app.py

import asyncio
import atexit
import json
from html import escape
from pathlib import Path
from typing import Any, List, Tuple

# Suppress Python 3.13 asyncio "Invalid file descriptor: -1" noise at GC/shutdown.
# CPython 3.13 prints these via the "Exception ignored in: <__del__>" path which
# bypasses the warnings system entirely — the only reliable fix is to monkeypatch
# BaseEventLoop.__del__ so the ValueError is swallowed before CPython can print it.
try:
    import asyncio.base_events as _abe
    _orig_loop_del = _abe.BaseEventLoop.__del__

    def _safe_loop_del(self) -> None:
        try:
            _orig_loop_del(self)
        except Exception:
            pass

    _abe.BaseEventLoop.__del__ = _safe_loop_del
    del _abe, _safe_loop_del
except Exception:
    pass


def _close_asyncio_loop() -> None:
    """Close any leftover asyncio event loop at process exit."""
    try:
        loop = asyncio.get_event_loop_policy().get_event_loop()
        if loop and not loop.is_closed():
            loop.close()
    except Exception:
        pass


atexit.register(_close_asyncio_loop)

import gradio as gr
from dotenv import load_dotenv

from src.jobs.ats_detector import detect_ats
from src.jobs.company_loader import load_companies
from src.jobs.debug_utils import log_debug_header, log_debug_line, save_debug_html
from src.jobs.extractor import extract_jobs_with_diagnostics
from src.jobs.fetcher import fetch_jobs_from_ats_api, resolve_real_jobs_page
from src.models import JobPosting
from src.output.generator import build_talking_points, resume_profile_to_json
from src.resume.pdf_extract import extract_resume_text
from src.resume.profile_builder import build_resume_profile
from src.scoring.matcher import rank_companies, score_job_match

BASE_DIR = Path(__file__).resolve().parent
load_dotenv(BASE_DIR / ".env")

DEFAULT_COMPANY_CANDIDATES = [
    BASE_DIR / "NSBE 2026 Baltimore Company_ Schools  - Companies.csv",
    BASE_DIR / "data" / "NSBE 2026 Baltimore Company_ Schools  - Companies (1).csv",
]
DEBUG_HTML_DIR = BASE_DIR / "debug_html"
APP_THEME = gr.themes.Base(
    primary_hue="cyan",
    secondary_hue="indigo",
    neutral_hue="slate",
    font=["Manrope", "ui-sans-serif", "sans-serif"],
)

CUSTOM_CSS = """
@import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700;800&family=Space+Grotesk:wght@500;700&display=swap');

:root {
    --bg: #f5f7fb;
    --surface: #ffffff;
    --surface-muted: #f8fafc;
    --surface-soft: #f1f5f9;
    --border: #e5eaf2;
    --border-strong: #d7dfeb;
    --text: #102033;
    --text-muted: #5e7086;
    --text-soft: #7d8ea4;
    --accent: #3366ff;
    --accent-soft: #eef3ff;
    --accent-hover: #2856df;
    --success: #1f9d73;
    --danger: #d94f45;
    --shadow-lg: 0 18px 40px rgba(15, 23, 42, 0.08);
    --shadow-md: 0 10px 24px rgba(15, 23, 42, 0.06);
}

html, body, .gradio-container {
    min-height: 100%;
}

body, .gradio-container {
    background: linear-gradient(180deg, #f7f9fc 0%, #f3f6fb 100%);
    color: var(--text);
    font-family: 'Inter', sans-serif;
}

.gradio-container {
    max-width: 1260px !important;
    padding: 20px 20px 30px !important;
}

.gradio-container * {
    box-sizing: border-box;
}

.app-shell {
    gap: 18px;
}

.app-hero {
    padding: 20px 22px;
    border-radius: 18px;
    border: 1px solid var(--border);
    background: var(--surface);
    box-shadow: var(--shadow-md);
}

.eyebrow {
    display: inline-flex;
    align-items: center;
    min-height: 28px;
    padding: 0 10px;
    border-radius: 999px;
    background: var(--accent-soft);
    color: var(--accent);
    font-size: 0.74rem;
    font-weight: 700;
    letter-spacing: 0.04em;
    text-transform: uppercase;
}

.hero-title {
    margin: 12px 0 6px;
    color: var(--text);
    font-family: 'Space Grotesk', sans-serif;
    font-size: clamp(1.8rem, 2.5vw, 2.5rem);
    letter-spacing: -0.04em;
    line-height: 1.02;
}

.hero-copy {
    margin: 0;
    max-width: 760px;
    color: var(--text-muted);
    font-size: 0.98rem;
    line-height: 1.55;
}

.hero-meta {
    display: grid;
    grid-template-columns: repeat(3, minmax(0, 1fr));
    gap: 10px;
    margin-top: 16px;
}

.hero-pill {
    padding: 12px 14px;
    border-radius: 14px;
    border: 1px solid var(--border);
    background: var(--surface-muted);
    color: var(--text-muted);
    font-size: 0.88rem;
    line-height: 1.45;
}

.hero-pill strong {
    display: block;
    margin-bottom: 4px;
    color: var(--text);
    font-size: 0.92rem;
}

.panel {
    border-radius: 18px;
    border: 1px solid var(--border);
    background: var(--surface);
    box-shadow: var(--shadow-lg);
}

.control-panel,
.results-panel {
    padding: 18px;
}

.section-title {
    margin-bottom: 14px;
}

.section-title h3 {
    margin: 0 0 6px;
    color: var(--text);
    font-size: 1.05rem;
    font-weight: 700;
    letter-spacing: -0.02em;
}

.section-title p {
    margin: 0;
    color: var(--text-muted);
    line-height: 1.5;
    font-size: 0.92rem;
}

.chip-row {
    display: flex;
    flex-wrap: wrap;
    gap: 8px;
    margin-top: 10px;
}

.chip {
    display: inline-flex;
    align-items: center;
    min-height: 28px;
    padding: 0 10px;
    border-radius: 999px;
    background: var(--surface-soft);
    border: 1px solid var(--border);
    color: var(--text-muted);
    font-size: 0.8rem;
    font-weight: 600;
}

.subcard {
    padding: 14px;
    margin-bottom: 12px;
    border-radius: 14px;
    border: 1px solid var(--border);
    background: var(--surface);
}

.subcard:last-child {
    margin-bottom: 0;
}

.subcard-title {
    margin: 0 0 10px;
    color: var(--text);
    font-size: 0.92rem;
    font-weight: 700;
}

.results-note {
    margin-top: 12px;
    color: var(--text-soft);
    font-size: 0.86rem;
    line-height: 1.5;
}

.gr-box,
.gr-group,
.gr-form,
.gr-panel,
.gradio-container .block,
.gradio-container .gr-block {
    background: transparent !important;
    border: none !important;
    box-shadow: none !important;
    padding: 0 !important;
}

.gradio-container .gr-column {
    gap: 0 !important;
}

.gradio-container .gr-form,
.gradio-container .gr-group {
    gap: 12px !important;
}

.gradio-container label,
.gradio-container .wrap label,
.gradio-container .prose,
.gradio-container .prose p,
.gradio-container .prose strong {
    color: var(--text) !important;
}

.gradio-container .gr-markdown p {
    color: var(--text-muted) !important;
}

.gradio-container .wrap label span,
.gradio-container .label-wrap span,
.gradio-container .gr-form label,
.gradio-container .gr-checkbox label {
    color: var(--text) !important;
    font-weight: 600 !important;
}

.gradio-container input,
.gradio-container textarea,
.gradio-container select,
.gradio-container .gr-textbox,
.gradio-container .cm-editor,
.gradio-container .gr-code,
.gradio-container .gr-dataframe {
    border-radius: 10px !important;
    border: 1px solid var(--border-strong) !important;
    background: #ffffff !important;
    color: var(--text) !important;
    box-shadow: none !important;
}

.gradio-container input::placeholder,
.gradio-container textarea::placeholder {
    color: var(--text-soft) !important;
}

.gradio-container .gr-file,
.gradio-container .upload-card {
    min-height: 84px !important;
    border-radius: 12px !important;
    border: 1px dashed #c7d3e3 !important;
    background: var(--surface-muted) !important;
    transition: border-color 140ms ease, background 140ms ease, box-shadow 140ms ease;
    overflow: hidden !important;
}

.gradio-container .gr-file > div,
.gradio-container .upload-card > div {
    min-height: 84px !important;
}

.gradio-container .gr-file:hover,
.gradio-container .upload-card:hover {
    border-color: #9fb8ff !important;
    background: #f7faff !important;
    box-shadow: 0 0 0 4px rgba(51, 102, 255, 0.05) !important;
}

.gradio-container .gr-file .wrap,
.gradio-container .gr-file .or,
.gradio-container .gr-file .hint {
    color: var(--text-muted) !important;
}

.gradio-container .gr-button-primary {
    min-height: 44px;
    border-radius: 10px !important;
    border: none !important;
    background: var(--accent) !important;
    color: #ffffff !important;
    font-weight: 700 !important;
    letter-spacing: 0.01em;
    box-shadow: 0 8px 18px rgba(51, 102, 255, 0.18) !important;
    transition: transform 140ms ease, background 140ms ease, box-shadow 140ms ease !important;
}

.gradio-container .gr-button-primary:hover {
    background: var(--accent-hover) !important;
    transform: translateY(-1px);
    box-shadow: 0 10px 20px rgba(51, 102, 255, 0.22) !important;
}

.gradio-container button:disabled,
.gradio-container .gr-button-primary[disabled] {
    opacity: 0.6 !important;
    cursor: not-allowed !important;
}

.gradio-container .gr-slider,
.gradio-container .gr-slider .wrap,
.gradio-container .gr-slider input {
    color: var(--text) !important;
}

.gradio-container input[type='checkbox'] {
    accent-color: var(--accent);
}

.gradio-container .tab-nav {
    margin-bottom: 12px;
    padding: 4px !important;
    border-radius: 12px !important;
    background: var(--surface-soft) !important;
    border: 1px solid var(--border) !important;
}

.gradio-container .tab-nav button {
    min-height: 38px;
    border-radius: 9px !important;
    color: var(--text-muted) !important;
    font-weight: 700 !important;
    transition: background 120ms ease, color 120ms ease;
}

.gradio-container .tab-nav button.selected {
    background: #ffffff !important;
    color: var(--text) !important;
    box-shadow: 0 1px 2px rgba(15, 23, 42, 0.08);
}

.tab-panel {
    padding-top: 4px;
}

.gradio-container .gr-dataframe {
    overflow: hidden !important;
}

.gradio-container table {
    border-collapse: collapse !important;
}

.gradio-container thead th {
    padding: 12px 14px !important;
    background: var(--surface-muted) !important;
    color: var(--text-muted) !important;
    font-size: 0.8rem !important;
    font-weight: 700 !important;
    border-bottom: 1px solid var(--border) !important;
}

.gradio-container tbody tr:hover {
    background: #f8fbff !important;
}

.gradio-container td {
    padding: 12px 14px !important;
    color: var(--text) !important;
    border-bottom: 1px solid #edf2f7 !important;
}

.status-card,
.summary-shell,
.empty-state {
    border-radius: 14px;
    border: 1px solid var(--border);
    background: var(--surface);
}

.status-card {
    padding: 14px 16px;
    margin-bottom: 12px;
}

.status-card strong {
    display: block;
    margin-bottom: 4px;
    color: var(--text);
    font-size: 0.94rem;
}

.status-card p {
    margin: 0;
    color: var(--text-muted);
    line-height: 1.5;
}

.status-card.info {
    border-color: #dbe6ff;
    background: #f8fbff;
}

.status-card.success {
    border-color: #d7f0e6;
    background: #f7fcf9;
}

.status-card.error {
    border-color: #f1d9d7;
    background: #fff8f7;
}

.summary-shell {
    padding: 12px;
    margin-bottom: 12px;
    background: var(--surface-muted);
}

.summary-grid {
    display: grid;
    grid-template-columns: repeat(4, minmax(0, 1fr));
    gap: 10px;
}

.summary-card {
    padding: 14px;
    border-radius: 12px;
    border: 1px solid var(--border);
    background: var(--surface);
}

.summary-card span {
    display: block;
    margin-bottom: 6px;
    color: var(--text-soft);
    font-size: 0.75rem;
    text-transform: uppercase;
    letter-spacing: 0.04em;
    font-weight: 700;
}

.summary-card strong {
    display: block;
    color: var(--text);
    font-size: 1.25rem;
    font-weight: 800;
    letter-spacing: -0.03em;
}

.summary-card small {
    display: block;
    margin-top: 8px;
    color: var(--text-muted);
    line-height: 1.45;
}

.empty-state {
    padding: 22px 18px;
    text-align: center;
}

.empty-state strong {
    display: block;
    margin-bottom: 8px;
    color: var(--text);
    font-size: 1rem;
}

.empty-state p {
    max-width: 520px;
    margin: 0 auto;
    color: var(--text-muted);
    line-height: 1.5;
}

@media (max-width: 980px) {
    .hero-meta,
    .summary-grid {
        grid-template-columns: repeat(2, minmax(0, 1fr));
    }

    .gradio-container {
        padding: 14px 14px 22px !important;
    }
}

@media (max-width: 720px) {
    .hero-meta,
    .summary-grid {
        grid-template-columns: 1fr;
    }
}
"""


def _resolve_file_path(file_obj: Any) -> str:
    if file_obj is None:
        return ""
    if isinstance(file_obj, str):
        return file_obj
    if hasattr(file_obj, "name"):
        return str(file_obj.name)
    if isinstance(file_obj, dict):
        return str(file_obj.get("name", ""))
    return ""


def _default_companies_path() -> str:
    for path in DEFAULT_COMPANY_CANDIDATES:
        if path.exists():
            return str(path)

    raise FileNotFoundError("No default company CSV file is available.")


def _fallback_job(company_name: str, careers_url: str, ats: str) -> JobPosting:
    return JobPosting(
        company=company_name,
        title="General Opportunities",
        location="",
        url=careers_url,
        department="",
        description="Careers page discovered but no structured roles were parsed.",
        ats=ats,
    )


def _build_status_html(title: str, body: str, tone: str = "info") -> str:
        return (
                f'<div class="status-card {escape(tone)}">'
                f'<strong>{escape(title)}</strong>'
                f'<p>{escape(body)}</p>'
                f'</div>'
        )


def _build_summary_html(ranked_rows: List[List[Any]], match_rows: List[List[Any]]) -> str:
        if not ranked_rows:
                return """
                <div class="empty-state">
                    <strong>No ranking data yet</strong>
                    <p>Upload a resume, run the matcher, and this panel will summarize the strongest companies, match volume, and best-fit roles.</p>
                </div>
                """

        top_company = str(ranked_rows[0][0]) if ranked_rows else "-"
        top_score = f"{float(ranked_rows[0][1]):.1f}" if ranked_rows and ranked_rows[0][1] not in (None, "") else "-"
        total_companies = len(ranked_rows)
        total_jobs = len(match_rows)
        avg_score = "-"
        if ranked_rows:
                scores = [float(row[1]) for row in ranked_rows if row[1] not in (None, "")]
                if scores:
                        avg_score = f"{sum(scores) / len(scores):.1f}"

        best_role = str(ranked_rows[0][3]) if ranked_rows and len(ranked_rows[0]) > 3 else "-"

        return f"""
        <div class="summary-shell">
            <div class="summary-grid">
                <div class="summary-card">
                    <span>Top Company</span>
                    <strong>{escape(top_company)}</strong>
                    <small>Best-fit company based on resolved job boards and resume alignment.</small>
                </div>
                <div class="summary-card">
                    <span>Top Score</span>
                    <strong>{escape(top_score)}</strong>
                    <small>Highest company fit score in the current analysis.</small>
                </div>
                <div class="summary-card">
                    <span>Companies / Jobs</span>
                    <strong>{total_companies} / {total_jobs}</strong>
                    <small>Ranked companies and extracted job matches returned in this run.</small>
                </div>
                <div class="summary-card">
                    <span>Average Fit / Best Role</span>
                    <strong>{escape(avg_score)}</strong>
                    <small>{escape(best_role)}</small>
                </div>
            </div>
        </div>
        """


def _save_company_debug_html(company_name: str, resolved_page_html: str, snapshots: dict[str, str], failure_type: str) -> None:
    for stage, html in snapshots.items():
        save_debug_html(company_name, html, stage, DEBUG_HTML_DIR)
    save_debug_html(company_name, resolved_page_html, "resolved", DEBUG_HTML_DIR)
    if failure_type and failure_type not in {"SUCCESS", "UNKNOWN"}:
        save_debug_html(company_name, resolved_page_html, failure_type.lower(), DEBUG_HTML_DIR)


def _log_company_diagnostics(
    company_name: str,
    original_url: str,
    resolved_page_url: str,
    fetch_method: str,
    final_url: str,
    html: str,
    ats: str,
    api_jobs: List[JobPosting],
    diagnostics: Any,
    resolution_steps: List[str],
) -> None:
    log_debug_header(company_name)
    log_debug_line("ORIGINAL URL", original_url)
    log_debug_line("RESOLVED URL", resolved_page_url)
    log_debug_line("FETCH METHOD", fetch_method)
    log_debug_line("FINAL URL", final_url)
    log_debug_line("RESOLUTION STEPS", resolution_steps)
    log_debug_line("HTML LENGTH", len(html))
    log_debug_line("ATS", ats)
    if ats not in {"greenhouse", "lever"}:
        log_debug_line("ATS NOTE", "No ATS API match detected; using generic HTML/script parsing")
    log_debug_line("TOTAL ELEMENTS SCANNED", diagnostics.total_elements_scanned)
    log_debug_line("RAW TEXT SAMPLE", diagnostics.raw_text_sample[:20])
    log_debug_line("CANDIDATES FOUND", diagnostics.candidates_found)
    log_debug_line("TITLE FILTER PASSES", diagnostics.title_filtered_count)
    log_debug_line("SCRIPT MATCHES", diagnostics.script_matches)
    log_debug_line("SCRIPT JOBS", diagnostics.script_jobs_extracted)
    log_debug_line("API JOBS", len(api_jobs))
    log_debug_line("VALID JOBS", diagnostics.valid_jobs + len(api_jobs))
    log_debug_line("SAMPLE TITLES", diagnostics.sample_titles)
    log_debug_line("FAILURE TYPE", diagnostics.failure_type)
    log_debug_line("SUCCESS", diagnostics.failure_type == "SUCCESS" or len(api_jobs) + diagnostics.valid_jobs > 0)


def analyze_resume(
    resume_pdf: Any,
    company_source: str,
    optional_company_csv: Any,
    max_companies: int,
    use_ai_parser: bool,
    progress: gr.Progress = gr.Progress(),
) -> Tuple[List[List[Any]], List[List[Any]], str, str, str, str]:
    resume_path = _resolve_file_path(resume_pdf)
    csv_path = _resolve_file_path(optional_company_csv) if company_source == "Custom CSV" else ""
    empty_summary = _build_summary_html([], [])

    if not resume_path:
        return (
            [],
            [],
            json.dumps({"error": "Please upload a resume PDF."}, indent=2),
            "",
            _build_status_html("Resume required", "Upload a PDF resume to start the analysis.", "error"),
            empty_summary,
        )

    try:
        # --- Debug: log pipeline inputs before anything runs ---
        import os
        print("[analyze] company_source:", company_source)
        print("[analyze] csv_path (resolved):", repr(csv_path))
        print("[analyze] resume_path:", repr(resume_path))
        print("[analyze] cwd:", os.getcwd())

        progress(0.05, desc="Extracting resume text")
        resume_text = extract_resume_text(resume_path)
        progress(0.12, desc="Building resume profile")
        profile = build_resume_profile(resume_text, use_ai=use_ai_parser)

        # Try to resolve the default CSV path and log clearly if it's missing.
        try:
            default_csv_path = _default_companies_path()
            print("[analyze] default_csv_path:", default_csv_path)
        except FileNotFoundError as fnf:
            print("[analyze] CRITICAL: default CSV not found:", fnf)
            return (
                [],
                [],
                json.dumps({"error": str(fnf)}, indent=2),
                "",
                _build_status_html("Company list not found", str(fnf), "error"),
                empty_summary,
            )

        companies = load_companies(default_csv_path, csv_path if csv_path else None)
        total_loaded = len(companies)
        with_url = sum(1 for c in companies if c.careers_url)
        print(f"[analyze] Loaded {total_loaded} companies, {with_url} have careers_url")

        # Hard-stop early so the user sees a clear reason rather than "0 companies processed".
        if total_loaded == 0:
            msg = (
                "No companies were loaded. "
                "Check that the CSV has a company-name column and at least one data row."
            )
            return (
                [],
                [],
                json.dumps({"error": msg}, indent=2),
                "",
                _build_status_html("No companies loaded", msg, "error"),
                empty_summary,
            )

        if with_url == 0:
            # All companies exist but every careers_url is empty — display which columns exist.
            col_sample = list((companies[0].meta or {}).keys())[:12] if companies else []
            msg = (
                f"Loaded {total_loaded} companies but none have a usable careers URL. "
                f"CSV columns detected: {col_sample}. "
                "This app now reads only the opening page column (col 4 / 'Direct links to company career/job openings page'). "
                "Add valid https URLs in that column."
            )
            print("[analyze] WARNING:", msg)
            return (
                [],
                [],
                json.dumps({"error": msg, "csv_columns": col_sample}, indent=2),
                "",
                _build_status_html("No careers URLs found", msg, "error"),
                empty_summary,
            )

        companies = companies[: int(max_companies)]
        print(f"[analyze] After max_companies cap: {len(companies)} companies to analyze")
        progress(0.18, desc=f"Analyzing {len(companies)} companies")

        discovered_jobs: List[JobPosting] = []
        processed_companies = 0
        for index, company in enumerate(companies, start=1):
            if not company.careers_url:
                continue

            try:
                progress(0.18 + (0.62 * index / max(1, len(companies))), desc=f"Resolving {company.company}")
                resolved_page = resolve_real_jobs_page(company.careers_url)
                resolved_url = resolved_page.url or company.careers_url
                resolved_html = resolved_page.html
                ats = detect_ats(resolved_url, resolved_html)

                if resolved_page.fallback_used:
                    print(f"[scraper] playwright fallback triggered: {resolved_page.fallback_reason or 'fallback_used'}")

                api_jobs = fetch_jobs_from_ats_api(company, ats, source_url=resolved_url)
                html_jobs, diagnostics = extract_jobs_with_diagnostics(
                    company,
                    resolved_html,
                    ats,
                    base_url=resolved_url,
                )

                if diagnostics.valid_jobs == 0 and company.careers_url == resolved_url and diagnostics.failure_type == "UNKNOWN":
                    diagnostics.failure_type = "SHELL_PAGE"

                _save_company_debug_html(
                    company.company,
                    resolved_html,
                    resolved_page.html_snapshots,
                    diagnostics.failure_type if not api_jobs else "SUCCESS",
                )
                _log_company_diagnostics(
                    company.company,
                    company.careers_url,
                    resolved_url,
                    resolved_page.fetch_method,
                    resolved_page.final_url or resolved_url,
                    resolved_html,
                    ats,
                    api_jobs,
                    diagnostics,
                    resolved_page.resolution_steps,
                )

                jobs = api_jobs[:]
                if len(jobs) < 3:
                    jobs.extend(html_jobs)

                if not jobs:
                    print(f"[scraper] {company.company} failed at parsing step with failure type: {diagnostics.failure_type}")
                    jobs = [_fallback_job(company.company, resolved_url, ats)]

                discovered_jobs.extend(jobs)
                processed_companies += 1
            except Exception as company_exc:
                print("=" * 60)
                print(f"COMPANY: {company.company}")
                print(f"FAILURE TYPE: PARSING_ERROR")
                print(f"SUCCESS: False")
                print(f"STEP BROKE: analyze_resume loop")
                print(f"ERROR: {company_exc}")
                continue

        progress(0.86, desc="Scoring matches")
        matches = [score_job_match(job, profile) for job in discovered_jobs]
        matches = sorted(matches, key=lambda item: item.score, reverse=True)

        rankings = rank_companies(matches)

        ranked_rows = [
            [r.company, r.company_score, r.match_count, r.best_role, r.ats, r.explanation]
            for r in rankings[:50]
        ]
        match_rows = [
            [m.company, m.title, m.location, m.score, m.ats, m.url, m.explanation]
            for m in matches[:250]
        ]

        profile_json = json.dumps(resume_profile_to_json(profile), indent=2)
        talking_points = build_talking_points(rankings, matches)
        status_html = _build_status_html(
            "Analysis complete",
            f"Processed {processed_companies} companies, extracted {len(match_rows)} job matches, and ranked {len(ranked_rows)} companies.",
            "success",
        )
        summary_html = _build_summary_html(ranked_rows, match_rows)
        progress(1.0, desc="Done")

        return ranked_rows, match_rows, profile_json, talking_points, status_html, summary_html
    except Exception as exc:
        return (
            [],
            [],
            json.dumps({"error": str(exc)}, indent=2),
            "",
            _build_status_html("Analysis failed", str(exc), "error"),
            empty_summary,
        )


with gr.Blocks(title="AI Career Fair Matcher") as demo:
    with gr.Column(elem_classes=["app-shell"]):
        gr.HTML(
            """
            <section class="app-hero">
                            <div class="eyebrow">AI Career Fair Matcher</div>
                            <h1 class="hero-title">Prioritize the right companies before you ever walk into the fair.</h1>
                            <p class="hero-copy">
                                Upload a resume, analyze a built-in or custom company list, and get ranked companies, matching jobs, and recruiter talking points in a clean workflow.
                            </p>
              <div class="hero-meta">
                                <div class="hero-pill"><strong>Resume Parsing</strong>Uses AI to extract structured information from your resume.</div>
                                <div class="hero-pill"><strong>Job Discovery</strong>Resolves real jobs pages behind career search shells.</div>
                                <div class="hero-pill"><strong>Actionable Output</strong>Ranked targets, matching roles, and talking points.</div>
              </div>
            </section>
            """
        )

        with gr.Row(equal_height=False):
            with gr.Column(scale=5, min_width=360, elem_classes=["panel", "control-panel"]):
                gr.Markdown(
                    """
                    <div class="section-title">
                      <h3>Workspace</h3>
                      <p>Load your resume, choose the company source, and tune how broad the analysis should be.</p>
                    </div>
                    <div class="chip-row">
                      <span class="chip">Dark Mode Default</span>
                      <span class="chip">AI Resume Parsing</span>
                      <span class="chip">Built-In NSBE Dataset</span>
                    </div>
                    """,
                    elem_classes=["section-title"],
                )

                with gr.Group(elem_classes=["subcard"]):
                    gr.Markdown("<div class='subcard-title'>Resume Upload</div>")
                    resume_input = gr.File(label="Upload resume PDF", file_types=[".pdf"], elem_classes=["upload-card"])

                with gr.Group(elem_classes=["subcard"]):
                    gr.Markdown("<div class='subcard-title'>Company Source</div>")
                    company_source_input = gr.Radio(
                        choices=["Built-in NSBE List", "Custom CSV"],
                        value="Built-in NSBE List",
                        label="Choose company source",
                    )
                    company_csv_input = gr.File(label="Optional custom company CSV", file_types=[".csv"], elem_classes=["upload-card"])

                with gr.Group(elem_classes=["subcard"]):
                    gr.Markdown("<div class='subcard-title'>Analysis Settings</div>")
                    use_ai_parser_input = gr.Checkbox(
                        value=True,
                        label="Use AI Resume Parser",
                    )
                    gr.Markdown(
                        "<div class='results-note'>Uses AI to extract structured information from your resume.</div>"
                    )
                    max_companies_input = gr.Slider(
                        minimum=5,
                        maximum=100,
                        step=1,
                        value=30,
                        label="Max companies to analyze",
                    )
                    analyze_button = gr.Button("Analyze Career Fair Fit", variant="primary")

                gr.Markdown(
                    """
                    <div class="results-note">
                      Designed for quick scanning: inputs stay compact on the left while results, summaries, and tabs stay dense and readable on the right.
                    </div>
                    """
                )

            with gr.Column(scale=7, min_width=420, elem_classes=["panel", "results-panel"]):
                gr.Markdown(
                    """
                    <div class="section-title">
                      <h3>Results</h3>
                      <p>Start with the summary, then inspect ranked companies, matching jobs, resume profile fields, and recruiter talking points.</p>
                    </div>
                    """,
                    elem_classes=["section-title"],
                )

                status_output = gr.HTML(
                    value=_build_status_html(
                        "Ready to analyze",
                        "Upload a resume, optionally add a custom CSV, and launch the matcher.",
                        "info",
                    )
                )
                summary_output = gr.HTML(value=_build_summary_html([], []))

                with gr.Group(elem_classes=["subcard"]):
                    with gr.Tabs():
                        with gr.TabItem("Ranked Companies", elem_classes=["tab-panel"]):
                            ranked_output = gr.Dataframe(
                                headers=["Company", "Score", "Matches", "Best Role", "ATS", "Explanation"],
                                label="Ranked Companies",
                                wrap=True,
                            )

                        with gr.TabItem("Matching Jobs", elem_classes=["tab-panel"]):
                            jobs_output = gr.Dataframe(
                                headers=["Company", "Job Title", "Location", "Score", "ATS", "URL", "Why It Matches"],
                                label="Matching Jobs",
                                wrap=True,
                            )

                        with gr.TabItem("Resume Profile", elem_classes=["tab-panel"]):
                            profile_output = gr.Code(label="Resume Profile JSON", language="json")

                        with gr.TabItem("Talking Points", elem_classes=["tab-panel"]):
                            talking_points_output = gr.Markdown(label="Talking Points")

    analyze_button.click(
        fn=analyze_resume,
        inputs=[resume_input, company_source_input, company_csv_input, max_companies_input, use_ai_parser_input],
        outputs=[ranked_output, jobs_output, profile_output, talking_points_output, status_output, summary_output],
    )


if __name__ == "__main__":
    demo.queue().launch(theme=APP_THEME, css=CUSTOM_CSS, ssr_mode=False)