changes to recommender py file

edsa_recommender_new.py

@@ -0,0 +1,233 @@
+"""
+
+    Streamlit webserver-based Recommender Engine.
+
+    Author: Explore Data Science Academy.
+
+    Note:
+    ---------------------------------------------------------------------
+    Please follow the instructions provided within the README.md file
+    located within the root of this repository for guidance on how to use
+    this script correctly.
+
+    NB: !! Do not remove/modify the code delimited by dashes !!
+
+    This application is intended to be partly marked in an automated manner.
+    Altering delimited code may result in a mark of 0.
+    ---------------------------------------------------------------------
+
+    Description: This file is used to launch a minimal streamlit web
+	application. You are expected to extend certain aspects of this script
+    and its dependencies as part of your predict project.
+
+	For further help with the Streamlit framework, see:
+
+	https://docs.streamlit.io/en/latest/
+
+"""
+# 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","Welcome", "Predictive Insights", "Process", "In a Nutshell" ]
+
+    # -------------------------------------------------------------------
+    # ----------- !! 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 == "Predictive Insights":
+        st.title("Predictive Insights")
+        st.write("## About Us")
+        st.write("Predictive Insights is a renowned data-based agency that has been on the scene for over a decade. We strive to put our best foot forward with every project we tackle. Our mission is to provide a service that goes above and beyond your expectations.")
+        about = st.sidebar.checkbox("About Us")
+        team = st.sidebar.checkbox("Team Members")
+    if team:
+        st.subheader("Our Team")
+        st.markdown("Thato Molapisi, CEO")
+        st.markdown("Bongokuhle Dladla, Team Lead")
+        st.markdown("Dominick Bardo")
+
+    if page_selection == "Welcome":
+        st.title("Welcome to your Movie Recommender App")
+
+        # st.image('Images/Cinema.jpg',width = 640)
+
+        st.write("The aim for this application is to give users the ability to discover new movies based on their 3 favourite movies.")
+        team = st.sidebar.checkbox("Meet the Team")
+        about = st.sidebar.checkbox("About")
+        if about:
+
+
+            st.subheader("**Problem Statement**")
+            st.markdown("Recommender systems have become indispensable in navigating the vast sea of online content.\
+                        With an abundance of information available, many users struggle not only to find what they want\
+                        but even to define their preferences in the first place,\
+                        These systems delicately connect users with relevant content, making recommendations for a wide range of items,\
+                        such as shoes, clothes, places, films, applications, browser plugins, memes, music, blog posts,.\
+                        In essence,anything can be recommended or suggested - shoe's, clothes, places, films, applications,\
+                         browser plugins, memes, music, blog posts, communities, and even people with specific skills.")
+
+            st.subheader("**Overview**")
+            st.markdown("The app utilizes a content-based algorithm to provide personalized movie recommendations based on the user's,\
+                        three favorite movies. By taking input from the user about their preferences,\
+                        the app generates a curated list of recommended movies, offering an excellent opportunity\
+                        for users to discover new and exciting content to watch.")
+        if team:
+            st.subheader("**DN!**")
+
+        # st.image("Images/group_member.png", use_column_width=True)
+            st.markdown("* Domincick Bardo- Team scientist")
+            st.markdown("* enter memeber name")
+            st.markdown("* enter memeber name")
+            st.markdown("* enter memeber name")
+            st.markdown("* enter memeber name")
+
+            st.subheader("**CEO**")
+            st.write("who is the ceo")
+
+
+    if page_selection == "In a Nutshell":
+        st.subheader("**Overall conclusion**")
+        st.markdown("In this where data is generated at a faster rated than we can use.\
+                        With a majority of modern services and products now being offered predominately online,\
+                        it can be hard to get to know your customers.\
+                        Unlike running a local store where you get to know each person that comes in,\
+                        online businesses can struggle to know exactly what their users are expecting.")
+
+        st.subheader("**How can your business use this?**")
+        st.markdown("To advances in machine learning, and deep learning specifically,\
+                        it is now possible to get to know millions of customers completely online simply through their data.\
+                        By using a data model to filter through your users' favorite products and interests,\
+                        it is easier than ever to make recommendations to them for what they would enjoy to use or buy.")
+
+        st.markdown("Content-based solutions are used by a range of different businesses.\
+                        They're probably the most commonly used method and one we have all encountered at some point.\
+                        Sites such as Amazon and the Google Play Store are just some of the many examples out there.")
+
+
+        st.title("Movie Time, Enjoy! ")
+
+    if page_selection == "Team":
+        st.title("Meet the team")
+
+
+
+    if page_selection == "Process":
+        st.sidebar.subheader("Exploratory Data Analysis")
+
+        source = st.sidebar.checkbox("Data Source")
+        if source:
+            st.subheader("Where did we get the data from?")
+            st.image('Images/download.jpg', use_column_width =False)
+            st.markdown("The data was obtained from the MovieLens \
+            which has the several millions 5-star ratings obtained from users using the online recommendation system.\
+            The IMBD (imbd_df) was legally scraped from IMDB.")
+
+        look = st.sidebar.checkbox("Data")
+        if look:
+            st.subheader("What did the data consists?")
+            st.markdown("* Movies -Movie titles are entered manually or imported from https://www.themoviedb.org/, and include the year of release in parentheses")
+            st.markdown("* Genome_scores - is a score mapping the strength between movies and tag-related properties.")
+            st.markdown("* Genome_tags - User assigned for the movies within the dataset.")
+            st.markdown("* Imdb - Additional movie metadata scraped from IMDB using the links.csv file.")
+            st.markdown("* Links - File providing a mapping between a MovieLens ID and associated IMDB and TMDB IDs.")
+            st.markdown("* Train - The training split of the dataset. Contains user and movie IDs with associated rating data.")
+
+
+        user = st.sidebar.checkbox("User analysis")
+        if user:
+            st.subheader("Insights from users")
+            st.image('Images/user_analysis.png', width = 720)
+            st.markdown("These graphs show the density plots of the average rating of users and the number of movies per user.\
+                        What is observed that most of the users rated the movies an avg of 3.5 and most users watched less than 500 movies")
+        movies = st.sidebar.checkbox("Movie analysis")
+        if movies:
+            st.subheader("Insights from Movies")
+            st.image('Images/wordcloud.jpg', use_column_width=False)
+            st.markdown("These are most common genres in the dataset!")
+
+        st.sidebar.subheader("Modelling")
+        model = st.sidebar.checkbox("Content-based")
+        if model:
+            st.subheader("Content based Model")
+            st.markdown("**Algorithm:**")
+            st.markdown("The algorithm that was used was the unsupervised kNN learning best method.\
+                        It acts as a uniform interface to three different nearest neighbors algorithms:\
+                        BallTree, KDTree, and a brute-force algorithm based on routines in sklearn.metrics.pairwise.\
+                        More infomation can be received here:https://scikit-learn.org/stable/modules/neighbors.html#:~:text=1.-,Unsupervised%20Nearest%20Neighbors,based%20on%20routines%20in%20sklearn.")
+
+
+
+
+
+    # You may want to add more sections here for aspects such as an EDA,
+    # or to provide your business pitch.
+
+
+if __name__ == '__main__':
+    main()