Update app.py

app.py

@@ -1,67 +1,51 @@
+from sentence_transformers import SentenceTransformer
 import streamlit as st
-from PyPDF2 import PdfReader
 import pandas as pd
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.metrics.pairwise import cosine_similarity
-from gliner import GLiNER
-import plotly.express as px
-import time
-from sentence_transformers import SentenceTransformer
+from PyPDF2 import PdfReader
 
 model = SentenceTransformer("all-mpnet-base-v2")
-
-
 st.title("AI Resume Analysis based on Keywords App")
 st.divider()
-
-job = pd.Series(st.text_area("Paste the job description and then press Ctrl + Enter", key="job_desc"), name="Text")
+job_desc = st.text_area("Paste the job description and then press Ctrl + Enter", key="job_desc")
 
 if 'applicant_data' not in st.session_state:
     st.session_state['applicant_data'] = {}
-
 max_attempts = 1
-
-for i in range(1, 51):  # Looping for 2 applicants
+for i in range(1, 51):  # Looping for 50 applicants
     st.subheader(f"Applicant {i} Resume", divider="green")
     applicant_key = f"applicant_{i}"
     upload_key = f"candidate_{i}"
-
     if applicant_key not in st.session_state['applicant_data']:
         st.session_state['applicant_data'][applicant_key] = {'upload_count': 0, 'uploaded_file': None, 'analysis_done': False}
-
     if st.session_state['applicant_data'][applicant_key]['upload_count'] < max_attempts:
         uploaded_file = st.file_uploader(f"Upload Applicant's {i} resume", type="pdf", key=upload_key)
-
         if uploaded_file:
             st.session_state['applicant_data'][applicant_key]['uploaded_file'] = uploaded_file
             st.session_state['applicant_data'][applicant_key]['upload_count'] += 1
             st.session_state['applicant_data'][applicant_key]['analysis_done'] = False # Reset analysis flag
-
         if st.session_state['applicant_data'][applicant_key]['uploaded_file'] and not st.session_state['applicant_data'][applicant_key]['analysis_done']:
-            pdf_reader = PdfReader(st.session_state['applicant_data'][applicant_key]['uploaded_file'])
-            text_data = ""
-            for page in pdf_reader.pages:
-                text_data += page.extract_text()
+            try:
+                pdf_reader = PdfReader(st.session_state['applicant_data'][applicant_key]['uploaded_file'])
+                text_data = ""
+                for page in pdf_reader.pages:
+                    text_data += page.extract_text()
+                with st.expander(f"See Applicant's {i} resume"):
+                    st.write(text_data)
 
-            with st.expander(f"See Applicant's {i} resume"):
-                st.write(text_data)
+                # Encode the job description and resume text separately
+                job_embedding = model.encode(job_desc)
+                resume_embedding = model.encode(text_data)
 
-            data = pd.Series(text_data, name='Text')
-            result = pd.concat([job, data])
+                # Calculate the cosine similarity between the two embeddings
+                similarity_score = model.similarity(job_embedding, resume_embedding)[0][0]
 
-            
-            embeddings = model.encode([result])
-            similarities = model.similarity(embeddings, embeddings)
-            
-            
-           
-            for j, similarity_score in enumerate(similarities[0][1:]):
                 with st.popover(f"See Result for Applicant {i}"):
                     st.write(f"Similarity between Applicant's resume and job description based on keywords: {similarity_score:.2f}")
                     st.info(
                         f"A score closer to 1 (0.80, 0.90) means higher similarity between Applicant's {i} resume and job description. A score closer to 0 (0.20, 0.30) means lower similarity between Applicant's {i} resume and job description.")
-            st.session_state['applicant_data'][applicant_key]['analysis_done'] = True
-
+                st.session_state['applicant_data'][applicant_key]['analysis_done'] = True
+            except Exception as e:
+                st.error(f"An error occurred while processing Applicant {i}'s resume: {e}")
     else:
         st.warning(f"Maximum upload attempts has been reached ({max_attempts}).")
         if st.session_state['applicant_data'][applicant_key]['upload_count'] > 0: