Update app.py

app.py

@@ -48,12 +48,6 @@ combined_features = vectorizer.transform(combined_features)
 similarity = cosine_similarity(combined_features)
 
 # Streamlit app
-st.set_page_config(
-    page_title="Course Recommendation App",
-    page_icon="✅",
-    layout="wide",
-)
-
 st.title('Learning Pathway Index Course Recommendation')
 user_input = st.text_input('Enter What You Want to Learn : ')
 
@@ -68,19 +62,12 @@ if user_input:
         sorted_similar_course = sorted(similarity_score, key=lambda x: x[1], reverse=True)
 
         st.subheader('Courses suggested for you:')
-        with st.beta_container():
-            col1, col2 = st.beta_columns(2)
-            for i, course in enumerate(sorted_similar_course[:30], start=1):
-                index = course[0]
-                title_from_index = lpi_df.loc[index, 'Module']
-                if i % 2 == 0:
-                    with col2:
-                        st.write(f"{i}. {title_from_index}")
-                else:
-                    with col1:
-                        st.write(f"{i}. {title_from_index}")
+        for i, course in enumerate(sorted_similar_course[:30], start=1):
+            index = course[0]
+            title_from_index = lpi_df.loc[index, 'Module']
+            st.write(f"{i}. {title_from_index}")
 
         if len(sorted_similar_course) == 0:
-            st.warning('No close matches found.')
+            st.write('No close matches found.')
     else:
-        st.warning('No close matches found.')
+        st.write('No close matches found.')