Update app.py

app.py

@@ -41,12 +41,12 @@ with st.sidebar:
 
 
 
-st.subheader ("Job Description", divider = "orange")
+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 = "red")
+st.subheader("Candidate Profile 1", divider = "green")
 
 
 if 'upload_count' not in st.session_state:
@@ -61,58 +61,47 @@ if st.session_state['upload_count'] < max_attempts:
 
 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"]
-        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'],
+    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"]
+            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")
+            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)
             
-        
-        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"),
+            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")
-        
-
-        
-        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.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.")
 
 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).")
+    
+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).")
 
 
 
@@ -122,9 +111,7 @@ else:
         
 
 
-# fix else: warning
-# fix, keys
-# st.title
+