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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Sep 7 14:09:23 2022
@author: ilkayisik
Streamlit app for user based movie recommendations
Changes to the first version:
1. put all the widgets to the sidebar
2. add the time period option in the user id based recommendation
"""
# imports
import streamlit as st
import pandas as pd
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
from PIL import Image
from datetime import datetime
import os
abspath = os.path.abspath(__file__)
dname = os.path.dirname(abspath)
os.chdir(dname)
# %% load data
movie_df = pd.read_csv('https://raw.githubusercontent.com/sherwan-m/WBSFLIX_Recommender_System/main/ml-latest-small/movies.csv')
rating_df = pd.read_csv('https://raw.githubusercontent.com/sherwan-m/WBSFLIX_Recommender_System/main/ml-latest-small/ratings.csv')
links_df = pd.read_csv('https://raw.githubusercontent.com/sherwan-m/WBSFLIX_Recommender_System/main/ml-latest-small/links.csv')
tags_df = pd.read_csv('https://raw.githubusercontent.com/sherwan-m/WBSFLIX_Recommender_System/main/ml-latest-small/tags.csv')
# %% format dataframes
# MOVIE DF:
movie_df = (
movie_df
.assign(year=lambda df_ : df_['title'].replace(r'(.*)\((\d{4})\)', r'\2', regex= True))
# replace with 0 if there is no year
.assign(year=lambda df_ : np.where(df_['year'].str.len() <=5 , df_['year'], 0)))
# convert the year column to int
movie_df['year'] = movie_df['year'].astype(int)
# create a genre list
genre_list = []
for i in movie_df['genres']:
if "|" in i:
genre_list.extend(i.rsplit("|"))
else:
genre_list.append(i)
genre_list = list(set(genre_list))
i = genre_list.index("(no genres listed)")
del genre_list[i]
genre_list.sort()
genre_list.insert(0, 'Any')
year_list = list(set(list(movie_df['year'])))[1:]
# create a list of movies
movie_list = list(set(list(movie_df['title'])))
# %% RATING DF
# convert timestamp to datetime format
rating_df['datetime'] = rating_df['timestamp'].apply(datetime.fromtimestamp)
# drop the timestamp column
rating_df.drop(columns=['timestamp'], inplace=True)
# %% DEFINE FUNCTIONS
# to make the the dataframe look nicer
def make_pretty(styler):
styler.set_caption("Top movie recommendations for you")
# styler.background_gradient(cmap="YlGnBu")
return styler
# population based: v1
# def popular_n_movies(n, genre):
# popular_n = (
# rating_df
# .groupby(by='movieId')
# .agg(rating_mean=('rating', 'mean'),
# rating_count=('movieId', 'count'),
# datetime=('datetime','mean'))
# .sort_values(['rating_mean','rating_count','datetime'], ascending= False)
# .loc[lambda df_ :df_['rating_count'] >= (df_['rating_count'].mean() + df_['rating_count'].median())/2]
# .reset_index()
# )['movieId'].to_list()
# result = movie_df.loc[lambda df_ : df_['movieId'].isin(popular_n)]
# if genre != 'Any':
# result = result.loc[lambda df_ : df_['genres'].str.contains(genre)]
# df_rec = result.head(n).reset_index(drop=True)
# df_rec = df_rec[['title', 'genres', 'year']].reset_index(drop=True)
# new_index = ['movie-{}'.format(i+1) for i in range(n)]
# df_rec.index = new_index
# pretty_rec = df_rec.style.pipe(make_pretty)
# return pretty_rec
# population_based v2
def popular_n_movies(n, genres):
if genres == "Any":
genres = ""
recommendations = (
rating_df
.groupby('movieId')
.agg(avg_rating = ('rating', 'mean'), num_ratings = ('rating', 'count'))
.merge(movie_df, on='movieId')
.assign(combined_rating = lambda x: x['avg_rating'] * x['num_ratings']**0.5)
[lambda df: df["genres"].str.contains(genres, regex=True)]
.sort_values('combined_rating', ascending=False)
.head(n)
[['title', 'avg_rating', 'genres']]
.rename(columns= {'title': 'Movie Title', 'avg_rating': 'Average Rating', 'genres': 'Genres'}))
recommendations = recommendations[['Movie Title', 'Genres']]
recommendations.reset_index(drop=True, inplace=True)
pretty_recommendations = recommendations.style.pipe(make_pretty)
return pretty_recommendations
# movie/item based
def item_n_movies(movie_name, n):
min_rate_count = 10
movieId = list(movie_df[movie_df['title'] == movie_name].movieId.head(1))[0]
movies_crosstab = pd.pivot_table(data=rating_df, values='rating',
index='userId',
columns='movieId')
movie_ratings = movies_crosstab[movieId]
movie_ratings = movie_ratings[movie_ratings>=0] # exclude NaNs
# evaluating similarity
similar_to_movie = movies_crosstab.corrwith(movie_ratings)
corr_movie = pd.DataFrame(similar_to_movie, columns=['PearsonR'])
corr_movie.dropna(inplace=True)
rating = pd.DataFrame(rating_df.groupby('movieId')['rating'].mean())
rating['rating_count'] = rating_df.groupby('movieId')['rating'].count()
movie_corr_summary = corr_movie.join(rating['rating_count'])
movie_corr_summary.drop(movieId, inplace=True) # drop forrest gump itself
top_n = movie_corr_summary[movie_corr_summary['rating_count'] >= min_rate_count].sort_values('PearsonR', ascending=False).head(n)
top_n = top_n.merge(movie_df, left_index=True, right_on="movieId")
top_n = top_n[['title', 'genres']].reset_index(drop=True)
new_index = ['movie-{}'.format(i+1) for i in range(n)]
top_n.index = new_index
pretty_rec = top_n.style.pipe(make_pretty)
return pretty_rec
# user based
def user_n_movies(user_id, n):
users_items = pd.pivot_table(data=rating_df,
values='rating',
index='userId',
columns='movieId')
users_items.fillna(0, inplace=True)
user_similarities = pd.DataFrame(cosine_similarity(users_items),
columns=users_items.index,
index=users_items.index)
weights = (user_similarities.query("userId!=@user_id")[user_id] / sum(user_similarities.query("userId!=@user_id")[user_id]))
not_seen_movies = users_items.loc[users_items.index!=user_id, users_items.loc[user_id,:]==0]
weighted_averages = pd.DataFrame(not_seen_movies.T.dot(weights), columns=["predicted_rating"])
recommendations = weighted_averages.merge(movie_df, left_index=True, right_on="movieId")
top_recommendations = recommendations.sort_values("predicted_rating", ascending=False).head(n)
top_recommendations = top_recommendations[['title', 'genres']].reset_index(drop=True)
new_index = ['movie-{}'.format(i+1) for i in range(n)]
top_recommendations.index = new_index
pretty_rec = top_recommendations.style.pipe(make_pretty)
return pretty_rec
# user based with year as input
def top_n_user_based(user_id , n , genres, time_period):
if user_id not in rating_df["userId"]:
return pd.DataFrame(columns= ['movieId', 'title', 'genres', 'year'])
users_items = pd.pivot_table(data=rating_df,
values='rating',
index='userId',
columns='movieId')
users_items.fillna(0, inplace=True)
user_similarities = pd.DataFrame(cosine_similarity(users_items),
columns=users_items.index,
index=users_items.index)
weights = (
user_similarities.query("userId!=@user_id")[user_id] / sum(user_similarities.query("userId!=@user_id")[user_id])
)
new_userids = weights.sort_values(ascending=False).head(100).index.tolist()
new_userids.append(user_id)
new_ratings = rating_df.loc[lambda df_: df_['userId'].isin(new_userids)]
new_users_items = pd.pivot_table(data=new_ratings,
values='rating',
index='userId',
columns='movieId')
new_users_items.fillna(0, inplace=True)
new_user_similarities = pd.DataFrame(cosine_similarity(new_users_items),
columns=new_users_items.index,
index=new_users_items.index)
new_weights = (
new_user_similarities.query("userId!=@user_id")[user_id] / sum(new_user_similarities.query("userId!=@user_id")[user_id])
)
not_watched_movies = new_users_items.loc[new_users_items.index!=user_id, new_users_items.loc[user_id,:]==0]
weighted_averages = pd.DataFrame(not_watched_movies.T.dot(new_weights), columns=["predicted_rating"])
recommendations = weighted_averages.merge(movie_df, left_index=True, right_on="movieId").sort_values("predicted_rating", ascending=False)
recommendations = recommendations.loc[lambda df_ : ((df_['year'] >= time_period[0]) & ( df_['year'] <= time_period[1]))]
if len(genres)>0:
result = pd.DataFrame(columns=['predicted_rating', 'movieId', 'title', 'genres', 'year'])
for genre in genres:
result = pd.concat([result, recommendations.loc[lambda df_ : df_['genres'].str.contains(genre)]])
result.drop_duplicates(inplace=True)
result = result.sort_values("predicted_rating", ascending=False)
result.reset_index(inplace=True, drop= True)
return result.drop(columns=['predicted_rating']).head(n)
return recommendations.reset_index(drop=True).drop(columns=['predicted_rating']).head(n)
# %% STREAMLIT
# Set configuration
st.set_page_config(page_title="WBSFLIX",
page_icon="🎬",
initial_sidebar_state="expanded",
layout="wide"
)
# set colors: These has to be set on the setting menu online
# primary color: #FF4B4B, background color:#0E1117
# text color: #FAFAFA, secondary background color: #E50914
# Set the logo of app
st.sidebar.image("wbs_logo.png",
width=300, clamp=True)
welcome_img = Image.open('welcome_page_img01.png')
st.image(welcome_img)
st.sidebar.markdown("""
# 🎬 Welcome to the next generation movie recommendation app
""")
# %% APP WORKFLOW
st.sidebar.markdown("""
### How may we help you?
"""
)
# Popularity based recommender system
genre_default = None
pop_based_rec = st.sidebar.checkbox("Show me the all time favourites",
False,
help="Movies that are liked by many people")
if pop_based_rec:
st.markdown("### Select the Genre and the Number of recommendations")
genre_default, n_default = None, 5
with st.form(key="pop_form"):
genre_default = ['Any']
genre = st.multiselect(
"Genre",
options=genre_list,
help="Select the genre of the movie you would like to watch",
default=genre_default)
nr_rec = st.slider("Number of recommendations",
min_value=1,
max_value=20,
value=5,
step=1,
key="n",
help="How many movie recommendations would you like to receive?",
)
submit_button_pop = st.form_submit_button(label="Submit")
if submit_button_pop:
popular_movie_recs = popular_n_movies(nr_rec, genre[0])
st.table(popular_movie_recs)
# to put some space in between options
st.write("")
st.write("")
st.write("")
item_based_rec = st.sidebar.checkbox("Show me a movie like this",
False,
help="Input some movies and we will show you similar ones")
if item_based_rec:
st.markdown("### Tell us a movie you like:")
with st.form(key="movie_form"):
movie_name = st.multiselect(label="Movie name",
# options=movie_list,
options=pd.Series(movie_list),
help="Select a movie you like",
key='item_select',
# default=choice(short_movie_list)
)
nr_rec = st.slider("Number of recommendations",
min_value=1,
max_value=20,
value=5,
step=1,
key="nr_rec_movie",
help="How many movie recommendations would you like to receive?",
)
submit_button_movie = st.form_submit_button(label="Submit")
if submit_button_movie:
st.write('Because you like {}:'.format(movie_name[0]))
item_movie_recs = item_n_movies(movie_name[0], nr_rec)
st.table(item_movie_recs)
# to put some space in between options
st.write("")
st.write("")
st.write("")
user_based_rec = st.sidebar.checkbox("I want to get personalized recommendations",
False,
help="Login to get personalized recommendations")
if user_based_rec:
st.markdown("### Please login to get customized recommendations just for you")
genre_default, n_default = None, 5
with st.form(key="user_form"):
user_id = st.number_input("Please enter your user id", step=1,
min_value=1)
genre_default = ['Any']
genre = st.multiselect(
"Genre",
options=genre_list,
help="Select the genre of the movie you would like to watch",
#default=genre_default
)
nr_rec = st.slider("Number of recommendations",
min_value=1,
max_value=20,
value=5,
step=1,
key="nr_rec",
help="How many movie recommendations would you like to receive?",
)
time_period = st.slider('years:', min_value=1900,
max_value=2018,
value=(2010,2018),
step=1)
submit_button_user = st.form_submit_button(label="Submit")
if submit_button_user:
# user_movie_recs = user_n_movies(user_id, nr_rec)
user_movie_recs = top_n_user_based(user_id, nr_rec, genre, time_period)
# st.write(time_period)
st.table(user_movie_recs[['title', 'genres']].style.pipe(make_pretty))
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