app.py

import os
import streamlit as st
from dotenv import load_dotenv
from PyPDF2 import PdfReader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.document_loaders import UnstructuredPDFLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.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.llms import HuggingFaceHub
from langchain.vectorstores import Chroma
from gpt4all import GPT4All


# set this key as an environment variable
os.environ["HUGGINGFACEHUB_API_TOKEN"] = st.secrets['huggingface_token']


def add_logo():

    st.markdown(
        f"""
            <style>
                [data-testid="stSidebar"] {{
                    background-image: url(https://smbk.s3.amazonaws.com/media/organization_logos/111579646d1241f4be17bd7394dcb238.jpg);
                    background-repeat: no-repeat;
                    padding-top: 80px;
                    background-position: 20px 20px;
                }}
            </style>
            """,
        unsafe_allow_html=True,
    )





def get_pdf_text(pdf_docs : list) -> str:
    text = ""
    for pdf in pdf_docs:
        pdf_reader = PdfReader(pdf)
        for page in pdf_reader.pages:
            text += page.extract_text()
    return text

def get_pdf_pages(pdf_docs):
    """
    Extract text from a list of PDF documents.
    Parameters
    ----------
    pdf_docs : list
        List of PDF documents to extract text from.
    Returns
    -------
    str
        Extracted text from all the PDF documents.
    """
    pages = []
    import tempfile

    with tempfile.TemporaryDirectory() as tmpdirname:
        for pdf in pdf_docs:
            pdf_path=os.path.join(tmpdirname,pdf.name)
            with open(pdf_path, "wb") as f:
               f.write(pdf.getbuffer())
        
            pdf_loader = UnstructuredPDFLoader(pdf_path)
            pdf_pages = pdf_loader.load_and_split()
            pages=pages+pdf_pages
    return pages

    
#def get_text_chunks(text:str) ->list:
#    text_splitter = CharacterTextSplitter(
#        separator="\n", chunk_size=1500, chunk_overlap=300, length_function=len
#    )
#    chunks = text_splitter.split_text(text)
#    return chunks

def get_text_chunks(pages):
    """
    Split the input text into chunks.
    Parameters
    ----------
    text : str
        The input text to be split.
    Returns
    -------
    list
        List of text chunks.
    """
    text_splitter = RecursiveCharacterTextSplitter(
        chunk_size=1024, chunk_overlap=64
    )
    texts = text_splitter.split_documents(pages)
    print(str(len(texts)))
    return texts




#def get_vectorstore(text_chunks : list) -> FAISS:
#    model = "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"
#    encode_kwargs = {
#        "normalize_embeddings": True
#    }  # set True to compute cosine similarity
#    embeddings = HuggingFaceBgeEmbeddings(
#        model_name=model, encode_kwargs=encode_kwargs, model_kwargs={"device": "cpu"}
#    )
#    vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
#    return vectorstore


def get_vectorstore(text_chunks):
    """
    Generate a vector store from a list of text chunks using HuggingFace BgeEmbeddings.
    Parameters
    ----------
    text_chunks : list
        List of text chunks to be embedded.
    Returns
    -------
    FAISS
        A FAISS vector store containing the embeddings of the text chunks.
    """
    MODEL_NAME = "WhereIsAI/UAE-Large-V1"
    MODEL_NAME = "sentence-transformers/all-MiniLM-L6-v2"
    MODEL_NAME = "avsolatorio/GIST-Embedding-v0"
    
    hf_embeddings = HuggingFaceEmbeddings(model_name=MODEL_NAME)
    vectorstore = Chroma.from_documents(text_chunks, hf_embeddings, persist_directory="db")
    return vectorstore




def get_conversation_chain(vectorstore:FAISS) -> ConversationalRetrievalChain:
    # llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613")
    #llm = HuggingFaceHub(
    #    repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1",
    #    #repo_id="clibrain/lince-mistral-7b-it-es",
    #    #repo_id="TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF"
    #    model_kwargs={"temperature": 0.5, "max_length": 2096},#1048
    #)
    llm = HuggingFaceHub(
        repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1",
        model_kwargs={"temperature": 0.5, "max_new_tokens": 1024, "max_length": 1048, "top_k": 3, "trust_remote_code": True, "torch_dtype": "auto"},
    )
    
 


    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:str):
#    response = st.session_state.conversation({"pregunta": 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("   Usuario: " + message.content)
 #       else:
#            st.write("🤖 ChatBot: " + message.content)


def handle_userinput(user_question):
    """
    Handle user input and generate a response using the conversational retrieval chain.
    Parameters
    ----------
    user_question : str
        The user's 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: " + message.content)
        else:
            st.write("🤖 ChatBot: " + message.content)




def main():
    st.set_page_config(
        page_title="Chat with a Bot that tries to answer questions about multiple PDFs",
        page_icon=":books:",
    )

    #st.markdown("# Charla con TedCasBot")
    #st.markdown("Este Bot será tu aliado a la hora de buscar información en múltiples documentos pdf. Déjanos ayudarte! 🙏🏾")
    st.markdown("# Chat with TedCasBot")
    st.markdown("This Bot is a powerful AI tool designed to simplify the process of extracting information from PDF documents")

    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("Charla con un Bot 🤖🦾 que te ayudará a responder preguntas sobre tus pdfs:")
    st.header("Chat with the TedCasBot. He will help you with any doubt you may have with your documents:")
    
    user_question = st.text_input("Ask what you need!:")
    if user_question:
        handle_userinput(user_question)

    
    with st.sidebar:
        add_logo()
        st.subheader("Your documents")
        pdf_docs = st.file_uploader(
            "Upload your documents and ress 'Process'", accept_multiple_files=True
        )
        if st.button("Process"):
            with st.spinner("Processing"):
                # get pdf text
                raw_text = get_pdf_text(pdf_docs)
                pages = get_pdf_pages(pdf_docs)
                
                # get the text chunks
                #text_chunks = get_text_chunks(raw_text)
                text_chunks = get_text_chunks(pages)
                # create vector store
                vectorstore = get_vectorstore(text_chunks)

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


if __name__ == "__main__":
    main()