'''
    DS2000
    Spring 2022
    Building a rcommendation system from Review objects
    
    Some refinements we could try...
    1. Gather genre data from the reviews,
       recommend most-watched genres
       
    2. When the show was on, current vs old timey
    
    3. Maturity rating (pg, pg-13, etc.)
    
    4. User demographics. Recommend to someone similar to you.
    
    5. Recommend only to someone who has shows in common with you
    
    6. Recommend the overall most-watched, or most popular shows
    
    7. Use platform compatibility. Don't recommend something
        you can't watch.
    
    
'''

import csv
from review import Review

REVIEW_FILE = "reviews_sec1.csv"

def csv_to_dict(filename):
    ''' Function: csv_to_dictt
        Parameter: filename, a string
        returns: a list of dictionaries, where key = column header,
                  value = value from a row
    '''
    list_of_dict = []
    with open(filename, "r") as infile:
        csvfile = csv.reader(infile, delimiter = ",")
        keys = next(csvfile)
        for row in csvfile:
            d = {}
            for i in range(len(keys)):
                d[keys[i]] = row[i]
            list_of_dict.append(d)
    return list_of_dict
    
    
def make_review_objs(list_of_dict, person):
    ''' function: make_laney_objs
        paramter: list of dictionaries, where one dictionary = one show
                   and the name of a person to look for (string)
        returns: list of Review objects
    '''
    reviews = []
    for item in list_of_dict:
        name = item["Name"]
        if name == person:
            title = item["Title"]
            rating = int(item["Rating"])
            platform = item["Platform"]
            review = Review(name, title, rating, platform = platform)
            reviews.append(review)
    return reviews
    
    


def make_recommendations(laney_reviews, friend_reviews, platforms):
    ''' Function: make _recomjendations
        Paramaeters: list of review objects (laney's), list of review 
                     objects (friend's), list of acceptable
                     platforms (list of strings)
        Returns: list of review objects(recommendations)
    '''
    recs = []
    for review in friend_reviews:
        if review.rating > 3 and review not in laney_reviews:
            if review.platform in platforms:
                recs.append(review)
    return recs
    
def shows_in_common(laney, friend):
    ''' Function: shows in common
        parameters: 2 lists of review objects (Laney, and recommender)
        Return: # of shows in common
    '''
    count = 0
    for review in laney:
        if review in friend:
            count += 1
    return count
    
def main():
    
    # Step one -- gather data
    # Make a list of dictionaries for all the reviews
    reviews = csv_to_dict(REVIEW_FILE)
    # print(reviews)
    
    # Make a list of review objects for Laney
    laney_objs = make_review_objs(reviews, "Laney")
    platforms = []
    for review in laney_objs:
        platforms.append(review.platform)
    
    # Make a list of review objects for my recommender
    friend = input("Who is recommending to Laney?\n")
    friend_objs = make_review_objs(reviews, friend)


    # Get number of shows in common
    in_common = shows_in_common(laney_objs, friend_objs)
    if in_common == 0:
        print("You and", friend, "have nothing in common, but "
              "here are their recs...")
    elif in_common == 1:
        print("You might want to watch...")
    else:
        print("You should DEFINITELY watch omg...")
        
    # Make recommendations!
    recs = make_recommendations(laney_objs, friend_objs, platforms)
    for rec in recs:
        print(rec)
    
    
main()