app.py

import os
from io import BytesIO

import streamlit as st
import fitz  # PyMuPDF
import requests
from dotenv import load_dotenv

from config import supabase, HF_API_TOKEN, HF_HEADERS, HF_MODELS
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,
)

# ------------------------- Main App Function -------------------------
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)

    # Upload resumes (limit: 10 files)
    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

    # Input job description
    job_description = st.text_area("Enter Job Description")

    # Evaluation trigger
    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

        st.write("### 📊 Evaluating Resumes...")

        # Resume Evaluation
        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 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}")

            # Downloadable PDF Report
            pdf_report = generate_pdf_report(shortlisted, questions)
            st.download_button("Download Shortlist Report", pdf_report, "shortlist.pdf")

        # Removed Candidates Info
        if removed_candidates:
            st.subheader("❌ Resumes Removed:")
            for removed in removed_candidates:
                st.write(f"**{removed['name']}** - {removed['reason']}")

# ------------------------- Run the App -------------------------
if __name__ == "__main__":
    main()