Update app.py

app.py

@@ -79,7 +79,7 @@ for uploaded_file in uploaded_files:
         fig1 = px.treemap(entities, path=[px.Constant("all"), 'text', 'label'],
                 values='score', color='label')
         fig1.update_layout(margin = dict(t=50, l=25, r=25, b=25))
-        st.plotly_chart(fig1, key = "figure 1")
+        st.plotly_chart(fig1)
        
             
         
@@ -96,12 +96,12 @@ for uploaded_file in uploaded_files:
         fig2 = 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(fig2, key = "figure 2")
+        st.plotly_chart(fig2)
 
         
         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}")
-            st.write("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.")
+            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:
     st.warning(f"You have reached the maximum URL attempts ({max_attempts}).")
@@ -151,7 +151,7 @@ for uploaded_file in uploaded_files:
         fig3 = px.treemap(entities, path=[px.Constant("all"), 'text', 'label'],
                 values='score', color='label')
         fig3.update_layout(margin = dict(t=50, l=25, r=25, b=25))
-        st.plotly_chart(fig3, key = "chart 3")
+        st.plotly_chart(fig3)
        
             
         
@@ -168,7 +168,7 @@ for uploaded_file in uploaded_files:
         fig4 = 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(fig4, key = "chart 4")
+        st.plotly_chart(fig4)
 
         
         for i, similarity_score in enumerate(cosine_sim_matrix[0][1:]):