Multi-Document Conversational AI: Chat with PDFs, Word Docs, and Text Files Using Streamlit.

Introduction

import streamlit as stimport hashlibfrom dotenv import load_dotenvfrom PyPDF2 import PdfReaderfrom docx import Document from langchain.text_splitter import CharacterTextSplitterfrom langchain.embeddings import HuggingFaceEmbeddingsfrom langchain.vectorstores import FAISSfrom langchain_groq import ChatGroqfrom langchain.memory import ConversationBufferMemoryfrom langchain.chains import ConversationalRetrievalChainfrom htmlTemplates import css, bot_template, user_templatefrom langchain.llms import HuggingFaceHub

# Hashing Function for Passwordsdef hash_password(password):    return hashlib.sha256(password.encode()).hexdigest()# Authentication Functionsdef register_user(username, password):    if "user_data" not in st.session_state:        st.session_state.user_data = {}            hashed_password = hash_password(password)    st.session_state.user_data[username] = hashed_password    st.success("Registration successful! Please log in.")def authenticate_user(username, password):    if "user_data" not in st.session_state:        st.session_state.user_data = {}    if not username or not password:        st.error("Username and password cannot be empty.")        return False    hashed_password = hash_password(password)    if username in st.session_state.user_data and st.session_state.user_data[username] == hashed_password:        return True    else:        st.error("Invalid username or password.")        return False

# Function to extract text from PDF filesdef 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# Function to extract text from Word documents (.docx)def get_word_text(word_docs):    text = ""    for doc in word_docs:        doc = Document(doc)        for para in doc.paragraphs:            text += para.text + "\n"    return text# Function to extract text from text files (.txt)def get_text_file_text(text_files):    text = ""    for txt in text_files:        text += txt.read().decode("utf-8") + "\n"    return text

# Function to split text into chunks for processingdef 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

# Function to generate a vectorstore from text chunksdef get_vectorstore(text_chunks):    embeddings = HuggingFaceEmbeddings()    vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)    return vectorstore

# Function to get a conversational chain for the chat modeldef get_conversation_chain(vectorstore):    llm = ChatGroq()    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

# Process the conversation and display chat historydef handle_userinput(user_question):    response = st.session_state.conversation({'question': user_question})    st.session_state.chat_history = response['chat_history']        # Loop through the chat history and display messages    for i, message in enumerate(st.session_state.chat_history):        if i % 2 == 0:  # User's input            st.write(f"**🤖❓:** {message.content}")        else:  # Model's response            if message.content.lower().startswith("based on the information"):                answer = message.content[message.content.find('.')+1:].strip()                st.write(f"**🤖:** {answer}")            else:                st.write(f"**🤖:** {message.content}")

def main():    # Initialize session state variables    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    load_dotenv()    st.set_page_config(page_title="Chat with Documents", page_icon=":books:")    # Sidebar Login/Register    with st.sidebar:        st.subheader("Login/Register")        choice = st.radio("Select an option", ["Login", "Register"])        username = st.text_input("Username")        password = st.text_input("Password", type="password")        if choice == "Register":            if st.button("Register"):                if username and password:                    register_user(username, password)                else:                    st.warning("Please provide both username and password.")        elif choice == "Login":            if st.button("Login"):                if username and password:                    if authenticate_user(username, password):                        st.success("Login successful!")                        st.session_state.logged_in_user = username                    else:                        st.error("Invalid username or password.")                else:                    st.warning("Please provide both username and password.")    # Main app after login    if "logged_in_user" in st.session_state:        st.header(f"Welcome, {st.session_state.logged_in_user}!")        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")            uploaded_files = st.file_uploader(                "Upload your PDFs, Word docs, and text files here", accept_multiple_files=True            )            if st.button("Process"):                with st.spinner("Processing..."):                    raw_text = ""                    pdf_files = [file for file in uploaded_files if file.type == "application/pdf"]                    word_files = [file for file in uploaded_files if file.type == "application/vnd.openxmlformats-officedocument.wordprocessingml.document"]                    text_files = [file for file in uploaded_files if file.type == "text/plain"]                    if pdf_files:                        raw_text += get_pdf_text(pdf_files)                    if word_files:                        raw_text += get_word_text(word_files)                    if text_files:                        raw_text += get_text_file_text(text_files)                    text_chunks = get_text_chunks(raw_text)                    vectorstore = get_vectorstore(text_chunks)                    st.session_state.conversation = get_conversation_chain(vectorstore)if __name__ == "__main__":    main()

Note: This is a personal project but for a business, the app should have the ability to store data from user chat history.