Thulani

.github/workflows/python-package-conda.yml

@@ -0,0 +1,34 @@
+name: Python Package using Conda
+
+on: [push]
+
+jobs:
+  build-linux:
+    runs-on: ubuntu-latest
+    strategy:
+      max-parallel: 5
+
+    steps:
+    - uses: actions/checkout@v3
+    - name: Set up Python 3.10
+      uses: actions/setup-python@v3
+      with:
+        python-version: 3.10
+    - name: Add conda to system path
+      run: |
+        # $CONDA is an environment variable pointing to the root of the miniconda directory
+        echo $CONDA/bin >> $GITHUB_PATH
+    - name: Install dependencies
+      run: |
+        conda env update --file environment.yml --name base
+    - name: Lint with flake8
+      run: |
+        conda install flake8
+        # stop the build if there are Python syntax errors or undefined names
+        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
+        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+    - name: Test with pytest
+      run: |
+        conda install pytest
+        pytest

edsa_recommender.py

@@ -1,112 +1,152 @@
 """
-
     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
+import hydralit_components as hc
 
 # Data handling dependencies
 import pandas as pd
 import numpy as np
+from sympy import im
 
 # Custom Libraries
 from utils.data_loader import load_movie_titles
 from recommenders.collaborative_based import collab_model
 from recommenders.content_based import content_model
-
+import functions.youtube_scrapper as top_trailers
+import menu.trailers as t
+import menu.data_professionals as dreamers
+import menu.statistics as stat
+import menu.helper as h
+import menu.About as a
+import time
 # Data Loading
-title_list = load_movie_titles('resources/data/movies.csv')
+title_list = load_movie_titles('https://raw.githubusercontent.com/Dream-Team-Unsupervised/Data/main/movies.csv')
+
+st.set_page_config(page_icon='resources/imgs/MovieXplorer.png', page_title= 'Movie Xplorer', layout='wide', initial_sidebar_state='auto')
+
+over_theme = {'txc_inactive': '#FFFFFF'}
+
+# specify the primary menu definition
+menu_data = [
+    {'icon': "far fa-copy", 'label':'About'},
+    {'id':'Trailers','icon':'fas fa-film','label':'Trailers'},
+    {'icon': 'far fa-chart-bar', 'label':'Statistics'}, #no tooltip message
+    {'id':'Contact Us','icon': 'fas fa-laptop', 'label':'Contact Us'},
+    {'id':'Help', 'icon': 'fas fa-question', 'label':'Help'}
+]
 
 # App declaration
-def main():
 
+def main():
+    # define hydralit navbar
+    menu_id = hc.nav_bar(
+    menu_definition=menu_data,
+    override_theme=over_theme,
+
+    home_name='Home',
+    # login_name='Logout',
+    hide_streamlit_markers=False, #will show the st hamburger as well as the navbar now!
+    sticky_nav=True, #at the top or not
+    sticky_mode='pinned', #jumpy or not-jumpy, but sticky or pinned
+)
+    page_selection = f'{menu_id}'
     # 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"]
+    # page_options = ["Recommender System", "About", "Trailers", "Statistics", "The Dream Team", "Help Page"]
 
     # -------------------------------------------------------------------
     # ----------- !! THIS CODE MUST NOT BE ALTERED !! -------------------
     # -------------------------------------------------------------------
-    page_selection = st.sidebar.selectbox("Choose Option", page_options)
-    if page_selection == "Recommender System":
+    # page_selection = st.sidebar.selectbox("Choose Option", page_options)
+
+    if page_selection == 'Home':
         # 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)
+        st.write('# Movie Xplorer')
+        # st.write('### EXPLORE Data Science Academy Unsupervised Predict')
+        st.image('resources/imgs/Header2L.gif',use_column_width=True)
         # Recommender System algorithm selection
-        sys = st.radio("Select an algorithm",
-                       ('Content Based Filtering',
-                        'Collaborative Based Filtering'))
+        st.write('<style>div.row-widget.stRadio > div{flex-direction:row;justify-content: right;} </style>', unsafe_allow_html=True)
+        st.write('<style>div.st-bf{flex-direction:column;} div.st-ag{font-weight:bold;padding-right:2px;}</style>', unsafe_allow_html=True)
+        sys = st.radio("", ('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])
+        st.write('### Select Your Three Favorite Movies')
+        movie_1 = st.selectbox('1ˢᵗ Movie',title_list[14930:15200])
+        movie_2 = st.selectbox('2ⁿᵈ Movie',title_list[25055:25255])
+        movie_3 = st.selectbox('3ʳᵈ Movie',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"):
+            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:")
+                    # intialize hydralit loaders
+                    with hc.HyLoader('We\'re getting movies only you will love...\n',hc.Loaders.standard_loaders,index=[5,0,3]):
+                        # get top 10 recommended movies using the content_model algorithm
+                        top_recommendations = content_model(movie_list=fav_movies, top_n=10)
+                        time.sleep(5)
+                    st.title('Only you will love these movies...')
                     for i,j in enumerate(top_recommendations):
                         st.subheader(str(i+1)+'. '+j)
+                        # get trailer from youtube
+                        top_trailers.youtubeScrapper(top_recommendations[i])
                 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"):
+            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:")
+                    # intialize hydralit loaders
+                    with hc.HyLoader('We\'re getting movies only you will love...\n',hc.Loaders.standard_loaders,index=[5,0,3]):
+                        # get top 10 recommended movies using the collab_model algorithm
+                        top_recommendations = collab_model(movie_list=fav_movies, top_n=10)
+                        time.sleep(5)
+                    st.title('Only you will love these movies...')
                     for i,j in enumerate(top_recommendations):
                         st.subheader(str(i+1)+'. '+j)
+                        # get trailer from youtube
+                        top_trailers.youtubeScrapper(top_recommendations[i])
                 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.title("Solution Overview")
-        st.write("Describe your winning approach on this page")
-
+    # ------------- SAFE FOR ALTERING/EXTENSION -------------------------
+    elif page_selection == 'About':
+        # navigate to the About page
+        a.about()
+    elif page_selection == 'Trailers':
+        # navigate to the Trailers page
+        t.vids()
+    elif page_selection == 'Contact Us':
+        # navigate to the Contact Us page
+        dreamers.data_professionals()
+    elif page_selection == 'Statistics':
+        # navigate to the Statistics page
+        stat.visuals()
+    elif page_selection == 'Help':
+        # navigate to the Help page
+        h.helppage()
     # You may want to add more sections here for aspects such as an EDA,
     # or to provide your business pitch.
 
-
 if __name__ == '__main__':
     main()

functions/CRUD/CREATE.py

@@ -0,0 +1,7 @@
+import csv
+
+def insert(post):
+    with open('./resources/data/comments.csv', 'a', newline='', encoding='UTF8') as c:
+        writer = csv.writer(c)
+        # write the data
+        writer.writerow(post)

functions/CRUD/READ.py

@@ -0,0 +1,16 @@
+import csv
+import streamlit as st
+
+COMMENT_TEMPLATE_MD = """{} - {}
+> {}"""
+
+def select(): 
+    # opening the CSV file
+    with open('./resources/data/comments.csv', mode='r') as c:
+    # reading the CSV file
+        csvFile = csv.reader(c)
+        # displaying the contents of the CSV file
+        next(csvFile)
+        for lines in csvFile:
+                st.markdown(COMMENT_TEMPLATE_MD.format(lines[0], lines[2], lines[1]))
+

functions/CRUD/UPDATE.py

@@ -0,0 +1,7 @@
+import csv
+
+def insert(post):
+    with open('./resources/data/comments.csv', 'a', newline='', encoding='UTF8') as c:
+        writer = csv.writer(c)
+        # write the data
+        writer.writerow(post)

functions/comments.py

@@ -0,0 +1,21 @@
+from datetime import datetime
+import streamlit as st
+import functions.CRUD.CREATE as create
+import functions.CRUD.READ as read
+
+def commenter(yr):
+
+    read.select()
+
+    form = st.form(yr + 'comments')
+    name = form.text_input('Name')
+    comment = form.text_area('Comment')
+    submit = form.form_submit_button('Share comment')
+
+    if submit:
+        date = datetime.now().strftime("%d/%m/%Y %H:%M:%S")
+        par = [name, comment, date]
+        create.insert(par)
+        if "just_posted" not in st.session_state:
+            st.session_state["just_posted"] = True
+        st.experimental_rerun()

functions/youtube_scrapper.py

@@ -0,0 +1,26 @@
+import urllib.request
+import requests
+import unicodedata
+import re
+from streamlit_player import st_player
+# import os
+# import streamlit as st
+# import googleapiclient.discovery
+
+def youtubeScrapper(top_10):
+    search_string = unicodedata.normalize('NFKD', top_10).encode('ascii', 'ignore').decode()
+    youtube_str = re.sub("[ ]", "+", search_string)
+    html = urllib.request.urlopen('https://www.youtube.com/results?search_query=' + youtube_str + '+trailer')
+    vid_id = re.findall(r'watch\?v=(\S{11})', html.read().decode())
+
+    # Below we verify that the video contains nudity or not
+    # os.environ["OAUTHLIB_INSECURE_TRANSPORT"] = "1"
+    # api_service_name = "youtube"
+    # api_version = "v3"
+    # youtube = googleapiclient.discovery.build(api_service_name, api_version, developerKey = 'AIzaSyCLfeDKMIRoTZ9JsDGALP3gbNnTb-3DLTQ')
+    # request = youtube.videos().getRating(id=str(vid_id[0]))
+    # response = request.execute()
+    # st.write(str(response))
+
+    trailer_res = 'https://www.youtube.com/watch?v=' + vid_id[0]
+    st_player(trailer_res)

menu/About.py

@@ -0,0 +1,26 @@
+import streamlit as st
+
+def about():
+    #about page title
+    st.title("Trends Analytics")
+    # company behind the app infomation
+    about_movieXplorer, movieXplorer_logo, = st.columns([2, 3])
+
+    with about_movieXplorer:
+        st.write("**About Trends Analytics**.")
+        st.info("Founded in 2022, Trends Analytics is a data driven company which provides data and business solutions. " +
+                "We use Artificial Intelligence (AI) and Advanced Analytics to enable more personalised " +
+                "solutions for your business needs. We are passionate about Artificial Intelligence and its impact in the " +
+                "future of our society. We help optimise  operations, superior customer experiences and innovative business models. " +
+                "We have a team of certified Data Professionals, which consists of Data Scientists, Engineers, Analysts and Project Managers.")
+        st.markdown("")
+        #app information
+        st.write("**About Movie Xplorer**.")
+        st.info("Movie Xplorer is a movie recommendation application which uses collaborative and content-based algorithms to recommend " +
+                "movies to the user. The dataset is obtained from MovieLens and is maintained by the GroupLens research group in the " +
+                "Department of Computer Science and Engineering at the University of Minnesota. Additional movie content data was legally " +
+                "scraped from IMDB. The movie recommendations are catered to each user\'s needs and the user can comment about the movies they" +
+                " have watched. The video content is legally scraped from YouTube.")
+        st.markdown("")
+    with movieXplorer_logo:
+        st.image('resources/imgs/MovieXplorer.png', caption="© The Dream Team")