Update app.py

app.py

@@ -110,44 +110,36 @@ if st.session_state['upload_count'] < max_attempts:
     if uploaded_files:
         st.session_state['upload_count'] += 1
         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()
-
-    job_desc_series = pd.Series([job_desc], name='Text')  # Convert job_desc to a Series
-    data = pd.Series([text_data], name='Text')        # Ensure text_data is also a Series
-
-    frames = [job_desc_series, data]
-    result = pd.concat(frames, ignore_index=True)  # Concatenate along rows, reset index
-
-    model = GLiNER.from_pretrained("urchade/gliner_base")
-    labels = ["person", "country", "organization", "role", "skills"]
-    entities = model.predict_entities(text_data, labels)
-    df = pd.DataFrame(entities)
-    st.subheader("Applicant's Profile", divider = "orange")
-    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")
-
-            
-    job_embedding = model.encode([job_desc])
-    resume_embedding = model.encode([text_data])
-
-                
-    similarity_score = model.similarity(job_embedding, resume_embedding)[0][0]
-            
-                  
-    st.subheader("Similarity between Applicant's Profile and Job Description", divider = "orange")
-
-    fig = px.imshow(similarity_score, 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")
+            time.sleep(2)
+            pdf_reader = PdfReader(uploaded_files)
+            text_data = ""
+            for page in pdf_reader.pages:
+                text_data += page.extract_text()
+            job_desc_series = pd.Series([job_desc], name='Text')  # Convert job_desc to a Series
+            data = pd.Series([text_data], name='Text')        # Ensure text_data is also a Series
+            frames = [job_desc_series, data]
+            result = pd.concat(frames, ignore_index=True)  # Concatenate along rows, reset index
+            model = GLiNER.from_pretrained("urchade/gliner_base")
+            labels = ["person", "country", "organization", "role", "skills"]
+            entities = model.predict_entities(text_data, labels)
+            df = pd.DataFrame(entities)
+            st.subheader("Applicant's Profile", divider = "orange")
+            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")
+            job_embedding = model.encode([job_desc])
+            resume_embedding = model.encode([text_data])
+            similarity_score = model.similarity(job_embedding, resume_embedding)[0][0]
+            st.subheader("Similarity between Applicant's Profile and Job Description", divider = "orange")
+            fig = px.imshow(similarity_score, 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")
 else:
     st.warning(f"Maximum upload attempts has been reached ({max_attempts}).")
     if 'upload_count' in st.session_state and st.session_state['upload_count'] > 0:
-        st.info(f"Files uploaded {st.session_state['upload_count']} time(s).")
+        st.info(f"Files uploaded {st.session_state['upload_count']} time(s).")
+
+