Update app.py

app.py

@@ -61,7 +61,9 @@ if st.session_state['upload_count'] < max_attempts:
         text_data = ""
         for page in pdf_reader.pages:
             text_data += page.extract_text()
-            st.text_area("Applicant's 1 resume", value = text_data, height = 300, key = "text 2")
+            with st.expander("See Applicant'1 resume"):
+                st.write("text_data")
+            
             data = pd.Series(text_data, name='Text')
             frames = [job, data]
             result = pd.concat(frames)
@@ -100,7 +102,10 @@ if st.session_state['upload_count'] < max_attempts:
         text_data = ""
         for page in pdf_reader.pages:
             text_data += page.extract_text()
-            st.text_area("Applicant's 2 resume", value = text_data, height = 300, key = "text 2")
+            with st.expander("See Applicant'2 Resume"):
+                st.write("text_data")
+        
+    
             data = pd.Series(text_data, name='Text')
             frames = [job, data]
             result = pd.concat(frames)
@@ -124,47 +129,8 @@ else:
         
 
 
-st.subheader("Applicant Resume 3", 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 Applicant's 3 resume", type="pdf", key="candidate 3")
-    if uploaded_files:
-        st.session_state['upload_count'] += 1
-        
-        pdf_reader = PdfReader(uploaded_files)
-        text_data = ""
-        for page in pdf_reader.pages:
-            text_data += page.extract_text()
-            st.text_area("Applicant's 3 resume", value = text_data, height = 300, key = "text 2")
-            data = pd.Series(text_data, name='Text')
-            frames = [job, data]
-            result = pd.concat(frames)
-            
-
-            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)
-
-            for i, similarity_score in enumerate(cosine_sim_matrix[0][1:]):
-                with st.popover("See result"):
-                    st.write(f"Similarity of job description with Applicant's 3 resume based on keywords: {similarity_score:.2f}")
-                st.info(
-                    "A score closer to 1 (0.80, 0.90) means higher similarity between Applicant's 3 resume and job description. A score closer to 0 (0.20, 0.30) means lower similarity between Applicant's 3 resume and job description.")
-else:
-    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).")
-
-
-
-                
-
 
+