app.py

import spacy
import streamlit as st
import re
import random
import json

# Load the trained model
nlp = spacy.load("en_pipeline")

# Load FAQs
with open("faq.json") as f:
    faqs = json.load(f)["faqs"]

start_index = random.randint(0, len(faqs) - 5)
end_index = start_index + 5

# Define intents and responses
intents_responses = {
    "get_invoice": "Here is a link to download your invoice: [link_to_invoice].",
    "payment_issue": "If you're facing payment issues, please check your payment method and try again.",
    "check_invoice": "Your invoice details are available here: [link_to_invoice_details].",
    "contact_customer_service": "You can reach our customer service at 1-800-123-456 or email support@company.com.",
    "complain": "We have logged your complaint. Our team will review it and get back to you shortly.",
    "review": "Thank you for your review. We value your feedback and will use it to improve our services.",
    "cancel_order": "Your order has been successfully canceled.",
    "check_cancellation_fee": "There is no cancellation fee if you cancel within 24 hours of placing the order.",
    "check_payment_methods": "We accept Visa, MasterCard, American Express, and PayPal.",
    "set_up_shipping_address": "Your shipping address has been updated successfully.",
    "track_order": "Your order is currently in transit and will be delivered by the estimated date.",
    "place_order": "Your order has been placed successfully. You will receive a confirmation email shortly.",
    "check_refund_policy": "Our refund policy states that refunds can be requested within 30 days of purchase.",
    "get_refund": "Your refund has been processed and will be credited to your account within 5-7 business days.",
    "registration_problems": "If you're having trouble registering, please try clearing your browser cache and cookies.",
    "recover_password": "To recover your password, please click the 'Forgot Password' link on the login page.",
    "track_refund": "Your refund is being processed and should be completed within 5-7 business days.",
    "newsletter_subscription": "You have successfully subscribed to our newsletter.",
    "create_account": "Your account has been created successfully. You can now log in using your credentials.",
    "delivery_options": "We offer standard, express, and next-day delivery options.",
    "delete_account": "Your account has been deleted successfully.",
    "delivery_period": "Standard delivery takes 3-5 business days, while express delivery takes 1-2 business days.",
    "edit_account": "Your account details have been updated successfully.",
    "change_order": "Your order has been updated successfully.",
    "switch_account": "You have successfully switched to your new account.",
    "change_shipping_address": "You can change the shipping address in the orders section.",
    "contact_human_agent": "You are now being connected to a human agent. Please hold on."
}


# Greeting and farewell patterns
greeting_patterns = re.compile(r'\b(hi|hello|hey|greetings|good morning|good afternoon|good evening|how are you|hiya|howdy|what’s up|sup|yo)\b', re.IGNORECASE)
farewell_patterns = re.compile(r'\b(bye|thankyou|thanks|okay|goodbye|see you|farewell|take care|later|peace|cheers|goodnight|cya)\b', re.IGNORECASE)

# Set the confidence threshold
CONFIDENCE_THRESHOLD = 0.7

def get_intent(text):
    doc = nlp(text)
    intent = max(doc.cats, key=doc.cats.get)
    confidence = doc.cats[intent]
    return intent, confidence

def get_response(text):
    if greeting_patterns.search(text):
        return "Hello! How can I assist you today?"
    elif farewell_patterns.search(text):
        return "Thank you for chatting. Have a great day!"
    else:
        intent, confidence = get_intent(text)
        # print(intent,confidence)
        if confidence >= CONFIDENCE_THRESHOLD:
            return intents_responses.get(intent, "I'm not sure how to respond to that. Can you please rephrase your question?")
        else:
            return "I'm not quite sure what you're asking. Could you please provide more details or rephrase your question?"



# Initialize session state
if 'chat_history' not in st.session_state:
    st.session_state.chat_history = []
if 'processing' not in st.session_state:
    st.session_state.processing = False

# Streamlit app
st.title("Customer Support Chatbot")
multi = '''
Here are some topics you can ask about:
- Return policy
- Order tracking
- Shipping information
- Payment methods
- Customer support
'''

with st.expander("**Hi, ask me anything related to our policies and services!**"):
    st.markdown(multi)

# CSS styles for chat messages
st.markdown("""
<style>
.user-msg {
    color: #fff;
    padding: 8px 12px;
    border-radius: 10px;
    margin-block: 5px;
    left-margin:auto;
    text-align: right;
}
.bot-msg {
    background-color: #F1F0F0;
    color: #000000;
    padding: 8px 12px;
    border-radius: 10px;
    margin: 5px;
    text-align: left;
    width:fit-content;
}
.chat-container {
    display: flex;
    flex-direction: column;
}
</style>
""", unsafe_allow_html=True)

# Display chat history
st.subheader("Chat History")
chat_container = st.container()

# Create a form to handle user input
with st.form(key='chat_form', clear_on_submit=True):
    user_input = st.text_input("You:", key="user_input")
    submit_button = st.form_submit_button(label='Send')

# Handle user input
if submit_button and user_input and not st.session_state.processing:
    st.session_state.processing = True
    with st.spinner('Processing...'):
        response = get_response(user_input)
        # Append user query and response to chat history
        st.session_state.chat_history.append(("You", user_input))
        st.session_state.chat_history.append(("Bot", response))
    st.session_state.processing = False

st.write("Frequently Asked Questions:")
faq_buttons = [st.button(faq['question'], key=f"faq_{i}") for i, faq in enumerate(faqs[0:5])]

# Checking if any FAQ button is clicked
for i, faq_button in enumerate(faq_buttons):
    if faq_button and not st.session_state.processing:
        st.session_state.processing = True
        user_input = faqs[i]['question']
        with st.spinner('Processing...'):
            response = get_response(user_input)
            # Append user query and response to chat history
            st.session_state.chat_history.append(("You", user_input))
            st.session_state.chat_history.append(("Bot", response))
            # Display chat history in the container
        st.session_state.processing = False

# Display chat history in the container
with chat_container:
    for role, message in st.session_state.chat_history:
        if role == "You":
            st.markdown(f'<div class="chat-container"><div class="user-msg">{message}</div></div>', unsafe_allow_html=True)
        else:
            st.markdown(f'<div class="chat-container"><div class="bot-msg">{message}</div></div>', unsafe_allow_html=True)

# Automatically scroll to the bottom of the chat
st.markdown('<script>window.scrollTo(0,document.body.scrollHeight);</script>', unsafe_allow_html=True)