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import streamlit as st
from utils import (
evaluate_resumes, generate_pdf_report, store_in_supabase, extract_email,
score_candidate, parse_resume, summarize_resume, extract_keywords, generate_interview_questions_from_summaries
)
from config import supabase
from config import HF_API_TOKEN, HF_HEADERS, HF_MODELS
import fitz # PyMuPDF
from io import BytesIO
from dotenv import load_dotenv
import os
import requests
def main():
st.set_page_config(page_title="TalentLens.AI", layout="centered")
st.markdown("<h1 style='text-align: center;'>TalentLens.AI</h1>", unsafe_allow_html=True)
st.divider()
st.markdown("<h3 style='text-align: center;'>AI-Powered Intelligent Resume Screening</h3>", unsafe_allow_html=True)
# Limit resume uploads to 10
uploaded_files = st.file_uploader("Upload Resumes (PDF Only, Max: 10)", accept_multiple_files=True, type=["pdf"])
if uploaded_files and len(uploaded_files) > 10:
st.error("⚠️ You can upload a maximum of 10 resumes at a time.")
return
job_description = st.text_area("Enter Job Description")
if st.button("Evaluate Resumes"):
if not job_description:
st.error("⚠️ Please enter a job description.")
return
if not uploaded_files:
st.error("⚠️ Please upload at least one resume.")
return
#if uploaded_files and job_description:
st.write("### 📊 Evaluating Resumes...")
# 🔹 Extract required keywords dynamically from the job description
# required_keywords = extract_keywords(job_description)
# st.write(f"**Extracted Keywords:** {', '.join(required_keywords)}")
shortlisted, removed_candidates = evaluate_resumes(uploaded_files, job_description)
if not shortlisted:
st.warning("⚠️ No resumes matched the required keywords.")
else:
st.subheader("✅ Shortlisted Candidates:")
for candidate in shortlisted:
st.write(f"**{candidate['name']}**")
# Generate PDF Report
pdf_report = generate_pdf_report(shortlisted)
st.download_button("Download Shortlist Report", pdf_report, "shortlist.pdf")
# Generate Interview Questions
questions = generate_interview_questions_from_summaries(shortlisted)
st.subheader("🧠 Suggested Interview Questions:")
for idx, q in enumerate(questions, 1):
st.markdown(f"**Q{idx}.** {q}")
# 🔻 Display removed candidates due to missing keywords
if removed_candidates:
st.subheader("❌ Resumes Removed:")
for removed in removed_candidates:
st.write(f"**{removed['name']}** - {removed['reason']}")
if __name__ == "__main__":
main() |