Update app.py

app.py

@@ -25,6 +25,7 @@ from sklearn.feature_extraction.text import TfidfVectorizer
 from sklearn.metrics.pairwise import cosine_similarity
 import tempfile
 
+import plotly.express as px
 with st.sidebar:
     st.button("DEMO APP", type="primary")
    
@@ -63,7 +64,7 @@ with st.sidebar:
 
 
 
-txt = st.text_area("Job description", key="text 1")
+txt = st.text_area("Job description", key = "text 1")
 job = pd.Series(txt, name="Text")
 st.dataframe(job)
 
@@ -83,52 +84,52 @@ if st.session_state['upload_count'] < max_attempts:
             text_data = ""
             for page in pdf_reader.pages:
                 text_data += page.extract_text()
-            data = pd.Series(text_data, name='Text')
-            st.dataframe(data)
-
-            frames = [job, data]
-            result = pd.concat(frames)
-            st.dataframe(result)
-
-            model = GLiNER.from_pretrained("urchade/gliner_base")
-            labels = ["person", "country", "city", "organization", "date", "money", "percent value", "position"]
-            entities = model(text_data, labels=labels)
-            df_entities = pd.DataFrame(entities)
-            st.subheader("Extracted Entities:")
-            st.dataframe(df_entities)
-
-            fig = px.treemap(df_entities, path=[px.Constant("all"), 'word', 'entity_group'],
-                             values='score', color='entity_group')
-            fig.update_layout(margin=dict(t=50, l=25, r=25, b=25))
-            st.plotly_chart(fig)
-
-            vectorizer = TfidfVectorizer()
-            tfidf_matrix = vectorizer.fit_transform(result)
-            tfidf_df = pd.DataFrame(tfidf_matrix.toarray(), columns=vectorizer.get_feature_names_out())
-            st.subheader("TF-IDF Values:")
-            st.dataframe(tfidf_df)
-
-            cosine_sim_matrix = cosine_similarity(tfidf_matrix)
-            cosine_sim_df = pd.DataFrame(cosine_sim_matrix)
-            st.subheader("Cosine Similarity Matrix:")
-            st.dataframe(cosine_sim_df)
-
-            st.subheader("A score closer to 1 means closer match")
-            fig = px.imshow(cosine_sim_df, text_auto=True,
-                            labels=dict(x="Cosine similarity", y="Text", color="Productivity"),
-                            x=['Job Description'] + [f'Candidate {i+1}' for i in range(len(uploaded_files))],
-                            y=['Job Description'] + [f'Candidate {i+1}' for i in range(len(uploaded_files))])
-            st.plotly_chart(fig)
-
-            st.subheader("Cosine Similarity Scores (Job Description vs. Resumes):")
-            for i, similarity_score in enumerate(cosine_sim_matrix[0][1:]):
-                st.write(f"Similarity with Candidate Profile {i + 1}: {similarity_score:.4f}")
+                data = pd.Series(text_data, name = 'Text')
+                st.dataframe(data)
+                frames = [job, data]
+                result = pd.concat(frames)
+                st.dataframe(result)
+                model = GLiNER.from_pretrained("urchade/gliner_base")
+                labels = ["person", "country", "city", "organization", "date", "money", "percent value", "position"]
+                entities = model.predict_entities(text_data, labels)
+                df = pd.DataFrame(entities)
+                st.dataframe(entities)
+                st.dataframe(df)
+                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)
+                vectorizer = TfidfVectorizer()
+                tfidf_matrix = vectorizer.fit_transform(result)
+                tfidf_df = pd.DataFrame(tfidf_matrix.toarray(), columns=vectorizer.get_feature_names_out())
+                st.subheader("TF-IDF Values:")
+                st.dataframe(tfidf_df)
+        
+                cosine_sim_matrix = cosine_similarity(tfidf_matrix)
+                cosine_sim_df = pd.DataFrame(cosine_sim_matrix)
+                st.subheader("Cosine Similarity Matrix:")
+                st.dataframe(cosine_sim_df)
+
+        
+                st.subheader("A score closer to 1 means closer match")
+        
+                fig = px.imshow(cosine_sim_df, text_auto=True, labels=dict(x="Cosine similarity", y="Text", color="Productivity"),
+                        x=['text1', 'Jon Description'],
+                        y=['text1', 'Job Description'])
+                st.plotly_chart(fig)
+
+                st.subheader("Cosine Similarity Scores (Job Description vs. Resumes):")
+                for i, similarity_score in enumerate(cosine_sim_matrix[0][1:]):
+                    st.write(f"Similarity with Candidate Profile {i + 1}: {similarity_score:.4f}")
+
 else:
     st.warning(f"You have reached the maximum upload attempts ({max_attempts}). Please refresh to upload more files.")
 
 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).")
-        
+
+
+