backroud changed, pages added and colours added

.vs/ProjectSettings.json

@@ -0,0 +1,3 @@
+{
+  "CurrentProjectSetting": null
+}

.vs/VSWorkspaceState.json

@@ -0,0 +1,7 @@
+{
+  "ExpandedNodes": [
+    ""
+  ],
+  "SelectedNode": "\\edsa_recommender.py",
+  "PreviewInSolutionExplorer": false
+}

edsa_recommender.py

@@ -1,112 +1,162 @@
-"""
-
-    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","Solution Overview"]
-
-    # -------------------------------------------------------------------
-    # ----------- !! 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.title("Solution Overview")
-        st.write("Describe your winning approach on this page")
-
-    # You may want to add more sections here for aspects such as an EDA,
-    # or to provide your business pitch.
-
-
-if __name__ == '__main__':
-    main()
+"""
+
+    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","Solution Overview","Business Proposal", "About Us" ,"Contact Us"]
+
+    # -------------------------------------------------------------------
+    # ----------- !! THIS CODE MUST NOT BE ALTERED !! -------------------
+    # -------------------------------------------------------------------
+    page_selection = st.sidebar.selectbox("Menu", page_options)
+    if page_selection == "Recommender System":
+        # Header contents
+        st.write('# Movie Recommender Engine')
+        st.write('### Videoligy')
+        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.title("Solution Overview")
+        st.image('resources/imgs/first.jpg', use_column_width= True)
+        st.markdown(open('resources/About_solution.md').read())
+        st.image('resources/imgs/third.jpg', use_column_width= True)
+        st.markdown(open('resources/About2.md').read())
+        st.image('resources/imgs/fourth.png', use_column_width= True)
+        st.markdown(open('resources/About3.md').read())
+        st.image('resources/imgs/fifth.png', use_column_width= True)
+        st.markdown(open('resources/About4.md').read())
+        st.markdown(open('resources/examples.md').read())
+        st.image('resources/imgs/netflix.jpg', use_column_width= True)
+        st.markdown(open('resources/benefits.md').read())
+
+    if page_selection == "Business Proposal":
+        st.title("Why our Product")
+        st.markdown(open("resources/Pages/Business.md").read())
+
+
+    if page_selection == "Contact Us":
+
+        #Adding sidebar image
+        #image = Image.open("resources/LOGOFINAL.png")
+
+        with st.form("form1", clear_on_submit=True):
+            st.subheader("Get in touch with us")
+            name = st.text_input("Enter full name")
+            email = st.text_input("Enter email")
+            message = st.text_area("Message")
+
+            submit = st.form_submit_button("Submit Form")
+            if submit:
+                st.write("Your form has been submitted and we will be in touch 🙂")
+
+
+
+    page_bg_img = f"""
+    <style>
+    [data-testid="stAppViewContainer"] > .main {{
+    background-image: url("https://images.unsplash.com/photo-1535303311164-664fc9ec6532?ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D&auto=format&fit=crop&w=1887&q=80");
+    background-size: 100%;
+    background-position: top;
+    background-repeat: no-repeat;
+    background-attachment: scroll;
+    color: black;
+    display : flex;
+    justify-content: centre;
+    align-items: centr;
+    }}
+
+    [data-testid="stHeader"].main
+    </style>"""
+
+    st.markdown(page_bg_img , unsafe_allow_html=True)
+    # You may want to add more sections here for aspects such as an EDA,
+    # or to provide your business pitch.
+
+
+if __name__ == '__main__':
+    main()