Commit

.streamlit/config.toml

@@ -0,0 +1,6 @@
+[theme]
+base='light'
+primaryColor="#607985"
+backgroundColor="#c7c6c6"
+secondaryBackgroundColor="#949fa2"
+textColor="#333333"

Pages/3_👋_About.py

@@ -1,28 +0,0 @@
-import streamlit as st
-
-
-# Display the about page of the app with information about the creator, code, and data
-def about_page():
-    st.header("About")
-    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.write("The code for this app can be found [here]( ).")
-    st.write(
-        "The data on which these models are trained can be found [here](https://www.kaggle.com/datasets/harsh502s/stackexchange-tag-dataset)."
-    )
-    st.subheader("Models used in this app are:")
-    st.write(
-        "1. [BERTopic](https://maartengr.github.io/BERTopic/api/bertopic.html#:~:text=BERTopic%20is%20a%20topic%20modeling,words%20in%20the%20topic%20descriptions.)"
-    )
-    st.write(
-        "2. [KeyBERT](https://maartengr.github.io/KeyBERT/#:~:text=KeyBERT%20is%20a%20minimal%20and,most%20similar%20to%20a%20document.)"
-    )
-    st.write(
-        "3. [CNN](https://www.tensorflow.org/tutorials/text/text_classification_rnn)"
-    )
-    pass
-
-
-if __name__ == "__main__":
-    about_page()

Pages/About.py

@@ -0,0 +1,48 @@
+import streamlit as st
+
+
+# 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])
+        with col[0]:
+            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.write("\n")
+            st.write("The code for this app can be found [here]( ).")
+            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)."
+            )
+        with col[1]:
+            st.image("Images/group.svg", width=300)
+
+    st.write("\n")
+    st.write("\n")
+
+    with st.container():
+        col = st.columns([1, 2])
+        with col[0]:
+            st.image("Images/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.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.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."""
+            )
+
+
+if __name__ == "__main__":
+    about_page()

app.py

@@ -5,9 +5,9 @@ from st_pages import Page, show_pages
 show_pages(
     [
         Page(r"app.py", "Home", "🏠"),
-        Page(r"Pages/1_📊_Topic Model Results.py", 'Topic Model Result',"📊"),
-        Page(r"Pages/2_🤖_Models.py", "Models", "🤖"),
-        Page(r"Pages/3_👋_About.py", "About", "👋"),
+        Page(r"Pages/Topic Model Results.py", "Topic Model Result", "📊"),
+        Page(r"Pages/Models.py", "Models", "🤖"),
+        Page(r"Pages/About.py", "About", "👋"),
     ]
 )
 
@@ -22,15 +22,26 @@ st.set_page_config(
 # Display the main page of the app with instructions on how to use it
 def main():
     st.title("Autonomous Text Tagging App")
-    st.subheader(
-        "This application shows a demo of different supervised and unsupervised approches taken in the field of NLP to give relevant tags to the text."
-    )
-    st.subheader("This is a multi-page app.")
-    st.write("1. You can navigate between pages by clicking on the sidebar.")
-    st.write("2. The Topic Modeling Results page shows the results of BERTopic.")
-    st.write("3. The Model page give a demo of all the models used in this app.")
-    st.write("4. The About page gives information about the creator, code, and data.")
-    st.divider()
+    cols = st.columns([1, 1])
+    with st.container():
+        with cols[0]:
+            st.write(
+                "A Text tagging is the process of adding metadata or labels to specific elements within a text, such as identifying and categorizing named entities, parts of speech, or sentiment."
+            )
+            st.write(
+                "This app show the results of BERTopic Model and a demo of all the models used in this project."
+            )
+            st.subheader("How to use this app:")
+            st.write("1. Select the model you want to use from the sidebar.")
+            st.write("2. Enter the text you want to tag.")
+            st.write('3. Click on the "Tag" button.')
+            st.write("4. The tags will be displayed in the output section.")
+            st.write("5. You can see the results of BERTopic Model in the sidebar.")
+            st.write("6. You can use tabs to see the visualization of the results.")
+            st.divider()
+
+        with cols[1]:
+            st.image("Images/sort.svg", width=450)
 
 
 if __name__ == "__main__":