Update app.py

app.py

@@ -111,39 +111,27 @@ if st.session_state['upload_count'] < max_attempts:
     uploaded_files = st.file_uploader("Upload Applicant's resume", type="pdf")
     if uploaded_files:
         st.session_state['upload_count'] += 1
-        
-        pdf_reader = PdfReader(uploaded_files)
-        text_data = ""
-        for page in pdf_reader.pages:
-            text_data += page.extract_text()
+
+        with st.spinner("Wait for it...", show_time=True):
+            time.sleep(2)
+            pdf_reader = PdfReader(uploaded_files)
+            text_data = ""
+            for page in pdf_reader.pages:
+                text_data += page.extract_text()
             
-            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)
-
-            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'],
+                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")
-
-                with tab2:
-                    vectorizer = TfidfVectorizer()
-                    tfidf_matrix = vectorizer.fit_transform(result)
-                    tfidf_df = pd.DataFrame(tfidf_matrix.toarray(), columns=vectorizer.get_feature_names_out())
-                    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', 'Jon Description'],
-                            y=['Resume', 'Job Description'])
-                    st.plotly_chart(fig, key="figure 2")
+                fig.update_layout(margin=dict(t=50, l=25, r=25, b=25))
+                st.plotly_chart(fig)
+ 
+             
 
                 
 else: