Update app.py

app.py

@@ -41,10 +41,12 @@ with st.sidebar:
 
 
 
-st.subheader("Candidate Profile 1", divider="red")
-txt = st.text_area("Paste the job description and then press Ctrl + Enter", key="text 1")
+st.subheader ("Candidate Profile 1", divider = "green")
+
+txt = st.text_area("Paste the job description and then press Ctrl + Enter", key = "text 1")
 job = pd.Series(txt, name="Text")
 
+
 if 'upload_count' not in st.session_state:
     st.session_state['upload_count'] = 0
 
@@ -54,57 +56,67 @@ 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')
-            frames = [job, data]
-            result = pd.concat(frames)
-            model = GLiNER.from_pretrained("urchade/gliner_base")
-            labels = ["person", "country", "organization", "date", "time", "role", "skills", "year"]
-            entities = model(text_data, labels=labels)
-            df = pd.DataFrame(entities)
-
-            st.subheader("Profile of candidate 1", divider="green")
-            fig1 = px.treemap(entities, path=[px.Constant("all"), 'text', 'label'],
-                            values='score', color='label')
-            fig1.update_layout(margin=dict(t=50, l=25, r=25, b=25))
-            st.plotly_chart(fig1, 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", divider="green")
-            fig2 = px.imshow(cosine_sim_df, text_auto=True,
-                            labels=dict(x="Keyword similarity", y="Resumes", color="Productivity"),
-                            x=['Resume 1', 'Job Description'],
-                            y=['Resume 1', 'Job Description'])
-            st.plotly_chart(fig2, 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.")
+
+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", "date", "time", "role", "skills", "year"]
+        entities = model.predict_entities(text_data, labels)
+        df = pd.DataFrame(entities)
+        
+
+        st.subheader("Profile of candidate 1")
+        fig1 = px.treemap(entities, path=[px.Constant("all"), 'text', 'label'],
+                values='score', color='label')
+        fig1.update_layout(margin = dict(t=50, l=25, r=25, b=25))
+        st.plotly_chart(fig1, 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")
+        fig2 = 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(fig2, 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 upload attempts ({max_attempts}).")
+    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.subheader("Candidate Profile 2", divider="red")
+st.subheader ("Candidate Profile 2", divider = "green")
+
+
 
 if 'upload_count' not in st.session_state:
     st.session_state['upload_count'] = 0
@@ -113,52 +125,74 @@ 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 2"
+        "Upload your resume in .pdf format", accept_multiple_files=True, type="pdf", key="candidate 2"
     )
-    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", "date", "time", "role", "skills", "year"]
-            entities = model(text_data, labels=labels)
-            df = pd.DataFrame(entities)
-
-            st.subheader("Profile of candidate 1", divider="green")
-            fig3 = px.treemap(entities, path=[px.Constant("all"), 'text', 'label'],
-                            values='score', color='label')
-            fig3.update_layout(margin=dict(t=50, l=25, r=25, b=25))
-            st.plotly_chart(fig3, key = "Figure 3")
-
-            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", divider="green")
-            fig4 = px.imshow(cosine_sim_df, text_auto=True,
-                            labels=dict(x="Keyword similarity", y="Resumes", color="Productivity"),
-                            x=['Resume 1', 'Job Description'],
-                            y=['Resume 1', 'Job Description'])
-            st.plotly_chart(fig4, key = "Figure 4")
-
-            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.")
+
+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", "date", "time", "role", "skills", "year"]
+        entities = model.predict_entities(text_data, labels)
+        df = pd.DataFrame(entities)
+        
+
+        
+        fig3 = px.treemap(entities, path=[px.Constant("all"), 'text', 'label'],
+                values='score', color='label')
+        fig3.update_layout(margin = dict(t=50, l=25, r=25, b=25))
+        st.plotly_chart(fig3, key = "figure 3")
+       
+            
+        
+        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)
+        
+
+        
+        
+        fig4 = px.imshow(cosine_sim_df, text_auto=True, labels=dict(x="Keyword similarity", y="Resumes", color="Productivity"),
+                        x=['Resume 2', 'Jon Description'],
+                        y=['Resume 2', 'Job Description'])
+        st.plotly_chart(fig4, key = "figure 4")
+
+        
+        for i, similarity_score in enumerate(cosine_sim_matrix[0][1:]):
+            st.write(f"Similarity with Candidate Profile. A score closer to 1 means higher similarity. {i + 1}: {similarity_score:.4f}")
+
 else:
-    st.warning(f"You have reached the maximum upload attempts ({max_attempts}).")
+    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).")
 
 
+
+
+               
+
+                
+
+
+
+
+
+
+