Update app.py

app.py

@@ -18,93 +18,97 @@ import pandas as pd
 from PyPDF2 import PdfReader
 from gliner import GLiNER
 
+import streamlit as st
+import pandas as pd
+from PyPDF2 import PdfReader
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+import tempfile
 
-txt = st.text_area("Job description", key = "text 1")
-job_description_series = pd.Series(txt, name="Text")
-st.dataframe(job_description_series)
+# First set of inputs and calculations
+txt1 = st.text_area("Job description 1", key="text 1")
+job_description_series1 = pd.Series([txt1], name="Text")
+st.dataframe(job_description_series1)
 
-uploaded_files = st.file_uploader(
-    "Choose a PDF file(s) for candidate profiles",  type="pdf", key = "candidate 1"
+uploaded_files1 = st.file_uploader(
+    "Choose PDF file(s) for candidate profiles 1", type="pdf", key="candidate 1", accept_multiple_files=True
 )
 
+all_resumes_text1 = []  # Store the text content of each PDF
 
-all_resumes_text = []  # Store the text content of each PDF
-
-if uploaded_files:
-    for uploaded_file in uploaded_files:
+if uploaded_files1:
+    for uploaded_file in uploaded_files1:
         try:
             pdf_reader = PdfReader(uploaded_file)
             text_data = ""
             for page in pdf_reader.pages:
                 text_data += page.extract_text()
-            all_resumes_text.append(text_data)
+            all_resumes_text1.append(text_data)
         except Exception as e:
             st.error(f"Error processing file {uploaded_file.name}: {e}")
 
-    if all_resumes_text:
-        all_documents = [job_description_series.iloc[0]] + all_resumes_text
+    if all_resumes_text1:
+        all_documents1 = [job_description_series1.iloc[0]] + all_resumes_text1
 
-        vectorizer = TfidfVectorizer()
-        tfidf_matrix = vectorizer.fit_transform(all_documents)
+        vectorizer1 = TfidfVectorizer()
+        tfidf_matrix1 = vectorizer1.fit_transform(all_documents1)
 
-        tfidf_df = pd.DataFrame(tfidf_matrix.toarray(), columns=vectorizer.get_feature_names_out())
-        st.subheader("TF-IDF Values:")
-        st.dataframe(tfidf_df)
+        tfidf_df1 = pd.DataFrame(tfidf_matrix1.toarray(), columns=vectorizer1.get_feature_names_out())
+        st.subheader("TF-IDF Values (Set 1):")
+        st.dataframe(tfidf_df1)
 
-        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)
+        cosine_sim_matrix1 = cosine_similarity(tfidf_matrix1)
+        cosine_sim_df1 = pd.DataFrame(cosine_sim_matrix1)
+        st.subheader("Cosine Similarity Matrix (Set 1):")
+        st.dataframe(cosine_sim_df1)
 
-        # 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_matrix[0][1:]):
+        st.subheader("Cosine Similarity Scores (Job Description 1 vs. Resumes 1):")
+        for i, similarity_score in enumerate(cosine_sim_matrix1[0][1:]):
             st.write(f"Similarity with Candidate Profile {i + 1}: {similarity_score:.4f}")
 
-
-       
 st.divider()
 
-txt = st.text_area("Job description", key = "text 2")
-job_description_series = pd.Series(txt, name="Text")
-st.dataframe(job_description_series)
+# Second set of inputs and calculations
+txt2 = st.text_area("Job description 2", key="text 2")
+job_description_series2 = pd.Series([txt2], name="Text")
+st.dataframe(job_description_series2)
 
-uploaded_files = st.file_uploader(
-    "Choose a PDF file(s) for candidate profiles",  type="pdf", key = "candidate 2"
+uploaded_files2 = st.file_uploader(
+    "Choose PDF file(s) for candidate profiles 2", type="pdf", key="candidate 2", accept_multiple_files=True
 )
 
+all_resumes_text2 = []  # Store the text content of each PDF
 
-all_resumes_text = []  # Store the text content of each PDF
-
-if uploaded_files:
-    for uploaded_file in uploaded_files:
+if uploaded_files2:
+    for uploaded_file in uploaded_files2:
         try:
             pdf_reader = PdfReader(uploaded_file)
             text_data = ""
             for page in pdf_reader.pages:
                 text_data += page.extract_text()
-            all_resumes_text.append(text_data)
+            all_resumes_text2.append(text_data)
         except Exception as e:
             st.error(f"Error processing file {uploaded_file.name}: {e}")
 
-    if all_resumes_text:
-        all_documents = [job_description_series.iloc[0]] + all_resumes_text
+    if all_resumes_text2:
+        all_documents2 = [job_description_series2.iloc[0]] + all_resumes_text2
 
-        vectorizer = TfidfVectorizer()
-        tfidf_matrix = vectorizer.fit_transform(all_documents)
+        vectorizer2 = TfidfVectorizer()
+        tfidf_matrix2 = vectorizer2.fit_transform(all_documents2)
 
-        tfidf_df = pd.DataFrame(tfidf_matrix.toarray(), columns=vectorizer.get_feature_names_out())
-        st.subheader("TF-IDF Values:")
-        st.dataframe(tfidf_df)
+        tfidf_df2 = pd.DataFrame(tfidf_matrix2.toarray(), columns=vectorizer2.get_feature_names_out())
+        st.subheader("TF-IDF Values (Set 2):")
+        st.dataframe(tfidf_df2)
 
-        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)
+        cosine_sim_matrix2 = cosine_similarity(tfidf_matrix2)
+        cosine_sim_df2 = pd.DataFrame(cosine_sim_matrix2)
+        st.subheader("Cosine Similarity Matrix (Set 2):")
+        st.dataframe(cosine_sim_df2)
 
-        # 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_matrix[0][1:]):
+        st.subheader("Cosine Similarity Scores (Job Description 2 vs. Resumes 2):")
+        for i, similarity_score in enumerate(cosine_sim_matrix2[0][1:]):
             st.write(f"Similarity with Candidate Profile {i + 1}: {similarity_score:.4f}")
 
+
+