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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+pictures/karim[[:space:]]1.jpg filter=lfs diff=lfs merge=lfs -text

pages/01_BERT_Topic_Modeling.py

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+import streamlit as st
+
+def bert_intro():
+    st.title("Topic Modeling with BERT")
+    st.markdown("BERT (BERTopic): Explanation of the advanced NLP technique used for analyzing the data, and its application in this project.")
+
+    st.header("Process Flow: Step-by-step breakdown of the analysis process, from data gathering to insights extraction.")
+
+def youth_classification():
+    st.title("Youth Classification")
+
+def sentiment_analysis():
+    st.title("Sentiment Analysis")
+
+def bert_topic_modeling():
+    st.title("Topic Modeling")
+
+sidebar_pages = ["Introduction", "Youth Classification", "Sentiment Analysis", "Topic Modeling"]
+def main():
+    st.sidebar.title("Navigation")
+    page = st.sidebar.selectbox("Select page:", sidebar_pages)
+
+    if page == "Introduction":
+        bert_intro()
+    elif page == "Youth Classification":
+        youth_classification()
+    elif page == "Sentiment Analysis":
+        sentiment_analysis()
+    elif page == "Topic Modeling":
+        bert_topic_modeling()
+
+if __name__ == "__main__":
+    main()

pages/02_Initial_Topic_Modeling.py

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+import streamlit as st
+import streamlit.components.v1 as components
+
+st.title("Topic Modeling")
+
+def introduction():
+    st.title("Research & Methodology")
+    st.markdown("LDA as Baseline: "
+    "Describe the use of Latent Dirichlet Allocation as a baseline for comparison and understanding.")
+    st.markdown("Process Flow: Step-by-step breakdown of the analysis process, from data gathering to insights extraction.")
+
+    # Display the LDA visualization HTML file
+    components.html(open('lda_visualization.html', 'r').read(), height=800)
+
+
+def lda_page():
+    st.title("Insights & Findings of Latent Dirichlet Allocation (LDA) Model")
+    st.markdown("Priliminary Results: findings, notebooks, documentation")
+    st.markdown("Visualizations including pyLDAvis: ")
+    st.markdown("Key Trends: ")
+
+sidebar_pages = ["Introduction", "Latent Dirichlet Allocation"]
+def main():
+    st.sidebar.title("Navigation")
+    page = st.sidebar.selectbox("Select a page:", sidebar_pages)
+
+    if page == "Introduction":
+        introduction()
+    elif page == "Latent Dirichlet Allocation":
+        lda_page()
+
+if __name__ == "__main__":
+    main()

pages/03_Contact.py

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+import streamlit as st
+from PIL import Image # for pictures
+
+st.title('Contact')
+
+st.markdown("Project Team: Introduction to the project team, including brief bios or links to their profiles.")
+st.markdown("Contact Info: social media, contact form, emails")
+
+
+### Pictures
+photo_karim = Image.open(r"C:pictures\karim 1.jpg")
+st.image(photo_karim, caption="horizontal Picture of Karim (default width)", width=None, use_column_width=None, clamp=False, channels="RGB", 
+         output_format="auto", use_container_width=False)
+st.header("FAQ")