How to use ChatGPT for Hostess / Host of Lounge, Restaurant, or Coffee Shop Job Role

python

import json

# Sample data file with existing reservations
reservations_file = 'reservations.json'

# Load existing reservations from the file
try:
    with open(reservations_file, 'r') as file:
        reservations = json.load(file)
except FileNotFoundError:
    reservations = {}

# Function to add a reservation
def add_reservation(name, date, time, party_size):
    reservation_id = len(reservations) + 1
    reservations[reservation_id] = {'name': name, 'date': date, 'time': time, 'party_size': party_size}
    save_reservations()
    print(f'Reservation for {name} added successfully!')

# Function to view all reservations
def view_reservations():
    for res_id, details in reservations.items():
        print(f'Reservation ID: {res_id}, Name: {details['name']}, Date: {details['date']}, Time: {details['time']}, Party Size: {details['party_size']}')

# Function to delete a reservation
def delete_reservation(reservation_id):
    if reservation_id in reservations:
        del reservations[reservation_id]
        save_reservations()
        print(f'Reservation ID {reservation_id} deleted successfully!')
    else:
        print('Reservation ID not found.')

# Function to save reservations to the file
def save_reservations():
    with open(reservations_file, 'w') as file:
        json.dump(reservations, file)

# Example usage
add_reservation('John Doe', '2023-10-15', '19:00', 4)
view_reservations()
delete_reservation(1)
view_reservations()

python

import pandas as pd

# Load the customer feedback CSV file
feedback_df = pd.read_csv('customer_feedback.csv')

python

# Display the first few rows of the dataframe
print(feedback_df.head())

# Check for missing values
print(feedback_df.isnull().sum())

python

# Example of summarizing feedback
positive_feedback = feedback_df[feedback_df['sentiment'] == 'positive']
negative_feedback = feedback_df[feedback_df['sentiment'] == 'negative']

print(f'Total Positive Feedback: {len(positive_feedback)}')
print(f'Total Negative Feedback: {len(negative_feedback)}')

# Display common themes
common_themes = feedback_df['comments'].str.split(expand=True).stack().value_counts()
print('Most Common Themes in Feedback:')
print(common_themes.head(10))