Update app.py

app.py

@@ -41,68 +41,61 @@ with st.sidebar:
 
 
 
-st.subheader ("Job Description", divider = "red")
-
-txt = st.text_area("Paste the job description and then press Ctrl + Enter", key = "text 1")
+st.subheader("Job Description", divider="red")
+txt = st.text_area("Paste the job description and then press Ctrl + Enter", key="text 1")
 job = pd.Series(txt, name="Text")
 
-st.subheader("Candidate Profile 1", divider = "green")
-
-
+st.subheader("Candidate Profile 1", divider="green")
 if 'upload_count' not in st.session_state:
     st.session_state['upload_count'] = 0
 
 max_attempts = 3
-
 if st.session_state['upload_count'] < max_attempts:
     uploaded_files = st.file_uploader(
         "Upload your resume", accept_multiple_files=True, type="pdf", key="candidate 1"
     )
-
-if uploaded_files:
-    st.session_state['upload_count'] += 1
-    for uploaded_file in uploaded_files:
-        pdf_reader = PdfReader(uploaded_file)
-        text_data = ""
-        for page in pdf_reader.pages:
-            text_data += page.extract_text()
-            data = pd.Series(text_data, name = 'Text')
+    if uploaded_files:
+        st.session_state['upload_count'] += 1
+        for uploaded_file in uploaded_files:
+            pdf_reader = PdfReader(uploaded_file)
+            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", "year"]
+            labels = ["person", "country", "organization", "role", "skills", "year"]
             entities = model.predict_entities(text_data, labels)
             df = pd.DataFrame(entities)
-            
+
             st.title("Profile of candidate 1")
             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")
-        
-       
+                             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)
             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)
-            
+
             st.subheader("Measuring similarity between keywords of candidate profile 1 and job description")
-            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")
-            
+            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:]):
                 st.write(f"Similarity of job description with candidate profile 1. {i + 1}: {similarity_score:.4f}")
-                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.")
-
+                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}).")
-    
-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.warning(f"You have reached the maximum upload attempts ({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).")