import nltk
from nltk.tokenize import word_tokenize

def generate_response(user_input):
# Tokenize the user's input
tokens = word_tokenize(user_input)

# Analyze the tokens to determine the context
context = analyze_tokens(tokens)

# Generate a response based on the context
response = generate_response_based_on_context(context)

return response

def analyze_tokens(tokens):
# Use NLTK to analyze the tokens and determine the context
# For example, you could use part-of-speech tagging to determine the parts of speech
# and named entity recognition to identify named entities
context = {}
for token in tokens:
# Use part-of-speech tagging to determine the part of speech
pos_tag = nltk.pos_tag([token])[0][1]
context[token] = pos_tag

return context

def generate_response_based_on_context(context):
# Use the context to generate a response
# For example, you could use a machine learning model to generate a response
# based on the context
response = "Hello! Welcome to Chuck E. Cheese's."
return response

import speech_recognition as sr
import os
from gtts import gTTS
from PIL import Image, ImageDraw
import numpy as np
import cv2
import torch
from typing import Tuple, List
from flask import Flask, request, jsonify
from twilio.rest import Client
from apscheduler.schedulers.background import BackgroundScheduler
from datetime import datetime, timedelta
import logging

Define constant values

VIDEO_WIDTH = 480
VIDEO_HEIGHT = 480
FPS = 24
SPEECH_TIMEOUT = 5
MAX_RETRIES = 2
LOG_FILE = "ms_pacman.log"

Setup logging

logging.basicConfig(
level=logging.INFO,
format="%(asctime)s - %(levelname)s - %(message)s",
handlers=[
logging.FileHandler(LOG_FILE),
logging.StreamHandler()
]
)
logger = logging.getLogger(name)

Initialize Twilio client

account_sid = os.environ.get("TWILIO_ACCOUNT_SID")
auth_token = os.environ.get("TWILIO_AUTH_TOKEN")
client = Client(account_sid, auth_token)

Initialize Flask app

app = Flask(name)

Define type hints for functions

def search_music_playlist(query: str) -> Tuple[str, List[str]]:
"""
Search for music playlist based on user query.

Args:
query (str): User query.

Returns:
Tuple[str, List[str]]: Response message and list of details.
"""
try:
    # Implement music playlist search logic here
    # For demonstration purposes, return a dummy response
    return "Music playlist found", ["Song 1", "Song 2", "Song 3"]
except Exception as e:
    logger.error(f"Error searching music playlist: {str(e)}")
    return "Error searching music playlist", []

def drive_thru_ordering(query: str) -> Tuple[str, List[str]]:
"""
Process drive-thru order based on user query.

Args:
query (str): User query.

Returns:
Tuple[str, List[str]]: Response message and list of details.
"""
try:
    # Implement drive-thru ordering logic here
    # For demonstration purposes, return a dummy response
    return "Order placed successfully", ["Item 1", "Item 2", "Item 3"]
except Exception as e:
    logger.error(f"Error processing drive-thru order: {str(e)}")
    return "Error processing drive-thru order", []

def generate_tts(text: str, filename: str = "response.mp3") -> str:
"""
Generate text-to-speech audio file.

Args:
text (str): Text to convert to speech.
filename (str): Output audio file name.

Returns:
str: Path to generated audio file.
"""
try:
    # Implement text-to-speech logic here
    # For demonstration purposes, use the gTTS library
    tts = gTTS(text=text, lang="en")
    tts.save(filename)
    return filename
except Exception as e:
    logger.error(f"Error generating text-to-speech: {str(e)}")
    return ""

def generate_fallback_video(text: str, duration: int = 5, fps: int = FPS, output: str = "ms_pacman_video.mp4") -> str:
"""
Generate fallback video based on text input.

Args:
text (str): Text to display in video.
duration (int): Video duration in seconds.
fps (int): Video frames per second.
output (str): Output video file name.

Returns:
str: Path to generated video file.
"""
try:
    # Implement fallback video logic here
    # For demonstration purposes, use the OpenCV library
    video = cv2.VideoWriter(output, cv2.VideoWriter_fourcc(*"mp4v"), fps, (VIDEO_WIDTH, VIDEO_HEIGHT))
    for i in range(duration * fps):
        frame = np.zeros((VIDEO_HEIGHT, VIDEO_WIDTH, 3), dtype=np.uint8)
        cv2.putText(frame, text, (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
        video.write(frame)
    video.release()
    return output
except Exception as e:
    logger.error(f"Error generating fallback video: {str(e)}")
    return ""

Add error handling for speech recognition failures

def recognize_speech() -> str:
try:
# Implement speech recognition logic here
# For demonstration purposes, use the speech_recognition library
r = sr.Recognizer()
with sr.Microphone() as source:
audio = r.listen(source)
return r.recognize_google(audio, language="en-US")
except sr.UnknownValueError