Update app.py

app.py

@@ -123,9 +123,10 @@ with detailsTab:
     st.header("App details")
     
     st.markdown(
-        """
+        f"""
         - The app supports Semantic search which seeks to improve search accuracy by understanding the content of the search query in contrast to traditional search engines which only find documents based on lexical matches.
         - The corpus consists of movie plots from Hindi, Telugu, Tamil, Kannada, Bengali, Malayalam, Odiya, Marathi, Punjabi & Gujarathi languages.
+          - Corpus size:{len(df)}
         - The core idea of the retrieval: 
           - Use Bi-Encoder (Retrieval) and Cross-encoder (Re-ranker) to retrieve the search results.
             - The Bi-encoder is responsible for independently embedding the sentences and search queries into a vector space. The result is then passed to the cross-encoder for checking the relevance/similarity between the query and sentences.