import re
from nltk.tokenize import word_tokenize
from django.db.models import Q
from inventory.models import Car  # Import your car-related models
import nltk
# Download required NLTK resources
try:

    nltk.download("punkt")
except ImportError:
    raise ImportError("Ensure nltk is installed by running 'pip install nltk'.")

# Static responses for predefined intents
RESPONSES = {
    "greet": ["Hello! How can I assist you today with Haikal Car Inventory?"],
    "inventory_check": ["You can check the car inventory in the Cars section. Do you want to search for a specific car?"],
    "car_status": ["If a car is sold, it will either be marked as SOLD or removed from the inventory, as per your preferences."],
    "sell_process": ["To sell a car, the process involves creating a Sell Order, adding the customer, confirming payment, generating an invoice, and finally delivering the car."],
    "transfer_process": ["Dealers can transfer cars to other branches or dealers for display or sale. This is handled through Sell Orders as well."],
    "bye": ["Goodbye! If you need further assistance, just ask!"],
}

def clean_input(user_input):
    """
    Clean and tokenize user input.
    """
    user_input = user_input.lower()
    user_input = re.sub(r"[^\w\s]", "", user_input)  # Remove punctuation
    return word_tokenize(user_input)

def classify_input(tokens):
    """
    Classify user intent based on tokens.
    """
    if any(word in tokens for word in ["hello", "hi", "hey"]):
        return "greet"
    elif any(word in tokens for word in ["inventory", "cars", "check"]):
        return "inventory_check"
    elif any(word in tokens for word in ["sell", "sold", "process"]):
        return "sell_process"
    elif any(word in tokens for word in ["transfer", "branch", "display"]):
        return "transfer_process"
    elif any(word in tokens for word in ["bye", "goodbye", "exit"]):
        return "bye"
    elif any(word in tokens for word in ["price", "cost", "value"]):
        return "car_price"
    else:
        return "unknown"

def get_dynamic_response(intent, tokens):
    """
    Generate dynamic responses by querying the database.
    """
    if intent == "car_price":
        # Extract car name from tokens
        car_name = " ".join([word.capitalize() for word in tokens])
        try:
            car = Car.objects.filter(Q(make__icontains=car_name) | Q(model__icontains=car_name)).first()
            if car:
                return f"The price of {car_name} is {car.finance.selling_price}."
            return f"Sorry, no car matching '{car_name}' was found in the inventory."
        except Exception as e:
            return f"An error occurred while retrieving the car price: {str(e)}"
    return None

def get_response(user_input):
    """
    Generate a response based on the user's input.
    """
    tokens = clean_input(user_input)
    intent = classify_input(tokens)

    # Check for a dynamic response
    dynamic_response = get_dynamic_response(intent, tokens)
    if dynamic_response:
        return dynamic_response

    # Return a static response if available
    if intent in RESPONSES:
        return RESPONSES[intent][0]

    # Default response for unknown intents
    return "I'm sorry, I didn't understand that. Could you rephrase your question about the Haikal system?"