Obot joshua streamlit app

edsa_recommender.py

@@ -1,112 +1,214 @@
-"""
-
-    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 = ["Home","Recommender System","Solution Overview", 'About us', "FAQ"]
+
+    # -------------------------------------------------------------------
+    # ----------- !! THIS CODE MUST NOT BE ALTERED !! -------------------
+    # -------------------------------------------------------------------
+    st.sidebar.image('resources/imgs/4.png',use_column_width=False)
+    st.sidebar.subheader("Movie Recommender Engine 💡")
+    page_selection = st.sidebar.selectbox("Choose Option", page_options)
+    if page_selection == "Recommender System":
+        # Header contents
+        c1, c2 = st.columns([1, 2])
+        with c1:
+
+            st.image(
+                "resources/imgs/logo.png",
+                width=250,)
+        with c2:
+            st.title("Movie Recommender Engine 💡")
+        # We need to set up session state via st.session_state so that app interactions don't reset the app.
+        if not "valid_inputs_received" in st.session_state:
+            st.session_state["valid_inputs_received"] = False
+        st.write('# ')
+        st.write("### let's Get You started! Get Your Recommendations Below")
+        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("We the FSC_TECH company data science company offers solutions that leverage data analytics, machine learning,")
+        st.write(" and artificial intelligence to solve complex problems, also recommend and provide valuable insights to clients. ")
+        st.write("Here's a detailed overview of the various components and processes involved in the operations of FSC_TECH company:")
+        st.write("A: Problem Identification and Consultation")
+        st.write("B: Data Acquisition and Integration")
+        st.write("C: Exploratory Data Analysis (EDA)")
+        st.write("D: Feature Engineering")
+        st.write("E: Model Development")
+        st.write("F: Model Evaluation and Validation")
+        st.write("G: Deployment and Integration")
+        st.write("H: Continuous Monitoring and Maintenance")
+        st.write("I: Reporting and Visualization")
+        st.write("J: Ongoing Support and Collaboration")
+
+    if page_selection == "Home":
+        st.image('resources/imgs/logo.png',use_column_width=True)
+        st.title("Home")
+        st.write("FSC_TECH company are into building recommendation system for companies and also offers the best services for our clients.")
+
+#         with st.expander("Click here to view the raw data"):
+#             st.dataframe(filtered_df)
+
+
+    # Building out the About Us page
+    if page_selection == "About us":
+        st.info("FSC_TECH company")
+        st.write("FSC_TECH company provides data and analytics solutions that enable clients to gain valuable insights from their data, make informed decisions in a timely manner, and consistently stay ahead of the competition.")
+
+        st.info("Our Vision:")
+        st.write("To be the lead Tech Solution Plug")
+
+        st.info("Meet the team")
+        image_width = 150
+        column_11, column_12 = st.columns([2, 2])
+        with column_11:
+            st.markdown("Fumani Thibela")
+            st.image(
+                "resources/imgs/Fumani.jpeg", width=image_width
+            )
+        with column_12:
+            st.markdown("Confidence")
+            st.image("resources/imgs/Confidence.jpeg", width=image_width)
+
+        column_11, column_12 = st.columns([2, 2])
+        with column_11:
+            st.markdown("Stephen")
+            st.image("resources/imgs/Stephen.jpeg", width=image_width)
+        with column_12:
+            st.markdown("Joshua")
+        st.image("resources/imgs/Joshua.jpeg", width=image_width)
+#         Fumani = st.image('resources/imgs/Fumani.JPEG')
+#         Fumani1 = Fumani.size((50, 55))
+#         Confidence = st.image('resources/imgs/Confidence.JPEG')
+#         Confidence1 = Confidence.resize((50, 55))
+#         Joshua = st.image('resources/imgs/Joshua.JPEG')
+#         Joshua1 = Joshua.resize((50, 55))
+#         Stephen = st.image('resources/imgs/Stephen.JPEG')
+#         Stephen1 = Stephen.resize((50, 55))
+#         #Collete = Image.open('resources/imgs/Collete.PNG')
+#         C#ollete1 = Collete.resize((150, 155))
+
+#         col1, col2, col3, col4 = st.columns(4)
+#         with col2:
+#             st.image(Fumani1, width=50, caption="Fumani: Team Lead")
+#         with col3:
+#             st.image(Confidence1, width=50, caption="Confidence: Technical Lead")
+
+#         col1, col2, col3 = st.columns(3)
+
+#         with col1:
+#             st.image(Stephen1, width=50, caption="Stephen: Project Manager")
+#         with col2:
+#             st.image(Joshua1, width=50, caption="Joshua: Data Scientist")
+
+        #with col3:
+           # st.image(Collete1, width=150, caption="Collete: Data Scientist")
+
+    # You may want to add more sections here for aspects such as an EDA,
+    # or to provide your business pitch.
+
+    # Building out the FAQ page
+    if page_selection == "FAQ":
+        c1, c2 = st.columns([1, 2])
+        with c1:
+
+            st.image(
+                "resources/imgs/logo.png",
+                width=250,)
+        with c2:
+            st.title("Frequently Asked Questions")
+        #st.title("")
+        st.write("")
+
+if __name__ == '__main__':
+    main()