Update app.py

app.py

@@ -113,10 +113,10 @@ if st.session_state['upload_count'] < max_attempts:
         
                 st.subheader("A score closer to 1 means closer match")
         
-                fig = px.imshow(cosine_sim_df, text_auto=True, labels=dict(x="Cosine similarity", y="Text", color="Productivity"),
+                fig2 = px.imshow(cosine_sim_df, text_auto=True, labels=dict(x="Cosine similarity", y="Text", color="Productivity"),
                         x=['text1', 'Jon Description'],
                         y=['text1', 'Job Description'])
-                st.plotly_chart(fig)
+                st.plotly_chart(fig2, key = "fig2")
 
                 st.subheader("Cosine Similarity Scores (Job Description vs. Resumes):")
                 for i, similarity_score in enumerate(cosine_sim_matrix[0][1:]):
@@ -161,10 +161,10 @@ if st.session_state['upload_count'] < max_attempts:
                 df = pd.DataFrame(entities)
                 st.dataframe(entities)
                 st.dataframe(df)
-                fig2 = px.treemap(entities, path=[px.Constant("all"), 'text', 'label'],
+                fig3 = px.treemap(entities, path=[px.Constant("all"), 'text', 'label'],
                 values='score', color='label')
-                fig2.update_layout(margin = dict(t=50, l=25, r=25, b=25))
-                st.plotly_chart(fig2, key = "fig2")
+                fig3.update_layout(margin = dict(t=50, l=25, r=25, b=25))
+                st.plotly_chart(fig3, key = "fig3")
                 vectorizer = TfidfVectorizer()
                 tfidf_matrix = vectorizer.fit_transform(result)
                 tfidf_df = pd.DataFrame(tfidf_matrix.toarray(), columns=vectorizer.get_feature_names_out())
@@ -179,10 +179,10 @@ if st.session_state['upload_count'] < max_attempts:
         
                 st.subheader("A score closer to 1 means closer match")
         
-                fig = px.imshow(cosine_sim_df, text_auto=True, labels=dict(x="Cosine similarity", y="Text", color="Productivity"),
+                fig4 = px.imshow(cosine_sim_df, text_auto=True, labels=dict(x="Cosine similarity", y="Text", color="Productivity"),
                         x=['text1', 'Jon Description'],
                         y=['text1', 'Job Description'])
-                st.plotly_chart(fig)
+                st.plotly_chart(fig4, key ="fig4")
 
                 st.subheader("Cosine Similarity Scores (Job Description vs. Resumes):")
                 for i, similarity_score in enumerate(cosine_sim_matrix[0][1:]):