Update app.py

app.py

@@ -25,100 +25,116 @@ from sklearn.feature_extraction.text import TfidfVectorizer
 from sklearn.metrics.pairwise import cosine_similarity
 import tempfile
 
-txt1 = st.text_area("Job description", key="text 1")
+txt1 = st.text_area("Job description", key = "text 1")
 job_description_series1 = pd.Series(txt1, name="Text")
 st.dataframe(job_description_series1)
 
-uploaded_files1 = st.file_uploader(
-    "Choose a PDF file(s) for candidate profiles", type="pdf", key="candidate_1"
+uploaded_files = st.file_uploader(
+    "Choose a PDF file(s) for candidate profiles",  type="pdf", key = "candidate 1"
 )
+
+
 all_resumes_text1 = []  # Store the text content of each PDF
-if uploaded_files1:
-    for uploaded_file in uploaded_files1:
+
+if uploaded_files:
+    for uploaded_file in uploaded_files:
         try:
-            if uploaded_file is not None:  # Check if a file was uploaded
-                pdf_reader = PdfReader(uploaded_file)
-                text_data = ""
-                for page in pdf_reader.pages:
-                    text_data += page.extract_text()
-                    model = GLiNER.from_pretrained("urchade/gliner_base")
-                    labels = ["person", "country", "organization", "time", "role"]
-                    entities = model.predict_entities(text_data, labels)
-                entity_dict = {}
-                for label in labels:
-                    entity_dict[label] = [entity["text"] for entity in entities if entity["label"] == label]
-                data = {"Text": text_data, **entity_dict}
-                all_resumes_text1.append(text_data)
-            else:
-                st.warning("No file uploaded for the first set of candidates.")
+            pdf_reader = PdfReader(uploaded_file)
+            text_data = ""
+            for page in pdf_reader.pages:
+                text_data += page.extract_text()
+                model = GLiNER.from_pretrained("urchade/gliner_base")
+                labels = ["person", "country", "organization", "time", "role"]
+                entities = model.predict_entities(text_data, labels)
+
+            entity_dict = {}
+            for label in labels:
+                entity_dict[label] = [entity["text"] for entity in entities if entity["label"] == label]
+
+            data = {"Text": text_data, **entity_dict}
+            
+
+
+            all_resumes_text1.append(data)
         except Exception as e:
-            if hasattr(uploaded_file, 'name'):
-                st.error(f"Error processing file {uploaded_file.name}: {e}")
-            else:
-                st.error(f"Error processing a file: {e}")
+            st.error(f"Error processing file {uploaded_file.name}: {e}")
 
     if all_resumes_text1:
-        all_documents1 = [job_description_series1.iloc[0]] + all_resumes_text1
-        vectorizer1 = TfidfVectorizer()
-        tfidf_matrix1 = vectorizer1.fit_transform(all_documents1)
-        tfidf_df1 = pd.DataFrame(tfidf_matrix1.toarray(), columns=vectorizer1.get_feature_names_out())
+        all_documents = [job_description_series.iloc[0]] + all_resumes_text
+
+        vectorizer = TfidfVectorizer()
+        tfidf_matrix = vectorizer.fit_transform(all_documents)
+
+        tfidf_df = pd.DataFrame(tfidf_matrix.toarray(), columns=vectorizer.get_feature_names_out())
         st.subheader("TF-IDF Values:")
-        st.dataframe(tfidf_df1)
-        cosine_sim_matrix1 = cosine_similarity(tfidf_matrix1)
-        cosine_sim_df1 = pd.DataFrame(cosine_sim_matrix1)
+        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_df1)
+        st.dataframe(cosine_sim_df)
+
         # Display similarity scores between the job description and each resume
         st.subheader("Cosine Similarity Scores (Job Description vs. Resumes):")
-        for i, similarity_score in enumerate(cosine_sim_matrix1[0][1:]):
+        for i, similarity_score in enumerate(cosine_sim_matrix[0][1:]):
             st.write(f"Similarity with Candidate Profile {i + 1}: {similarity_score:.4f}")
 
+
+       
 st.divider()
 
-txt2 = st.text_area("Job description", key="text 2")
+txt2 = st.text_area("Job description", key = "text 2")
 job_description_series2 = pd.Series(txt2, name="Text")
 st.dataframe(job_description_series2)
 
-uploaded_files2 = st.file_uploader(
-    "Choose a PDF file(s) for candidate profiles", type="pdf", key="candidate_2"
+uploaded_files = st.file_uploader(
+    "Choose a PDF file(s) for candidate profiles",  type="pdf", key = "candidate 2"
 )
+
+
 all_resumes_text2 = []  # Store the text content of each PDF
-if uploaded_files2:
-    for uploaded_file in uploaded_files2:
+
+if uploaded_files:
+    for uploaded_file in uploaded_files:
         try:
-            if uploaded_file is not None:  # Check if a file was uploaded
-                pdf_reader = PdfReader(uploaded_file)
-                text_data = ""
-                for page in pdf_reader.pages:
-                    text_data += page.extract_text()
-                    model = GLiNER.from_pretrained("urchade/gliner_base")
-                    labels = ["person", "country", "organization", "time", "role"]
-                    entities = model.predict_entities(text_data, labels)
-                entity_dict = {}
-                for label in labels:
-                    entity_dict[label] = [entity["text"] for entity in entities if entity["label"] == label]
-                data = {"Text": text_data, **entity_dict}
-                all_resumes_text2.append(text_data)
-            else:
-                st.warning("No file uploaded for the second set of candidates.")
+            pdf_reader = PdfReader(uploaded_file)
+            text_data = ""
+            for page in pdf_reader.pages:
+                text_data += page.extract_text()
+                model = GLiNER.from_pretrained("urchade/gliner_base")
+                labels = ["person", "country", "organization", "time", "role"]
+                entities = model.predict_entities(text_data, labels)
+
+            entity_dict = {}
+            for label in labels:
+                entity_dict[label] = [entity["text"] for entity in entities if entity["label"] == label]
+
+            data = {"Text": text_data, **entity_dict}
+            
+
+
+            all_resumes_text2.append(data)
         except Exception as e:
-            if hasattr(uploaded_file, 'name'):
-                st.error(f"Error processing file {uploaded_file.name}: {e}")
-            else:
-                st.error(f"Error processing a file: {e}")
+            st.error(f"Error processing file {uploaded_file.name}: {e}")
 
     if all_resumes_text2:
-        all_documents2 = [job_description_series2.iloc[0]] + all_resumes_text2
-        vectorizer2 = TfidfVectorizer()
-        tfidf_matrix2 = vectorizer2.fit_transform(all_documents2)
-        tfidf_df2 = pd.DataFrame(tfidf_matrix2.toarray(), columns=vectorizer2.get_feature_names_out())
+        all_documents = [job_description_series.iloc[0]] + all_resumes_text
+
+        vectorizer = TfidfVectorizer()
+        tfidf_matrix = vectorizer.fit_transform(all_documents)
+
+        tfidf_df = pd.DataFrame(tfidf_matrix.toarray(), columns=vectorizer.get_feature_names_out())
         st.subheader("TF-IDF Values:")
-        st.dataframe(tfidf_df2)
-        cosine_sim_matrix2 = cosine_similarity(tfidf_matrix2)
-        cosine_sim_df2 = pd.DataFrame(cosine_sim_matrix2)
+        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_df2)
+        st.dataframe(cosine_sim_df)
+
         # Display similarity scores between the job description and each resume
         st.subheader("Cosine Similarity Scores (Job Description vs. Resumes):")
-        for i, similarity_score in enumerate(cosine_sim_matrix2[0][1:]):
-            st.write(f"Similarity with Candidate Profile {i + 1}: {similarity_score:.4f}")
+        for i, similarity_score in enumerate(cosine_sim_matrix[0][1:]):
+            st.write(f"Similarity with Candidate Profile {i + 1}: {similarity_score:.4f}")
+
+