Update app.py

app.py

@@ -18,16 +18,15 @@ if __name__ == "__main__":
     # ---------------------------------
     # Main Application
     # ---------------------------------
-    st.title("Text Summarizer 📝")
+    st.title("Anavya-Text Summarizer 📝")
 
-    st.markdown("Creator: [Atharva Ingle](https://github.com/Gladiator07)")
-    st.markdown(
-        "Source code: [GitHub Repository](https://github.com/Gladiator07/Text-Summarizer)"
-    )
+    st.markdown("Creator: [Team Anavya](Sharvesh")
     summarize_type = st.sidebar.selectbox(
         "Summarization type", options=["Extractive", "Abstractive"]
     )
-
+    st.markdown(
+        "Children with learning disabilities face unique challenges in the education system. These challenges can include difficulties in reading, comprehending, and summarizing lengthy texts, making it harder for them to keep up with their peers. However, the use of text summarization technology can be a game-changer in improving their learning experiences
+    ")
     st.markdown(
         "Enter a text or a url to get a concise summary of the article while conserving the overall meaning. This app supports text in the following formats:"
     )
@@ -38,6 +37,7 @@ if __name__ == "__main__":
     )
     st.markdown(
         """This app supports two type of summarization:
+        
 
 1. **Extractive Summarization**: The extractive approach involves picking up the most important phrases and lines from the documents. It then combines all the important lines to create the summary. So, in this case, every line and word of the summary actually belongs to the original document which is summarized.
 2. **Abstractive Summarization**: The abstractive approach involves rephrasing the complete document while capturing the complete meaning of the document. This type of summarization provides more human-like summary"""
@@ -122,9 +122,3 @@ if __name__ == "__main__":
         # final summarized output
         st.subheader("Summarized text")
         st.info(summarized_text)
-
-        st.subheader("Rogue Scores")
-        rouge_sc = Rouge()
-        ground_truth = cleaned_txt[0] if is_url else cleaned_txt
-        score = rouge_sc.get_scores(summarized_text, ground_truth, avg=True)
-        st.code(score)