'''
    DS2000
    Spring 2023
    Sample code from class - recommendation sytem - driver
    
    Algorithm:
        * Have a bunch of TVShow objects =====> NEXT WEEK
        * Ask the user (Laney) for info about a show they 've watched =====> TODAY
        * Recommend shows with similar episode lengths, season,
          user age, and with high rating


    Possible todo --- ask the user for their rating, and recommend
                opposite of something they don't like, similar
                of something they like
'''

import csv
from tvshow import TVShow  

SHOW_FILE = "sec2_recommendations.csv"

def read_file(filename):
    ''' Function: read_file
        Parameter: filename, a string
        Returns: 2d list of strings
        Does: iterates over the file, each row becomes a sublist
    '''
    data = []
    with open(filename, "r") as infile:
        csvfile = csv.reader(infile)
        next(csvfile)
        for row in csvfile:
            data.append(row)
    return data

def make_show_objs(lst):
    ''' Function: make_show_objs
        Parameters: 2d list of strings
        Returns: 1d list of TVShow objects
        Does: literates over the list, each row becomes an object
    '''
    objs = []
    for row in lst:
        title = row[0]
        newness = int(row[1])
        pop = int(row[2])
        length = int(row[3])
        rating = float(row[4])
        show = TVShow(title, newness = newness, popularity = pop,
                      length = length, rating = rating)
        objs.append(show)
    return objs
    

def main():
    
    # Gather data - read in from the file
    show_lst = read_file(SHOW_FILE)
    
    # Gather data - ask the user for info about their show
    title = input("What show?\n")
    new = int(input("How new is it? 1=old, 10=new\n"))
    length = int(input("Length of episodes?\n"))
    popular = int(input("How popular?\n"))
    
    # computation - make an object of the TVShow class
    user_show = TVShow(title, length = length, popularity = popular, 
                  newness = new)
    
    # computation - make a list of show objects
    show_objs = make_show_objs(show_lst)
    
    # computation - filter the shows into recommendations
    recs = []
    for lst_show in show_objs:
        if lst_show.recommend(user_show):
            recs.append(lst_show)
    
    # communication - print out the recommended shows
    print("DS2000 thinks Laney should watch...\n")
    for rec in recs:
        print(rec)
    
    # rank the recommendations - pick the one with smallest
    # euclidean distance to watch first!
    min_show = None
    min_distance = float("inf")
    for rec in recs:
        distance = rec.euclidean(user_show)
        if distance < min_distance:
            min_distance = distance
            min_show = rec
    
    # Communicate - recommend the first show to watch
    print("\n\nBut start with ......")
    print(min_show)
    
    
    
  
main()