Create app.py

app.py

@@ -0,0 +1,71 @@
+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
+
+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")
+
+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
+    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()
+
+            with st.expander(f"See Applicant's {i} resume"):
+                st.write(text_data)
+
+            data = pd.Series(text_data, name='Text')
+            result = pd.concat([job, data])
+
+            
+            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
+
+    else:
+        st.warning(f"Maximum upload attempts has been reached ({max_attempts}).")
+        if st.session_state['applicant_data'][applicant_key]['upload_count'] > 0:
+            st.info(f"Files uploaded for Applicant {i}: {st.session_state['applicant_data'][applicant_key]['upload_count']} time(s).")
+
+
+