import streamlit as st
from langchain_google_genai import ChatGoogleGenerativeAI
from transformers import pipeline
import requests
import streamlit_authenticator as stauth

# Initialize the generative AI model
llm = ChatGoogleGenerativeAI(model="gemini-pro", google_api_key=st.secrets["GOOGLE_API_KEY"])

# Load a pre-trained intent classification model
classifier = pipeline("text-classification", model="distilbert-base-uncased")

# Initialize session state
if "conversation_history" not in st.session_state:
    st.session_state.conversation_history = []

if "feedback" not in st.session_state:
    st.session_state.feedback = []

# Define user authentication
users = stauth.Authenticate(
    names=['John Doe', 'Jane Smith'],
    usernames=['johndoe', 'janesmith'],
    passwords=['password1', 'password2'],
    cookie_name='chat_auth',
    key='abcdef'
)

# Function to handle user queries and generate responses
def generate_response(user_query):
    prompt = f"User: {user_query}"
    answers = llm.invoke(prompt)
    return answers.content

# Function to classify user intents using NLP
def classify_intent(user_query):
    result = classifier(user_query)[0]
    label = result['label']
    
    intents = {
        "greeting": ["LABEL_0"],
        "thanks": ["LABEL_1"],
        "goodbye": ["LABEL_2"],
        "help": ["LABEL_3"],
        "custom_query": ["LABEL_4"]
    }
    
    for intent, labels in intents.items():
        if label in labels:
            return intent
    return "unknown"

# Function to update conversation history
def update_conversation_history(user_query, bot_response):
    st.session_state.conversation_history.append(f"You: {user_query}")
    st.session_state.conversation_history.append(f"Bot: {bot_response}")

# Function to display conversation history
def display_conversation_history():
    for message in st.session_state.conversation_history:
        st.write(message)

# Function to handle feedback
def handle_feedback(feedback):
    st.session_state.feedback.append(feedback)
    st.write("Thank you for your feedback!")

# Function to integrate with CRM system (pseudo-code)
def integrate_with_crm(user_query, bot_response):
    crm_endpoint = "https://crm.example.com/api/record_interaction"
    data = {
        "user_query": user_query,
        "bot_response": bot_response
    }
    response = requests.post(crm_endpoint, json=data)
    return response.status_code

# Main Streamlit app
def main():
    st.sidebar.title("Customer Support Chatbot")
    
    # User authentication
    name, authentication_status, username = users.login('Login', 'main')
    
    if authentication_status:
        st.title(f"Hello, {name}!")
        
        display_conversation_history()

        user_input = st.text_input("You:")
        submit_button = st.button("Send")

        if submit_button:
            if user_input:
                intent = classify_intent(user_input)
                
                if intent == "greeting":
                    bot_response = "Hello! How can I help you today?"
                elif intent == "thanks":
                    bot_response = "You're welcome!"
                elif intent == "goodbye":
                    bot_response = "Goodbye! Have a great day."
                elif intent == "help":
                    bot_response = "I can assist you with account issues, billing questions, product support, and technical issues. Please specify your query."
                elif intent == "custom_query":
                    bot_response = generate_response(user_input)
                else:
                    bot_response = "I'm sorry, I didn't understand that. How can I assist you?"

                update_conversation_history(user_input, bot_response)
                display_conversation_history()
                
                # Integrate with CRM
                integrate_with_crm(user_input, bot_response)
                
                user_input = ""
    
        feedback = st.text_input("Provide Feedback:")
        feedback_button = st.button("Submit Feedback")

        if feedback_button:
            if feedback:
                handle_feedback(feedback)
                st.text_input("Provide Feedback:", value="", key="feedback_clear")

    elif authentication_status == False:
        st.error('Username/password is incorrect')
    elif authentication_status == None:
        st.warning('Please enter your username and password')

if __name__ == "__main__":
    main()