Commit

Pages/About.py

@@ -1,46 +1,47 @@
 import streamlit as st
 
 
-# Display the about page of the app with information about the creator, code, and data
+# Display the about page of the app with information about the creator, code and data.
 def about_page():
-    st.title("About Us")
     with st.container():
-        col = st.columns([1, 1])
+        col = st.columns([1.5, 1])
         with col[0]:
+            st.title("About Us")
             st.write("\n")
             st.write("\n")
-            st.write("\n")
-            st.write(
-                "This app was created by [Harshit Singh](https://harsh502s.github.io), Poorvi Singh and Samruddhi Raskar as a part of their MSc Data Science 3rd semester project."
+            st.markdown(
+                """##### This app was created by [Harshit Singh](https://harsh502s.github.io), Poorvi Singh and Samriddhi Raskar as a part of their MSc Data Science 3rd semester project."""
             )
             st.write("\n")
-            st.write("The code for this app can be found [here]( ).")
+            st.markdown(
+                """##### The code for this app can be found [here](https://github.com/Harsh502s/Autonomous-Text-Tagging-System)""",
+                unsafe_allow_html=True,
+            )
             st.write("\n")
-            st.write(
-                "The data on which these models are trained can be found [here](https://www.kaggle.com/datasets/harsh502s/stackexchange-tag-dataset)."
+            st.markdown(
+                """##### The data on which these models are trained can be found [here](https://www.kaggle.com/datasets/harsh502s/stackexchange-tag-dataset/data).""",
+                unsafe_allow_html=True,
             )
         with col[1]:
-            st.image("Group.svg", width=300)
+            st.image("Group.svg", width=325)
 
     st.write("\n")
     st.write("\n")
 
     with st.container():
-        col = st.columns([1, 2])
+        col = st.columns([1.5, 2])
         with col[0]:
             st.image("Robot.svg", width=350)
         with col[1]:
             st.title("Models Used:")
-            st.write(
-                """1. [BERTopic](https://maartengr.github.io/BERTopic/api/bertopic.html#:~:text=BERTopic%20is%20a%20topic%20modeling,words%20in%20the%20topic%20descriptions.)
-                is a topic modeling technique that leverages BERT embeddings and c-TF-IDF to create dense clusters allowing for easily interpretable topics whilst keeping important words in the topic descriptions."""
+            st.markdown(
+                """###### 1. BERTopic is a topic modeling technique that leverages BERT embeddings and c-TF-IDF to create dense clusters allowing for easily interpretable topics whilst keeping important words in the topic descriptions."""
             )
-            st.write(
-                """2. [KeyBERT](https://maartengr.github.io/KeyBERT/#:~:text=KeyBERT%20is%20a%20minimal%20and,most%20similar%20to%20a%20document.)
-                is a minimal and easy-to-use keyword extraction technique that leverages BERT embeddings to create keywords and keyphrases that are most similar to a document."""
+            st.markdown(
+                """###### 2. KeyBERT is a minimal and easy-to-use keyword extraction technique that leverages BERT embeddings to create keywords and keyphrases that are most similar to a document."""
             )
-            st.write(
-                """3. Convolutional Neural Networks (CNNs) are used for text classification. CNNs can identify patterns in text data, such as bigrams, trigrams, or n-grams. CNNs are translation invariant, so they can detect these patterns regardless of their position in the sentence."""
+            st.markdown(
+                """###### 3. Convolutional Neural Networks (CNNs) are used for text classification. CNNs can identify patterns in text data, such as bigrams, trigrams, or n-grams. CNNs are translation invariant, so they can detect these patterns regardless of their position in the sentence."""
             )
 
 

Pages/Models.py

@@ -175,6 +175,16 @@ def semi_unsupervised_page_keybert():
 
 # Display the model page of the app
 def model_page():
+    stype_for_page = """
+    <style>
+    button.st-emotion-cache-c766yy.ef3psqc11:hover {
+    scale: 1.07;
+    transition-duration: 0.3s;
+    }
+    </style>
+    """
+    st.markdown(stype_for_page, unsafe_allow_html=True)
+
     st.title("Select a model to use:")
     with st.container():
         tab1, tab2, tab3 = st.tabs(
@@ -187,7 +197,7 @@ def model_page():
         with tab3:
             unsupervised_page_bertopic()
     with st.container():
-        with st.expander("Example Texts", expanded=True):
+        with st.expander("Example Texts", expanded=False):
             st.markdown(
                 """
                 ### Here are 5 examples of questions from Stack Exchange. Try them out!