Update app.py

app.py

@@ -125,12 +125,13 @@ if st.session_state['upload_count'] < max_attempts:
             entities = model.predict_entities(text_data, labels)
             df = pd.DataFrame(entities)
 
-            tab1, tab2, tab3 = st.tabs(["Applicant's Profile", "Similarity"])
-            with tab1:
-                fig = px.treemap(entities, path=[px.Constant("all"), 'text', 'label'],
+            tab1, tab2 = st.tabs(["Applicant's Profile", "Similarity"])
+            with st.spinner("Wait for it...", show_time=True):
+                with tab1:
+                    fig = px.treemap(entities, path=[px.Constant("all"), 'text', 'label'],
                              values='score', color='label')
-                fig.update_layout(margin=dict(t=50, l=25, r=25, b=25))
-                st.plotly_chart(fig, key="figure 1")
+                    fig.update_layout(margin=dict(t=50, l=25, r=25, b=25))
+                    st.plotly_chart(fig, key="figure 1")
 
                 vectorizer = TfidfVectorizer()
                 tfidf_matrix = vectorizer.fit_transform(result)
@@ -138,20 +139,17 @@ if st.session_state['upload_count'] < max_attempts:
                 cosine_sim_matrix = cosine_similarity(tfidf_matrix)
                 cosine_sim_df = pd.DataFrame(cosine_sim_matrix)
 
-            with tab2:
-                fig = px.imshow(cosine_sim_df, text_auto=True,
+               with tab2:
+                   fig = px.imshow(cosine_sim_df, text_auto=True,
                             labels=dict(x="Keyword similarity", y="Resumes", color="Productivity"),
                             x=['Resume', 'Jon Description'],
                             y=['Resume', 'Job Description'])
-                st.plotly_chart(fig, key="figure 2")
+                   st.plotly_chart(fig, key="figure 2")
 
-        
-                
-            
-            for i, similarity_score in enumerate(cosine_sim_matrix[0][1:]):
-                with st.popover("See result"):
-                    st.write(f"Similarity of job description with Applicant's 1 resume based on keywords: {similarity_score:.2f}")
-                st.info(
+                   for i, similarity_score in enumerate(cosine_sim_matrix[0][1:]):
+                       with st.popover("See result"):
+                           st.write(f"Similarity of job description with Applicant's 1 resume based on keywords: {similarity_score:.2f}")
+                           st.info(
                     "A score closer to 1 (0.80, 0.90) means higher similarity between Applicant's 1 resume and job description. A score closer to 0 (0.20, 0.30) means lower similarity between Applicant's 1 resume and job description.")
 else:
     st.warning(f"You have reached the maximum upload attempts ({max_attempts}).")