TanDooriCodEs/FAQ-Agentic-RAG-ChatBot

1import os2from pathlib import Path3import streamlit as st4import google.generativeai as genai5from google.generativeai.types import HarmCategory, HarmBlockThreshold6 7is not os.path.exists("data"):8    os.mkdir("data")9 10# Configure Google API11api_key = os.getenv("GOOGLE_API_KEY")12genai.configure(api_key=api_key)13 14# Initialize Gemini Pro model15model = genai.GenerativeModel('gemini-pro')16 17# Function to load FAQ from a text file18@st.cache_data19def load_faq(file_path):20    faq = {}21    with open(file_path, 'r') as f:22        content = f.read().split('\n\n')23    for item in content:24        if '?' in item:25            question, answer = item.split('\n', 1)26            faq[question.strip()] = answer.strip()27    return faq28 29# Function to format FAQ for display30def format_faq_for_display(faq):31    formatted_faq = ""32    for question, answer in faq.items():33        formatted_faq += f"**Q: {question}**\n\n{answer}\n\n---\n\n"34    return formatted_faq35 36# Function to generate response using Gemini Pro37def generate_response(query, context):38    prompt = f"""39    Context: {context}40 41    Human: {query}42 43    Assistant: Analyze the human's input and respond accordingly:44    1. If it's a question that cannot be answered based on the given context, respond exactly with: 'cannot response'45    2. If it's a greeting (e.g., "Hi", "Hello"), respond with: "Hello! How may I help you today?"46    3. If it's a short affirmation or acknowledgment (e.g., "Nice", "Okay", "Thanks"), respond with: "I'm glad I could assist you. Is there anything else you'd like to know?"47    4. If it's a question that can be answered based on the context, provide a concise and relevant answer.48    5. If it's any other respose then answer with: "I'm here to help. What would you like to know?"49    """50    51    response = model.generate_content(prompt)52    return response.text53 54# Function to answer questions55def answer_question(user_query, faq):56    # Combine all FAQ content for context57    context = "\n".join([f"Q: {q}\nA: {a}" for q, a in faq.items()])58    59    # Generate response using Gemini Pro60    answer = generate_response(user_query, context)61    62    if "don't have enough information" in answer.lower():63        return None64    return answer65 66# WhatsApp fallback function67def whatsapp_fallback(query):68    whatsapp_link = "https://wa.link/m8ghvf"  # Replace with your actual WhatsApp Link69    return f"I not able to answer your question: '{query}'. Please contact our support via WhatsApp link: {whatsapp_link}."70 71# Streamlit app72def main():73    st.title("FAQ Chatbot")74 75    # Load FAQ76    faq_path = Path('data/FAQ.txt')77    if not faq_path.exists():78        st.error(f"Error: FAQ file not found at {faq_path}. Please make sure the file exists.")79        return80 81    faq = load_faq(faq_path)82 83    # Display FAQ in sidebar84    st.sidebar.title("Frequently Asked Questions")85    formatted_faq = format_faq_for_display(faq)86    st.sidebar.markdown(formatted_faq)87 88    # Initialize chat history89    if "messages" not in st.session_state:90        st.session_state.messages = []91 92    # Display chat messages from history on app rerun93    for message in st.session_state.messages:94        with st.chat_message(message["role"]):95            st.markdown(message["content"])96 97    # React to user input98    if prompt := st.chat_input("What is your question?"):99        # Display user message in chat message container100        st.chat_message("user").markdown(prompt)101        # Add user message to chat history102        st.session_state.messages.append({"role": "user", "content": prompt})103 104        response = answer_question(prompt, faq)105        106        if response != "cannot response":107            # Display assistant response in chat message container108            with st.chat_message("assistant"):109                st.markdown(response)110            # Add assistant response to chat history111            st.session_state.messages.append({"role": "assistant", "content": response})112        else:113            fallback_message = whatsapp_fallback(prompt)114            # Display fallback message in chat message container115            with st.chat_message("assistant"):116                st.markdown(fallback_message)117            # Add fallback message to chat history118            st.session_state.messages.append({"role": "assistant", "content": fallback_message})119 120if __name__ == "__main__":121    main()