# from langchain_community.llms import HuggingFaceHub
# import os
# # from sentence_transformers import SentenceTransformer
# from langchain.embeddings import HuggingFaceEmbeddings


# # from huggingface_hub import login

# # Retrieve the Hugging Face token from environment variables
# # token = os.getenv("HUGGINGFACEHUB_TOKEN") 
# import fitz  # PyMuPDF

# def get_pdf_text(pdf_docs):
#     text = ""
#     for pdf in pdf_docs:
#         try:
#             doc = fitz.open(stream=pdf.read(), filetype="pdf")
#             for page in doc:
#                 text += page.get_text()
#         except Exception as e:
#             st.error(f"Could not read the file: {pdf.name}. Error: {e}")
#     return text
# # def get_pdf_text(pdf_docs):
# #   text = ""
# #   for pdf in pdf_docs:
# #     pdf_reader = PdfReader(pdf)
# #     for page in pdf_reader.pages:
# #       text += page.extract_text()
# #   return text

# def get_text_chunks(text):
#   text_splitter=CharacterTextSplitter(
#       separator="\n",
#       chunk_size=1000,
#       chunk_overlap=200,
#       length_function=len
#   )
#   chunks=text_splitter.split_text(text)
#   return chunks

# # token="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1"
# # def get_vectorstore(text_chunks):
# #   # embeddings=HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl",huggingfacehub_token=os.getenv("TOKEN_API2"))
# #   embeddings=HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
# #   vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
# #   return vectorstore

# # def get_vectorstore(text_chunks):
# #     # Load a SentenceTransformer model for embeddings
# #     embedding_model = SentenceTransformer("hkunlp/instructor-xl")  # Replace with a model of your choice
# #     embeddings = [embedding_model.encode(chunk) for chunk in text_chunks]

# #     # Create a FAISS vectorstore
# #     vectorstore = FAISS.from_embeddings(embeddings=embeddings, texts=text_chunks)
# #     return vectorstore

# def get_vectorstore(text_chunks):
#   embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
#   vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
#   return vectorstore
    
# def get_conversation_chain(vectorstore):
#   llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature":0.5, "max_length":512},huggingfacehub_api_token=os.getenv("TOKEN_API2"))
#   memory=ConversationBufferMemory(
#       memory_key='chat_history',return_messages=True)
#   conversation_chain = ConversationalRetrievalChain.from_llm(
#       llm=llm,
#       retriever=vectorstore.as_retriever(),
#       memory=memory
#   )
#   return conversation_chain

# def handle_userinput(user_question):
#   response = st.session_state.conversation({'question':user_question})
#   st.session_state.chat_history = response['chat_history']

#   for i, message in enumerate(st.session_state.chat_history):
#     if i % 2 == 0:
#       st.write(user_template.replace("{{MSG}}", message.content),unsafe_allow_html=True)
#     else:
#       st.write(bot_template.replace("{{MSG}}", message.content),unsafe_allow_html=True)

# def main():
#   st.set_page_config(page_title="Chat with My RAG",
#   page_icon=":books:")
#   st.write(css,unsafe_allow_html=True)

#   if "conversation" not in st.session_state:
#     st.session_state.conversation = None
#   if "chat_history" not in st.session_state:
#     st.session_state.chat_history = None

#   st.header("Chat with My RAG :books:")
#   user_question=st.text_input("Ask a question about your documents:")
#   if user_question:
#     handle_userinput(user_question)

#   with st.sidebar:
#     st.subheader("Your Documents")
#     pdf_docs = st.file_uploader("Upload your PDFs here and click on 'Process'", accept_multiple_files=True)
#     if st.button("Process"):
#       with st.spinner("Processing"):
#         raw_text =get_pdf_text(pdf_docs)

#         text_chunks = get_text_chunks(raw_text)

#         vectorstore = get_vectorstore(text_chunks)

#         st.session_state.conversation = get_conversation_chain(vectorstore)


# if __name__ == '__main__':
#   main()