Update app.py

app.py

@@ -28,8 +28,12 @@ def recommend(index):
 st.set_page_config(page_title='DiscoverCourses', page_icon=':bird:')
 st.header('DiscoverCourses')
 st.write('')
-st.write("Do you like the tech + social impact focus of CS51? Excited by film-centered courses like FILMEDIA245B? Saw a cool study-abroad course (OSPISTAN76) and want to study that topic on campus? \nEnter DiscoverCourses. Just pick a course and get dozens of recommendations for similar courses based on titles or descriptions.")
+st.write("Do you like the tech + social impact focus of CS51? Excited by film-centered courses like FILMEDIA245B? Saw a cool study-abroad course (OSPISTAN76) and want to study that topic on campus?")
 st.write('')
+st.write("Enter DiscoverCourses. Just pick a course and get dozens of recommendations for similar courses based on titles or descriptions. Give it a go! If you have any thoughts on DiscoverCourses (or project ideas or a book recommendation or really anything), shoot me an email at [email protected].")
+st.write('')
+
+st.markdown('<style> a:link {color: white;background-color: transparent;text-decoration: none;}</style>',unsafe_allow_html=True)
 
 selected_course = st.selectbox('Pick a course from the dropdown (or click on it and start typing to search).',course_title_list)
 #st.write("Description: "+coursedf.iloc[np.where((coursedf['ref']+": "+coursedf['title'])==selected_course)[0][0],3])
@@ -63,7 +67,7 @@ if maincol2.button('Discover by description',use_container_width=True):
             st.write(coursedf.iloc[index,3]) #Using the new coursedf because it has proper descriptions for each course
         link1 = "[ExploreCourses ↗](https://explorecourses.stanford.edu/search?q="+course_id+"+"+result.replace(" ","+")+")"
         link2 = "[Carta ↗](https://carta-beta.stanford.edu/results/"+course_id+")"
-        st.markdown(":white["+link1+"   "+link2+"]", unsafe_allow_html=True)
+        st.markdown(+link1+"   "+link2, unsafe_allow_html=True)
         st.divider()
 
 st.write('© 2023 Rushank Goyal. All rights reserved. Source for the all-MiniLM-L6-v2 model: Wang, Wenhui, et al. "MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers." arXiv, 25 Feb. 2020, doi:10.48550/arXiv.2002.10957.')