"""
# Streamlit dependencies
import streamlit as st
# Data handling dependencies
import pandas as pd
import numpy as np
# Custom Libraries
from utils.data_loader import load_movie_titles
from recommenders.collaborative_based import collab_model
from recommenders.content_based import content_model
# Data Loading
title_list = load_movie_titles('resources/data/movies.csv')
# App declaration
def main():
# DO NOT REMOVE the 'Recommender System' option below, however,
# you are welcome to add more options to enrich your app.
page_options = [
"Recommender System", "Solution Overview", "Exploratory Data Analysis"
]
# -------------------------------------------------------------------
# ----------- !! THIS CODE MUST NOT BE ALTERED !! -------------------
# -------------------------------------------------------------------
page_selection = st.sidebar.selectbox("Choose Option", page_options)
def main():
# DO NOT REMOVE the 'Recommender System' option below, however,
# you are welcome to add more options to enrich your app.
page_options = ["Recommender System","Solution Overview","Exploratory Data Analysis","Hybrid Recommender System","New Movie Release","Search for a Movie","About Machine Learning App","Instruction of use"]
# -------------------------------------------------------------------
# ----------- !! THIS CODE MUST NOT BE ALTERED !! -------------------
# -------------------------------------------------------------------
page_selection = st.sidebar.selectbox("Choose Option", page_options)
if page_selection == "Recommender System":
# Header contents
st.write('# Movie Recommender Engine')
st.write('### EXPLORE Data Science Academy Unsupervised Predict')
st.image('resources/imgs/Image_header.png',use_column_width=True)
# Recommender System algorithm selection
sys = st.radio("Select an algorithm",
('Content Based Filtering',
'Collaborative Based Filtering'))
# User-based preferences
st.write('### Enter Your Three Favorite Movies')
movie_1 = st.selectbox('Fisrt Option',title_list[14930:15200])
movie_2 = st.selectbox('Second Option',title_list[25055:25255])
movie_3 = st.selectbox('Third Option',title_list[21100:21200])
fav_movies = [movie_1,movie_2,movie_3]
# Perform top-10 movie recommendation generation
if sys == 'Content Based Filtering':
if st.button("Recommend"):
try:
with st.spinner('Crunching the numbers...'):
top_recommendations = content_model(movie_list=fav_movies,
top_n=10)
st.title("We think you'll like:")
for i,j in enumerate(top_recommendations):
st.subheader(str(i+1)+'. '+j)
except:
st.error("Oops! Looks like this algorithm does't work.\
We'll need to fix it!")
if sys == 'Collaborative Based Filtering':
if st.button("Recommend"):
try:
with st.spinner('Crunching the numbers...'):
top_recommendations = collab_model(movie_list=fav_movies,
top_n=10)
st.title("We think you'll like:")
for i,j in enumerate(top_recommendations):
st.subheader(str(i+1)+'. '+j)
except:
st.error("Oops! Looks like this algorithm does't work.\
We'll need to fix it!")
# -------------------------------------------------------------------
# ------------- SAFE FOR ALTERING/EXTENSION -------------------
if page_selection == "Solution Overview":
st.image('resources/imgs/Avengers_Header.png',width = 600)
st.title("Solution Overview")
st.markdown("")
st.markdown('On this application you will find three methods to recommend movies to users, namely Content-Based filtering , Collaborative filtering and hybrid approach. A Hybrid approach is a combination of Content-Based filtering and collaborative filtering which the reason it works better than the individual approaches.\
Both Content-Based filtering and Collaborative filtering suffer from what we call a Cold start problem. A Cold start is a potential problem in computer-based information systems which involve a degree of automated data modelling. Specifically, it concerns the issue that the system cannot draw any inferences for users or items about which it has not yet gathered sufficient information.\
The item cold-start problem refers to when items added to the catalogue have either none or very little interactions. This constitutes a problem mainly for collaborative filtering algorithms due to the fact that they rely on the item\'s interactions to make recommendations.', unsafe_allow_html=True)
st.markdown("If no interactions are available then a pure collaborative algorithm cannot recommend the item.\
In case only a few interactions are available, although a collaborative algorithm will be able to recommend it, the quality of those recommendations will be poor.\
Content-based algorithms relying on user provided features suffer from the cold-start item problem as well, since for new items if no (or very few) interactions exist, also no (or very few) user reviews and tags will be available.")
st.markdown('When a new user enrolls in the system and for a certain period of time the recommender has to provide recommendation without relying on the user\'s past interactions, since none has occurred yet.\
This problem is of particular importance when the recommender is part of the service offered to users, since a user who is faced with recommendations of poor quality might soon decide to stop using the system before providing enough interaction to allow the recommender to understand his/her interests.\
The main strategy in dealing with new users is to ask them to provide some preferences to build an initial user profile. A threshold has to be found between the length of the user registration process, which if too long might indice too many users to abandon it, and the amount of initial data required for the recommender to work properly.', unsafe_allow_html=True)
st.markdown('The main approach is to rely on hybrid recommenders, in order to mitigate the disadvantages of one category or model by combining it with another.', unsafe_allow_html=True)
st.markdown('We use the RMSE to measure how accurate our model is when recommending movies for users.')
st.markdown('Root Mean Square Error (RMSE) is the standard deviation of the residuals (prediction errors). Residuals are a measure of how far from the regression line data points are; RMSE is a measure of how spread out these residuals are. In other words, it tells you how concentrated the data is around the line of best fit.', unsafe_allow_html=True)
st.markdown('The Hybrid Recommender Sytem gives the following RMSE for a subset of movies as when trying to use the whole dataframe gives memoryError')
st.markdown('<strong>RMSE:</strong> 0.9462',unsafe_allow_html=True)
st.markdown('The calculation of the RMSE can be found on the notebook in this Repository. The notebook name is Solution Overview.')
st.image('resources/imgs/Marvel_head.png',width = 800)
# You may want to add more sections here for aspects such as an EDA,
# or to provide your business pitch.
#Search for a Movie page
if page_selection =="Search for a Movie":
st.image('resources/imgs/52115.png',width = 600)
st.title("Search for Movies")
st.markdown('Please Refer to the About Machine Learning Page to learn more about the techniques used to recommend movies. If you decide not to use the recommender systems you can use this page to filter movies based on the rating of the movie , the year in which the movie was released and the genre of the movies. After you change the filter you will be left with movies that are specific to that filter used. Then when you scroll down you will see the movie name and the link to a youtube trailer of that movie. When you click the link ,you will see a page on youtube for that specific movie and you can watch the trailer and see if you like it. This is an alternative method to you if you are not satisfied with the recommender engine . Enjoy! ', unsafe_allow_html=True)
# Movies
df = pd.read_csv('resources/data/movies.csv')
def explode(df, lst_cols, fill_value='', preserve_index=False):
import numpy as np
# make sure `lst_cols` is list-alike
if (lst_cols is not None
and len(lst_cols) > 0
and not isinstance(lst_cols, (list, tuple, np.ndarray, pd.Series))):
lst_cols = [lst_cols]
# all columns except `lst_cols`
idx_cols = df.columns.difference(lst_cols)
# calculate lengths of lists
lens = df[lst_cols[0]].str.len()
# preserve original index values
idx = np.repeat(df.index.values, lens)
# create "exploded" DF
res = (pd.DataFrame({
col:np.repeat(df[col].values, lens)
for col in idx_cols},
index=idx)
.assign(**{col:np.concatenate(df.loc[lens>0, col].values)
for col in lst_cols}))
# append those rows that have empty lists
if (lens == 0).any():
# at least one list in cells is empty
res = (res.append(df.loc[lens==0, idx_cols], sort=False)
.fillna(fill_value))
# revert the original index order
res = res.sort_index()
# reset index if requested
if not preserve_index:
res = res.reset_index(drop=True)
return res
movie_data = pd.merge(rating_m, df, on='movieId')
movie_data['year'] = movie_data.title.str.extract('(\(\d\d\d\d\))',expand=False)
#Removing the parentheses
movie_data['year'] = movie_data.year.str.extract('(\d\d\d\d)',expand=False)
movie_data.genres = movie_data.genres.str.split('|')
movie_rating = st.sidebar.number_input("Pick a rating ",0.5,5.0, step=0.5)
movie_data = explode(movie_data, ['genres'])
movie_title = movie_data['genres'].unique()
title = st.selectbox('Genre', movie_title)
movie_data['year'].dropna(inplace = True)
movie_data = movie_data.drop(['movieId','timestamp','userId'], axis = 1)
year_of_movie_release = movie_data['year'].sort_values(ascending=False).unique()
release_year = st.selectbox('Year', year_of_movie_release)
movie = movie_data[(movie_data.rating == movie_rating)&(movie_data.genres == title)&(movie_data.year == release_year)]
df = movie.drop_duplicates(subset = ["title"])
if len(df) !=0:
st.write(df)
if len(df) ==0:
st.write('We have no movies for that rating!')
def youtube_link(title):
"""This function takes in the title of a movie and returns a Search query link to youtube
INPUT: ('The Lttle Mermaid')
-----------
OUTPUT: https://www.youtube.com/results?search_query=The+little+Mermaid&page=1
----------
"""
title = title.replace(' ','+')
base = "https://www.youtube.com/results?search_query="
q = title
page = "&page=1"
URL = base + q + page
return URL
if len(df) !=0:
for _, row in df.iterrows():
st.write(row['title'])
st.write(youtube_link(title = row['title']))
st.image('resources/imgs/horror.jpg',width = 600)
# Building out the EDA page
if page_selection == "Exploratory Data Analysis":
st.image('resources/imgs/philmovieheader.jpg',width = 600)
st.title("Insights on how people rate movies")
if st.checkbox('Show Rating graph'):
rating_m.groupby('rating')['userId'].count().plot(kind = 'bar', color = 'g',figsize = (8,7))
plt.xticks(rotation=85, fontsize = 14)
plt.yticks(fontsize = 14)
plt.xlabel('Ratings (scale: 0.5 - 5.0)', fontsize=16)
plt.ylabel('No. of Ratings', fontsize=16)
plt.title('Distribution of User Ratings ',bbox={'facecolor':'k', 'pad':5},color='w',fontsize = 18)
st.pyplot()
st.markdown("This is a bar graph showing the rating of movie by people who have watched them.")
st.markdown("The number of ratings is the total number of rating for each scale from 0.5 upto 5.0 rated by people who watched the movies.")
if st.checkbox('Show Pie chart for ratings'):
# Calculate and categorise ratings proportions
a = len(rating_m.loc[rating_m['rating']== 0.5]) / len(rating_m)
b = len(rating_m.loc[rating_m['rating']==1.0]) / len(rating_m)
c = len(rating_m.loc[rating_m['rating']==1.5]) / len(rating_m)
d = len(rating_m.loc[rating_m['rating']==2.0]) / len(rating_m)
low_ratings= a+b+c+d
e = len(rating_m.loc[rating_m['rating']==2.5]) / len(rating_m)
f = len(rating_m.loc[rating_m['rating']== 3.0]) / len(rating_m)
g = len(rating_m.loc[rating_m['rating']==3.5]) / len(rating_m)
medium_ratings= e+f+g
h = len(rating_m.loc[rating_m['rating']==4.0]) / len(rating_m)
i = len(rating_m.loc[rating_m['rating']==4.5]) / len(rating_m)
j = len(rating_m.loc[rating_m['rating']==5.0]) / len(rating_m)
high_ratings= h+i+j
# To view proportions of ratings categories, it is best practice to use pie charts
# Where the slices will be ordered and plotted clockwise:
labels = 'Low Ratings', 'Medium Ratings', 'High Ratings'
sizes = [low_ratings, medium_ratings, high_ratings]
explode = (0, 0, 0.1) # Only "explore" the 3rd slice (i.e. 'Anti')
# Create pie chart with the above labels and calculated class proportions as inputs
fig1, ax1 = plt.subplots()
ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',
shadow=True, startangle=270)#,textprops={'rotation': 65}
ax1.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle.
plt.title('Categorised Proportions of User Ratings ',bbox={'facecolor':'k', 'pad':5},color='w',fontsize = 18)
st.pyplot()
st.markdown("This is a pie chart showing the rating of movies by people who have watched them.")
st.markdown("Low Ratings (scale: 0.5 - 2.0)")
st.markdown("Medium Ratings (scale: 2.5 - 3.5)")
st.markdown("High Ratings (scale: 4.0 - 5.0)")
if st.checkbox('Show WordCloud of directors'):
imdb["title_cast"] = imdb["title_cast"].astype('str')
imdb["director"] = imdb["director"].astype('str')
imdb["plot_keywords"] = imdb["plot_keywords"].astype('str')
imdb["plot_keywords"] = imdb["plot_keywords"].apply(lambda x: x.replace('|',' '))
imdb["title_cast"] = imdb["title_cast"].apply(lambda x: x.replace(' ',''))
imdb["title_cast"] = imdb["title_cast"].apply(lambda x: x.replace('|',' '))
imdb["director"] = imdb["director"].apply(lambda x: x.replace(' ',''))
imdb["director"] = imdb["director"].apply(lambda x: x.replace('Seefullsummary',''))
imdb["director"] = imdb["director"].apply(lambda x: x.replace('nan',''))
imdb["title_cast"] = imdb["title_cast"].apply(lambda x: x.replace('nan',''))
imdb["plot_keywords"] = imdb["plot_keywords"].apply(lambda x: x.replace('nan',''))
directors = ' '.join([text for text in imdb["director"]])
# Word cloud for the overall data checking out which words do people use more often
wordcloud = WordCloud(width=1000, height=800).generate(directors)
#ploting the word cloud
plt.figure(figsize=(16,12))
plt.imshow(wordcloud)
plt.axis('off')
st.pyplot()
st.markdown("This is a wordcloud of the directors of movies in this Application.")
st.markdown("This wordcloud shows the most popular directors on the movies.")
if st.checkbox('Show WordCloud of Actors/Actresses'):
imdb["title_cast"] = imdb["title_cast"].astype('str')
imdb["director"] = imdb["director"].astype('str')
imdb["plot_keywords"] = imdb["plot_keywords"].astype('str')
imdb["plot_keywords"] = imdb["plot_keywords"].apply(lambda x: x.replace('|',' '))
imdb["title_cast"] = imdb["title_cast"].apply(lambda x: x.replace(' ',''))
imdb["title_cast"] = imdb["title_cast"].apply(lambda x: x.replace('|',' '))
imdb["director"] = imdb["director"].apply(lambda x: x.replace(' ',''))
imdb["director"] = imdb["director"].apply(lambda x: x.replace('Seefullsummary',''))
imdb["director"] = imdb["director"].apply(lambda x: x.replace('nan',''))
imdb["title_cast"] = imdb["title_cast"].apply(lambda x: x.replace('nan',''))
imdb["plot_keywords"] = imdb["plot_keywords"].apply(lambda x: x.replace('nan',''))
title_cast= ' '.join([text for text in imdb["title_cast"]])
# Word cloud for the overall data checking out which words do people use more often
wordcloud = WordCloud(width=1000, height=800).generate(title_cast)
#ploting the word cloud
plt.figure(figsize=(16,12))
plt.imshow(wordcloud)
plt.axis('off')
st.pyplot()
st.markdown("This is a wordcloud for Actors/Actresses on the movies on this Application.")
st.markdown("This wordcloud shows the most popular Actors/Actresses on the movies.")
if st.checkbox("Show wordcloud of different genres"):
movies = pd.read_csv('resources/data/movies.csv')
#here we make census of the genres:
genre_labels = set()
for s in movies['genres'].str.split('|').values:
genre_labels = genre_labels.union(set(s))
#counting how many times each of genres occur:
keyword_occurences, dum = count_word(movies, 'genres', genre_labels)
#Finally, the result is shown as a wordcloud:
words = dict()
trunc_occurences = keyword_occurences[0:50]
for s in trunc_occurences:
words[s[0]] = s[1]
tone = 100 # define the color of the words
f, ax = plt.subplots(figsize=(14, 6))
wordcloud = WordCloud(width=1000,height=800, background_color='white',
max_words=1628,relative_scaling=0.7,
color_func = random_color_func,
normalize_plurals=False)
wordcloud.generate_from_frequencies(words)
plt.figure(figsize=(16,12))
plt.imshow(wordcloud, interpolation="bilinear")
plt.axis('off')
st.pyplot()
st.markdown("This is a wordcloud for all the different genres in this Application.")
st.image('resources/imgs/genre.jpg',width = 600)
if page_selection == "Hybrid Recommender System":
st.title('Hybrid Recommender System')
st.image('resources/imgs/Image_header.png',use_column_width=True)
title_list1 = load_movie_titles('recommenders/hybrid_movies.csv')
# User-based preferences
st.write('### Enter Your Three Favorite Movies')
movie_1 = st.selectbox('Fisrt Option',title_list1[200:470])
movie_2 = st.selectbox('Second Option',title_list1[700:900])
movie_3 = st.selectbox('Third Option',title_list1[2000:2100])
fav_movies = [movie_1,movie_2,movie_3]
#st.write(movie_1)
#fav_movies = [movie_1]
if st.button("Recommend"):
#try:
with st.spinner('Crunching the numbers...'):
top_recommendations = recommendation(fav_movies, top_n = 10)
st.title("We think you'll like:")
for i,j in enumerate(top_recommendations):
st.subheader(str(i+1)+'. '+j)
if page_selection == "Instruction of use":
st.image('resources/imgs/joker-header.jpg',width = 600)
st.title("Instructions")
st.markdown('When the application opens the first page you will see is the Recommender System Page. Here you will see two algorithms you can choose from.')
st.image('recommenders/images/page1.png', width = 600)
st.markdown('Then you will have three options to choose from. On the select box you will choose your three favourite movie and then press the Recommend button.')
st.image(['recommenders/images/page2.png','recommenders/images/page3.png'],width=600)
st.markdown('After pressing the Recommend button then the recommended movies will be shown to you.')
st.image('recommenders/images/page4.png',width = 600)
st.markdown('Then you can choose the next algorithm to do the same task.')
st.image('recommenders/images/page16.png',width = 600)
st.markdown('On the left you will see a side bar that has all of the pages on this App.')
st.image('recommenders/images/page5.png',width = 600)
st.markdown('We have another page where we show you Actors , directors and how other people that watched the movies rate them.')
st.image('recommenders/images/page18.png',width = 600 )
st.markdown('This word cloud show the most popular Actors/Actresses on this movie App.')
st.image('recommenders/images/page7.png',width = 600)
st.markdown('This word cloud shows the different genres you can find on this App.')
st.image('recommenders/images/page8.png',width = 600)
st.markdown('The Search for a Movie page is an alternative to search for movies using filters instead of using Recommender Systems . The filters you can use are the release year of a movie, the rating of a movie and the genre of a movie and this will allow you to play around and find a specific movie using filters.')
st.image(['recommenders/images/page10.png','recommenders/images/page11.png'],width = 600)
st.image(['recommenders/images/page12.png','recommenders/images/page13.png'],width = 600)
st.markdown('Whe you scroll down this page you will see that as you change the movie name and a link . The link is a youtube movie trailer for that movie.')
st.image('recommenders/images/page14.png',width = 600)
st.markdown('Then when you click the link it will take you to the youtube page for the trailer of that movie.')
st.image('recommenders/images/page15.png',width = 600)
st.markdown('We have Another page where you can see new movie releases!')
st.image('recommenders/images/page19.png',width = 600)
if page_selection == "New Movie Release":
st.image('resources/imgs/ws_The_Avengers_Silhouettes_1680x1050.jpg',width = 600)
st.title("New Movie Release ")
st.markdown('You will find all new movie releases here . Enjoy!')
st.subheader("The Old Guard")
st.video('https://www.youtube.com/watch?v=aK-X2d0lJ_s')
st.markdown("Directed by : Gina Prince-Bythewood")
st.markdown("Starring : Charlize Theron, KiKi Layne, Marwan Kenzari, Luca Marinelli ,Harry Melling ")
st.markdown("Plot : Led by a warrior named Andy (Charlize Theron), a covert group of tight-knit mercenaries with a mysterious inability to die have fought to protect the mortal world for centuries. But when the team is recruited to take on an emergency mission and their extraordinary abilities are suddenly exposed, it's up to Andy and Nile (Kiki Layne), the newest soldier to join their ranks, to help the group eliminate the threat of those who seek to replicate and monetize their power by any means necessary.")
st.markdown("")
st.subheader("Bad Boys For Life (2020)")
st.video('https://www.youtube.com/watch?v=jKCj3XuPG8M')
st.markdown('Directed by : Adil & Bilall')
st.markdown('Starring : Will Smith, Martin Lawrence, Paola Núñez, Vanessa Hudgens, Alexander Ludwig, Charles Melton, Jacob Scipio')
st.markdown('Plot : Marcus and Mike have to confront new issues (career changes and midlife crises), as they join the newly created elite team AMMO of the Miami police department to take down the ruthless Armando Armas, the vicious leader of a Miami drug cartel')
st.subheader("Bloodshot (2020)")
st.video('https://www.youtube.com/watch?v=vOUVVDWdXbo')
st.markdown('Directed by : David S. F. Wilson ')
st.markdown('Starring : Vin Diesel, Eiza González ,Sam Heughan, Toby Kebbell')
st.markdown("Plot : After he and his wife are murdered, marine Ray Garrison is resurrected by a team of scientists. Enhanced with nanotechnology, he becomes a superhuman, biotech killing machine-'Bloodshot'. As Ray first trains with fellow super-soldiers, he cannot recall anything from his former life. But when his memories flood back and he remembers the man that killed both him and his wife, he breaks out of the facility to get revenge, only to discover that there's more to the conspiracy than he thought. ")
st.markdown("")
st.markdown("You can watch new movies in the following sites, Enjoy!")
st.image('resources/imgs/download1.jpg',width = 200)
st.markdown('<p><a href="https://www.netflix.com/za/">Netflix</a></p>', unsafe_allow_html=True)
st.image('resources/imgs/unnamed.jpg',width = 200)
st.markdown('<p><a href="https://www.showmax.com/eng/browse?type=movie">Showmax</a></p>', unsafe_allow_html=True)
st.image('resources/imgs/Disney_plus.jpg',width = 200)
st.markdown('<p><a href="https://preview.disneyplus.com/za">Disney plus</a></p>', unsafe_allow_html=True)
st.markdown('<p>This application is sponsored by <a href="https://explore-datascience.net/">Explore Data Science Academy</a> </p>', unsafe_allow_html=True)
st.image('resources/imgs/EDSA_logo.png', width = 800)
# Building out the About Machine Learning App page
if page_selection == "About Machine Learning App":
st.image('resources/imgs/star-wars-rise-of-skywalker-header.jpg',width = 600)
st.title("Welcome to the Recommender System Machine Learning App")
st.subheader('Machine Learning')
st.markdown('<p>Machine learning (ML) is the study of computer algorithms that improve automatically through experience.It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so.Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop conventional algorithms to perform the needed tasks. </p>', unsafe_allow_html=True)
st.subheader('Machine Learning Algorithms')
st.markdown('<p>A machine learning (ML) algorithm is essentially a process or sets of procedures that helps a model adapt to the data given an objective. An ML algorithm normally specifies the way the data is transformed from input to output and how the model learns the appropriate mapping from input to output. </p>', unsafe_allow_html=True)
st.subheader('Recommendation System')
st.markdown('<p>A recommendation system is a subclass of information filtering system that seeks to predict the "rating" or "preference" a user would give to an item. They are primarily used in commercial applications.Recommender systems are utilized in a variety of areas and are most commonly recognized as playlist generators for video and music services like Netflix, YouTube and Spotify, product recommenders for services such as Amazon, or content recommenders for social media platforms such as Facebook and Twitter. These systems can operate using a single input, like music, or multiple inputs within and across platforms like news, books, and search queries. Recommender systems usually make use of either or both collaborative filtering and content-based filtering.<p/>', unsafe_allow_html=True)
st.subheader('Collaborative filtering')
st.markdown('<p> Collaborative filtering approaches build a model from a user\'s past behavior (items previously purchased or selected and/or numerical ratings given to those items) as well as similar decisions made by other users. This model is then used to predict items (or ratings for items) that the user may have an interest in.<p/>', unsafe_allow_html=True)
st.subheader('Content-based filtering')
st.markdown('<p>Content-based filtering approaches utilize a series of discrete, pre-tagged characteristics of an item in order to recommend additional items with similar properties. Current recommender systems typically combine one or more approaches into a hybrid system. </p>', unsafe_allow_html=True)
st.subheader('Hybrid filtering')
st.markdown(' Hybrid filtering technique is a combination of multiple recommendation techniques like, merging collaborative filtering with content-based filtering or vice-versa.')
st.markdown(' Most recommender systems now use a hybrid approach, combining collaborative filtering, content-based filtering, and other approaches . There is no reason why several different techniques of the same type could not be hybridized. Hybrid approaches can be implemented in several ways: by making content-based and collaborative-based predictions separately and then combining them; by adding content-based capabilities to a collaborative-based approach (and vice versa); or by unifying the approaches into one model .')
st.markdown(' For more information about building data Apps Please go to :<a href="https://www.streamlit.io/">streamlit site</a></p>', unsafe_allow_html=True)
st.markdown('<p> </p>', unsafe_allow_html=True)
st.image('resources/imgs/Freaks.jpeg',width = 600)
# Streamlit dependencies
import streamlit as st
# Data handling dependencies
import pandas as pd
import numpy as np
# Custom Libraries
from utils.data_loader import load_movie_titles
from recommenders.collaborative_based import collab_model
from recommenders.content_based import content_model
path_to_s3 = ('../unsupervised_data/')
# Data Loading
title_list = load_movie_titles('../unsupervised_data/unsupervised_movie_data/movies.csv')
# App declaration
def main():
# DO NOT REMOVE the 'Recommender System' option below, however,
# you are welcome to add more options to enrich your app.
page_options = ["Introduction", "Directors Profiles","Recommender System","Solution Overview"]
# -------------------------------------------------------------------
# ----------- !! THIS CODE MUST NOT BE ALTERED !! -------------------
# -------------------------------------------------------------------
page_selection = st.sidebar.selectbox("Choose Option", page_options)
if page_selection == "Recommender System":
# Header contents
"""
# Streamlit dependencies
import streamlit as st
# Data handling dependencies
import pandas as pd
import numpy as np
# Custom Libraries
from utils.data_loader import load_movie_titles
from recommenders.collaborative_based import collab_model
from recommenders.content_based import content_model
# Data Loading
title_list = load_movie_titles(
'/home/explore-student/unsupervised_data/unsupervised_movie_data/movies.csv'
)
# App declaration
def main():
# DO NOT REMOVE the 'Recommender System' option below, however,
# you are welcome to add more options to enrich your app.
page_options = ["Recommender System", "Solution Overview"]
# -------------------------------------------------------------------
# ----------- !! THIS CODE MUST NOT BE ALTERED !! -------------------
# -------------------------------------------------------------------
page_selection = st.sidebar.selectbox("Choose Option", page_options)
if page_selection == "Recommender System":
