Update app.py

app.py

@@ -18,10 +18,10 @@ with st.sidebar:
     This app accepts files in .pdf formats.
     
     **How to Use**
-    Paste the job description first. Then, upload your resume to retrieve the results. You can upload up to 10 resumes in total.
+    Paste the job description first. Then, upload the resume of each applicant to retrieve the results. At the bottom of the app, you could upload each applicant's resume to visualize their profile as a treemap chart as well as the results in a matrix heatmap. 
     
     **Usage Limits**
-    You can request results up to 10 times in total.
+    You can request results up to 20 times in total.
     
      **Subscription Management**
     This demo app offers a one-day subscription, expiring after 24 hours. If you are interested in building your own AI Resume Analysis based on Keywords Web App, we invite you to explore our NLP Web App Store on our website. You can select your desired features, place your order, and we will deliver your custom app within five business days. If you wish to delete your Account with us, please contact us at [email protected]
@@ -40,19 +40,21 @@ with st.sidebar:
 ''')
 
 
+st.subheader("AI Resume Analysis based on Keywords App")
+st.divider()
 
 st.subheader("Job Description", divider="red")
 txt = st.text_area("Paste the job description and then press Ctrl + Enter", key="text 1")
 job = pd.Series(txt, name="Text")
 
-st.subheader("Candidate Profile 1", divider="green")
+st.subheader("Applicant Profile 1", divider="green")
 if 'upload_count' not in st.session_state:
     st.session_state['upload_count'] = 0
 
 max_attempts = 3
 if st.session_state['upload_count'] < max_attempts:
     uploaded_files = st.file_uploader(
-        "Upload your resume", accept_multiple_files=True, type="pdf", key="candidate 1"
+        "Upload Applicant's 1 resume", type="pdf", key="candidate 1"
     )
     if uploaded_files:
         st.session_state['upload_count'] += 1
@@ -69,7 +71,7 @@ if st.session_state['upload_count'] < max_attempts:
             entities = model.predict_entities(text_data, labels)
             df = pd.DataFrame(entities)
 
-            st.title("Profile of candidate 1")
+            
             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))
@@ -81,15 +83,19 @@ if st.session_state['upload_count'] < max_attempts:
             cosine_sim_matrix = cosine_similarity(tfidf_matrix)
             cosine_sim_df = pd.DataFrame(cosine_sim_matrix)
 
-            st.subheader("Measuring similarity between keywords of candidate profile 1 and job description")
+            
             fig = px.imshow(cosine_sim_df, text_auto=True,
                             labels=dict(x="Keyword similarity", y="Resumes", color="Productivity"),
                             x=['Resume 1', 'Jon Description'],
                             y=['Resume 1', 'Job Description'])
             st.plotly_chart(fig, key="figure 2")
 
+        
+                
+            
             for i, similarity_score in enumerate(cosine_sim_matrix[0][1:]):
-                st.write(f"Similarity of job description with candidate profile 1. {i + 1}: {similarity_score:.4f}")
+                with st.popover("See result"):
+                    st.write(f"Similarity of job description with Applicant's 1 resume based on keywords {i + 1}: {similarity_score:.2f}")
                 st.info(
                     "A score closer to 1 (0.80, 0.90) means higher similarity between candidate profile 1 and job description. A score closer to 0 (0.20, 0.30) means lower similarity between candidate profile 1 and job description.")
 else: