app.py

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
from tempfile import NamedTemporaryFile
def get_pdf_text(pdf_docs):
    # text = ''
    # pdf_file_ = open(pdf_docs,'rb')
    # text = "example hofjin"


    # for page in pdf_reader.pages:
    #     text += page.extract_text()

    # return text
    with NamedTemporaryFile() as temp_file:
        temp_file.write(pdf_docs.getvalue())
        temp_file.seek(0)
        pdf_loader = PyPDFLoader(temp_file.name)
        # print('pdf_loader = ', pdf_loader)
        pdf_doc = pdf_loader.load()
        # print('pdf_doc = ',pdf_doc)
        return pdf_doc


def get_text_file(docs):

    with NamedTemporaryFile() as temp_file:
        temp_file.write(docs.getvalue())
        temp_file.seek(0)
        text_loader = TextLoader(temp_file.name)
        text_doc = text_loader.load()

        return text_doc



def get_csv_file(docs):
    # import pandas as pd
    # text = ''

    # data = pd.read_csv(docs)

    # for index, row in data.iterrows():
    #     item_name = row[0]
    #     row_text = item_name
    #     for col_name in data.columns[1:]:
    #         row_text += '{} is {} '.format(col_name, row[col_name])
    #     text += row_text + '\n'

    # return text

    with NamedTemporaryFile() as temp_file:
        temp_file.write(docs.getvalue())
        temp_file.seek(0)
        text_loader = CSVLoader(temp_file.name)
        text_doc = text_loader.load()

        return text_doc


def get_json_file(docs):
    with NamedTemporaryFile() as temp_file:
        temp_file.write(docs.getvalue())
        temp_file.seek(0)
        json_loader = JSONLoader(temp_file.name,
                                jq_schema = '.scans[].relationships',
                                text_content= False)
        json_doc = json_loader.load()

        return json_doc

def get_hwp_file(docs):
    pass

def get_docs_file(docs):
    pass





def get_text_chunks(documents):
    
    text_splitter = RecursiveCharacterTextSplitter(
        chunk_size = 1000,
        chunk_overlap = 200,
        length_function= len
    )
    # text_splitter = CharacterTextSplitter(
    #     separator="\n",
    #     chunk_size=10f00,
    #     chunk_overlap=200,
    #     length_function=len
    # )
    documents = text_splitter.split_documents(documents)
    print('documents = ', documents)
    return documents


def get_vectorstore(text_chunks):
    # Load the desired embeddings model.
    embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L12-v2',
                                       model_kwargs={'device': 'cpu'})
    print('embeddings = ', embeddings)
    # embeddings = OpenAIEmbeddings()sentence-transformers/all-MiniLM-L6-v2
    # embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl",
    #                                           model_kwargs={'device':'cpu'})
    vectorstore = FAISS.from_documents(text_chunks, embeddings)
    # vectorstore = Chroma.from_texts(texts=text_chunks, embedding=embeddings)

    return vectorstore


def get_conversation_chain(vectorstore):
    
    model_path = 'llama-2-7b-chat.Q2_K.gguf'
    # llm = ChatOpenAI()
    # llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature":0.5, "max_length":512})
    config = {'max_new_tokens': 2048}

    
    # llm = CTransformers(model="llama-2-7b-chat.ggmlv3.q2_K.bin", model_type="llama", config=config)

    llm = LlamaCpp(model_path=model_path,
                   n_ctx = 4086,
                   input={"n_ctx":2048,"temperature": 0.75, "max_length": 2000, "top_p": 1},
                   verbose=True, )
    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):
    print('user_question =>  ', 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():
    load_dotenv()
    st.set_page_config(page_title="Chat with multiple PDFs",
                       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 PDFs :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")
        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)


if __name__ == '__main__':
    main()