Update app.py

app.py

@@ -65,16 +65,7 @@ if st.session_state['upload_count'] < max_attempts:
             data = pd.Series(text_data, name='Text')
             frames = [job, data]
             result = pd.concat(frames)
-            model = GLiNER.from_pretrained("urchade/gliner_base")
-            labels = ["person", "country", "organization", "role", "skills"]
-            entities = model.predict_entities(text_data, labels)
-            df = pd.DataFrame(entities)
-
             
-            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")
 
             vectorizer = TfidfVectorizer()
             tfidf_matrix = vectorizer.fit_transform(result)
@@ -82,16 +73,6 @@ if st.session_state['upload_count'] < max_attempts:
             cosine_sim_matrix = cosine_similarity(tfidf_matrix)
             cosine_sim_df = pd.DataFrame(cosine_sim_matrix)
 
-            
-            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:]):
                 with st.popover("See result"):
                     st.write(f"Similarity of job description with Applicant's 1 resume based on keywords: {similarity_score:.2f}")