fixes]

app.py

@@ -16,6 +16,7 @@ from sentence_transformers import SentenceTransformer
 import plotly.express as px
 import pandas as pd
 from sklearn.decomposition import PCA
+import time
 
 st.set_page_config(page_title="Mental disorder by description", page_icon="🤖")
 
@@ -27,10 +28,10 @@ def convert_string_to_numpy_array(s):
 #load the model
 @st.cache_resource
 def get_models():
-  st.write('Loading the model...')
+  st.write('*Loading the model...*')
   name = "stsb-bert-large"
   model = SentenceTransformer(name)
-  st.write("The app is loaded and ready to use!")
+  st.write("*The app is loaded and ready to use!*")
   lemmatizer = WordNetLemmatizer()
   return model, lemmatizer
 
@@ -134,33 +135,29 @@ def vis_results_3d(input_embed):
     # creating an interactive 3D scatter plot
     fig = px.scatter_3d(combined_3d, x='PC1', y='PC2', z='PC3', text='Name', color='Type', symbol='Type', width=800, height=800)
     fig.show()
-# with text_spinner_placeholder:
-#     with st.spinner("Please wait while your Tweet is being generated..."):
-#         mood_prompt = f"{mood} " if mood else ""
-#         if style:
-#             twitter = twe.Tweets(account=style)
-#             tweets = twitter.fetch_tweets()
-#             tweets_prompt = "\n\n".join(tweets)
-#             prompt = (
-#                 f"Write a {mood_prompt}Tweet about {topic} in less than 120 characters "
-#                 f"and in the style of the following Tweets:\n\n{tweets_prompt}\n\n"
-                    
+
 # Configure Streamlit page and state
 st.title("Detect your character's mental disorder!")
 st.markdown(
-    "This mini-app predicts top-5 most likely mental disorder based on your description. The more information you provide, the more informative the results will be. \
-    Note that this app can't be used for diagnostic purposes."
+    "This mini-app predicts top-5 most likely mental disorder based on your description. The more information you provide, the more informative the results will be."
 )
-
+st.caption("Note that this app can't be used for diagnostic purposes.")
 input = st.text_input(label="Your description", placeholder="Insert a description of your character")
 if input:
     input_embed = model.encode(input)
     sim_score = similarity_top(input_embed, icd_embeddings)
-    st.write(sim_score)
-
+    i = 1
+    for dis, value in sim_score:
+        st.write(f":green[Prediction number] {i}:")
+        st.write(f"{dis} (similarity score:", value, ")")
+        i+= 1
+        
+    text_spinner_placeholder = st.empty()
+    with st.spinner("Please wait while your visualizations are being generated..."):
+        time.sleep(5)
     vis_results_2d(input_embed)
     vis_results_3d(input_embed)
     
     
 
-text_spinner_placeholder = st.empty()
+