app.py

from util import *
from streamlit_option_menu import option_menu
from langchain_core.prompts import ChatPromptTemplate
from langchain_groq import ChatGroq
from dotenv import load_dotenv

# --- PAGE CONFIGURATION ---
st.set_page_config(page_title="Doc Chat", page_icon=":robot_face:", layout="centered")

# --- SETUP SESSION STATE VARIABLES ---
if "vector_store" not in st.session_state:
    st.session_state.vector_store = False
if "response" not in st.session_state:
    st.session_state.response = None
if "prompt_activation" not in st.session_state:
    st.session_state.prompt_activation = False
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
if "prompt" not in st.session_state:
    st.session_state.prompt = False

load_dotenv()

# --- SIDEBAR CONFIGURATION ---
st.sidebar.header('Configuration')
groq_api_key = sidebar_api_key_configuration()
model = sidebar_groq_model_selection()

# --- MAIN PAGE CONFIGURATION ---
st.title("Doc Chat :robot_face:")
st.write("*Interrogate Documents :books:, Ignite Insights: AI at Your Service*")
st.write(':blue[***Powered by Groq AI Inference Technology***]')

# ---- NAVIGATION MENU -----
selected = option_menu(
    menu_title=None,
    options=["Doc Chat", "Reference", "About"],
    icons=["robot", "bi-file-text-fill", "app"],  # https://icons.getbootstrap.com
    orientation="horizontal",
)

llm = ChatGroq(groq_api_key=groq_api_key, model_name=model)

prompt = ChatPromptTemplate.from_template(
    """
    Answer the question based on the provided context only. If question is not within the context, do not try to answer
    and respond that the asked question is out of context or something similar.
    Please provide the most accurate response based on the question.
    <context>
    {context}
    Questions: {input}
    """
)
# ----- SETUP Doc Chat MENU ------
if selected == "Doc Chat":
    st.subheader("Upload PDF(s)")
    pdf_docs = st.file_uploader("Upload your PDFs", type=['pdf'], accept_multiple_files=True,
                                disabled=not st.session_state.prompt_activation, label_visibility='collapsed')
    process = st.button("Process", type="primary", key="process", disabled=not pdf_docs)

    if process:
        with st.spinner("Processing ..."):
            st.session_state.vector_store = create_vectorstore(pdf_docs)
            st.session_state.prompt = True
            st.success('Database is ready')

    st.divider()

    if "messages" not in st.session_state:
        st.session_state["messages"] = [{"role": "assistant", "content": "How can I help you?"}]

    for msg in st.session_state.messages:
        st.chat_message(msg["role"]).write(msg["content"])

    container = st.container(border=True)
    if question := st.chat_input(placeholder='Enter your question related to uploaded document',
                                 disabled=not st.session_state.prompt):
        st.session_state.messages.append({"role": "user", "content": question})
        st.chat_message("user").write(question)

        with st.spinner('Processing...'):
            st.session_state.response = get_llm_response(llm, prompt, question)
            st.session_state.messages.append({"role": "assistant", "content": st.session_state.response['answer']})
            st.chat_message("assistant").write(st.session_state.response['answer'])

# ----- SETUP REFERENCE MENU ------
if selected == "Reference":
    st.title("Reference & Context")
    if st.session_state.response is not None:
        for i, doc in enumerate(st.session_state.response["context"]):
            with st.expander(f'Reference # {i + 1}'):
                st.write(doc.page_content)

# ----- SETUP ABOUT MENU ------
if selected == "About":
    with st.expander("About this App"):
        st.markdown(''' This app allows you to chat with your PDF documents. It has following functionality:

    - Allows to chat with multiple PDF documents
    - Support of Groq AI inference technology 
    - Display the response context and document reference

        ''')
    with st.expander("Which Large Language models are supported by this App?"):
        st.markdown(''' This app supports the following LLMs as supported by Groq:

    - Chat Models -- Groq
        - Llama3-8b-8192 
        - Llama3-70b-8192 
        - Mixtral-8x7b-32768
        - Gemma-7b-it
        ''')

    with st.expander("Which library is used for vectorstore?"):
        st.markdown(''' This app supports the FAISS for AI similarity search and vectorstore:
        ''')

    with st.expander("Whom to contact regarding this app?"):
        st.markdown(''' Contact [Sree Narayanan]([email protected])
        ''')