First use case

Consider the first scenario as follows:

A travel agency has three customers. For our use case, the requirement is to suggest a package for a specific city using the predefined preferences for any two customers. As an additional output or suggestion, there needs to be a suggestion on weather status for the next five days. Also, to provide an enhanced end user experience, ask a single question to determine check-in time and type of transport for the journey.

The code is as follows:

...
import getpass 
import base64
import requests
from collections import Counter
import re

#ask for username .. will be displayed when typed
uname=input("Enter your username :")

#ask for password ... will not be displayed when typed
#(try in cmd or invoke using python command)
p = getpass.getpass(prompt="Enter your password: ")

#construct credential with *.* as separator between username and password
creds=uname+"*.*"+p

#encrypted creds of the registered customers
#for testing username:password is customer1:password1 , customer2:password2, and so on

#create a dictionary:
customers={
    "customer1":b'Y3VzdG9tZXIxKi4qcGFzc3dvcmQx',
    "customer2":b'Y3VzdG9tZXIyKi4qcGFzc3dvcmQy',
    "customer3":b'Y3VzdG9tZXIzKi4qcGFzc3dvcmQz'
    }

###Decrypt a given set of credentials 
def decryptcredential(pwd):
    rvalue=base64.b64decode(pwd)
    rvalue=rvalue.decode()
    return rvalue
###Encrypt a given set of credentials
def encryptcredential(pwd):
  rvalue=base64.b64encode(pwd.encode())
  return rvalue

#to validate if a customer is legitimate
flag=True

### procedure for validated customer
def validatedcustomer(customer):
    print ("Hello "+customer)
    inputcity=input("Which city do you want to travel (ex London/Paris/Chicago): ")
    inputaddinfo=input("Any specific checkin time [AM/PM] and preferred mode of travel [car/bus]: ")

    #### extract the regex values from additional info
    regex=re.compile('\d+:\d+\s[AP]M')
    time=re.findall(regex,inputaddinfo)
    if "car" in inputaddinfo:
        transport="car"
    else:
        if "bus" in inputaddinfo:
            transport="bus"

    ### create sentence based upon the additional info provided
    print ("\n\nYou have selected to checkin at "+time[0]+", and your preferred transport will be "+transport+" .")
    
    getcityinfo=validatecity(inputcity)

    ### this is to sort the dictionary from highest to lowest based upon weather types
    sorted_d = [(k, getcityinfo[k]) for k in sorted(getcityinfo, key=getcityinfo.get, reverse=True)]
    
    ###iterate through the weathers to construct a sentence
    sentence="Weather prediction for next 5 days is (chance of) "
    for item in sorted_d:
        sentence=sentence+" "+item[0]+": "+str(item[1])+"%,"
    print (sentence)
        
### to validate the average weather for that city for next 5 days
def validatecity(inputcity):
    #create empty list
    weathers=[]
    weatherpercentage={}
    #remove any additional spaces accidentally entered
    inputcity=inputcity.strip()
    urlx="https://samples.openweathermap.org/data/2.5/forecast?q="+inputcity+"&appid=b6907d289e10d714a6e88b30761fae22"
    #send the request to URL using GET Method
    r = requests.get(url = urlx)
    output=r.json()
    ### this is to parse the type of weather and count them in a list
    for item in output['list']:
        weathers.append(item['weather'][0]['description'])
    countweather=Counter(weathers)
    #### this is to find the percentage of each weather type from the given output (36 variations are returned from API)
    for item in countweather:
        weatherpercentage[item]=int((countweather[item]/36) * 100)
    return weatherpercentage 
    
### validate if the username is part of any customers
if (uname in customers):
    encryptedcreds=encryptcredential(creds)
    getcustomercreds=customers[uname]
    ### validate if the credentials provided is the same as stored credentials for that customer
    if not(str(encryptedcreds.decode()) == str(getcustomercreds.decode())):
        flag=False
else:
    flag=False    

if not(flag):
    print ("Unauthorized customer.")
else:
    validatedcustomer(uname)

Scenario 1: Incorrect username and password:

C:\gdrive\book2\github\edition2\chapter1>python use_case1.py
Enter your username :abhishek
Enter your password:
Unauthorized customer.

Scenario 2: Correct username but incorrect password:

C:\gdrive\book2\github\edition2\chapter1>python use_case1.py
Enter your username :customer1
Enter your password:
Unauthorized customer.

C:\gdrive\book2\github\edition2\chapter1>

Scenario 3: Correct username and password:

C:\gdrive\book2\github\edition2\chapter1>python use_case1.py
Enter your username :customer1
Enter your password:

Hello customer1
Which city do you want to travel (ex London/Paris/Chicago): paris
Any specific checkin time [AM/PM] and preferred mode of travel [car/bus]: travel checkin at 12:30 PM by bus

You have selected to checkin at 12:30 PM, and your preferred transport will be bus .
Weather prediction for next 5 days is (chance of) clear sky: 61%, light rain: 27%, few clouds: 5%, broken clouds: 2%, moderate rain: 2%,

C:\gdrive\book2\github\edition2\chapter1>

As we can see in the preceding output, the customer selected paris, with a check in time of 12:30 PM and bus as their mode of transport.

Based upon the location selected, the API call was made to the weather site, and a prediction of the weather for the next 5 days was returned in JSON. This has been evaluated in terms of a percentage, and a result value was given, which predicts a 61% chance of clear sky, followed by a 27% chance of light rain.

Let's run this output for another customer:

C:\gdrive\book2\github\edition2\chapter1>python use_case1.py
Enter your username :customer2
Enter your password:
Hello customer2
Which city do you want to travel (ex London/Paris/Chicago): Chicago
Any specific checkin time [AM/PM] and preferred mode of travel [car/bus]: checkin preferred at 10:00 AM and travel by car


You have selected to checkin at 10:00 AM, and your preferred transport will be car .
Weather prediction for next 5 days is (chance of) clear sky: 51%, light rain: 37%, few clouds: 5%, broken clouds: 2%, moderate rain: 2%,

C:\gdrive\book2\github\edition2\chapter1>

In this particular situation, we see that customer2 has a check-in preference of 10:00 AM and prefers to travel by car. 

Also, as per their selection of Chicago, the prediction of the weather is clear sky: 51%, light rain: 37%, few clouds: 5%, broken clouds: 2%, moderate rain: 2%.