api_server.py

"""
api_server.py — FastAPI JSON API + Streamlit reverse proxy.

This is the NEW entrypoint for the HF Space, replacing `exec streamlit run ui.py`
in start.sh. It:
  - Serves /api/health and /api/generate directly (Chrome extension targets these)
  - Launches Streamlit as a subprocess on port 8501
  - Reverse-proxies all other HTTP traffic to Streamlit via httpx
  - Proxies the Streamlit WebSocket (/_stcore/stream) via the websockets library

No ATS or LLM logic is implemented here — only wiring calls into existing src/ modules.
"""

import asyncio
import base64
import hashlib
import hmac
import io
import json
import os
import re
import shutil
import subprocess
import sys
import tempfile
import time

import httpx
import uvicorn
from fastapi import FastAPI, Header, HTTPException, UploadFile, Form, Request, WebSocket, WebSocketDisconnect, BackgroundTasks
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse
from starlette.responses import Response as StarletteResponse

# ── App init ──────────────────────────────────────────────────────────────────
app = FastAPI(title="ATS Resume API", version="1.0")

# ── CORS middleware ───────────────────────────────────────────────────────────
# Dev: allow all origins (*). Override CORS_ORIGINS with the published
# chrome-extension://<ID> value after publishing to the Chrome Web Store.
ALLOWED_ORIGINS = os.getenv("CORS_ORIGINS", "*").split(",")

app.add_middleware(
    CORSMiddleware,
    allow_origins=ALLOWED_ORIGINS,
    allow_methods=["POST", "GET", "OPTIONS"],
    allow_headers=["X-Api-Token", "Content-Type"],
    expose_headers=["Content-Disposition"],
    allow_credentials=False,  # credentials=True requires specific origin, not *
)

# ── Auth token guard (constant-time comparison) ───────────────────────────────
_SECRET = os.getenv("API_SECRET_TOKEN", "")


def _check_token(token: str | None) -> None:
    """Raise HTTP 401 if API_SECRET_TOKEN is set and the header doesn't match."""
    if not _SECRET:
        return  # No token configured → open (development mode only)
    if not token or not hmac.compare_digest(token.strip(), _SECRET.strip()):
        raise HTTPException(status_code=401, detail="auth_failed")


def _truthy(v: str | None) -> bool:
    """Parse a multipart form flag into a bool ('1'/'true'/'yes'/'on')."""
    return str(v or "").strip().lower() in ("1", "true", "yes", "on")


def _term_list(v: str | None) -> list:
    """Split a comma/newline/semicolon term list into a clean list."""
    return [t.strip() for t in re.split(r"[\n,;]+", v or "") if t.strip()]


# ── In-memory resume parse cache (single-user, per-process) ──────────────────
# sha256_hex -> parsed Resume model (avoids redundant pdfplumber parsing)
_RESUME_CACHE: dict[str, object] = {}


# ── Core generation helper ────────────────────────────────────────────────────
def generate_resume_for_api(
    pdf_bytes: bytes,
    jd_text: str,
    job_title: str,
    company: str,
    maximum_ats_mode: bool = False,
    confirmed_terms: list | None = None,
) -> tuple[str, dict]:
    """
    Run the full ATS pipeline for an uploaded resume PDF + JD text.

    Returns (docx_filepath, v2_report_dict). Raises on hard failure.
    The caller is responsible for cleaning up the returned filepath's parent
    temp directory after base64-encoding the file.
    """
    from src.resume_parser_v2 import parse_resume_pdf
    from src.resume_customizer import ResumeCustomizer
    from src.llm_client import LLMClient
    from src.providers import build_provider_chain
    import config

    # 1. Parse the uploaded PDF — use in-memory cache keyed by SHA-256 to
    #    avoid redundant pdfplumber parsing across repeated requests.
    pdf_hash = hashlib.sha256(pdf_bytes).hexdigest()
    if pdf_hash in _RESUME_CACHE:
        base_resume = _RESUME_CACHE[pdf_hash]
    else:
        with tempfile.NamedTemporaryFile(suffix=".pdf", delete=False) as tmp_pdf:
            tmp_pdf.write(pdf_bytes)
            tmp_pdf_path = tmp_pdf.name
        try:
            base_resume = parse_resume_pdf(tmp_pdf_path)
        finally:
            try:
                os.unlink(tmp_pdf_path)
            except OSError:
                pass
        _RESUME_CACHE[pdf_hash] = base_resume

    # 2. Build ResumeCustomizer pointed at a fresh temp output dir.
    tmp_out = tempfile.mkdtemp()
    llm = LLMClient()
    provider_chain = build_provider_chain(llm)

    # Use the first tailor-capable model config for the customizer constructor
    # (same logic as the batch pipeline).
    primary_cfg = next(
        (m for m in config.ASSESSMENT_MODELS if m.get("tailor") and m.get("api_key")),
        None,
    )
    customizer = ResumeCustomizer(llm, base_resume.to_flat_text(), tmp_out,
                                  fast_model_cfg=primary_cfg)

    # 3. Build a minimal job dict (same shape the batch pipeline uses).
    job: dict = {
        "title": job_title,
        "company": company,
        "description": jd_text[:16000],  # truncate to ~4000 words to avoid LLM token overflow
        "ats_keywords": "",
        "_raw_assessment": {},
        # Maximum ATS Mode flows into _generate_resume_v4 via the job dict so
        # normal PM/AI craft terms are treated as user-confirmed and woven in.
        "_maximum_ats_mode": bool(maximum_ats_mode),
        "_confirmed_terms": confirmed_terms or [],
    }

    # 4. Call _run_provider_chain (full provider fallback + READY gate + best-attempt
    #    selection). Always pass base_resume_override to skip the hardcoded disk path.
    filepath = tempfile.mktemp(suffix=".docx", dir=tmp_out)
    result_path = customizer._run_provider_chain(
        job, filepath, provider_chain, base_resume_override=base_resume
    )

    # Always render a PDF sidecar so pdf_b64 is populated even on Linux/HF where
    # docx2pdf (Windows+Word only) is unavailable — docx_to_pdf falls back to
    # reportlab. Never fail generation because the PDF render failed.
    try:
        if result_path:
            from src.pdf_writer import docx_to_pdf
            docx_to_pdf(result_path)
    except Exception as exc:
        print(f"[api/generate] pdf sidecar render failed: {exc}")

    v2_report = job.get("_v2_report", {}) or {}
    return result_path, v2_report


# ── LaTeX resume flow ─────────────────────────────────────────────────────────
def _latex_status(pct: int, maximum_ats_mode: bool, has_unresolved_high: bool) -> str:
    """Map external-style coverage % onto the project's readiness statuses."""
    from src.fit_gate import (
        READY_MAX_ATS_95_PLUS, READY_90_PLUS_EXTERNAL_ALIGNED,
        READY_95_EXTERNAL_ALIGNED, NEEDS_USER_CONFIRMATION,
        BELOW_TARGET_REPAIRABLE,
    )
    import config

    target = config.MAXIMUM_ATS.get("target_external_score", 95)
    minimum = config.MAXIMUM_ATS.get("min_external_score", 90)
    if pct >= target:
        return READY_MAX_ATS_95_PLUS if maximum_ats_mode else READY_95_EXTERNAL_ALIGNED
    if pct >= minimum:
        return READY_90_PLUS_EXTERNAL_ALIGNED
    if has_unresolved_high and maximum_ats_mode:
        return NEEDS_USER_CONFIRMATION
    return BELOW_TARGET_REPAIRABLE


def latex_flow_for_api(
    latex_src: str,
    jd_text: str,
    job_title: str,
    company: str,
    maximum_ats_mode: bool = False,
    confirmed_terms: list | None = None,
) -> tuple[dict, str]:
    """LaTeX-first flow: extract text → gate JD keywords → inject → compile PDF.

    Returns (report_dict, out_dir). The caller cleans up out_dir after encoding.
    """
    from src.latex_resume import optimize_latex_resume
    from src.candidate_vault import user_blocked_terms

    out_dir = tempfile.mkdtemp(prefix="latex_resume_")
    try:
        blocked = list(user_blocked_terms())
    except Exception:
        blocked = []

    report = optimize_latex_resume(
        latex_src, jd_text,
        maximum_ats_mode=maximum_ats_mode,
        confirmed_terms=confirmed_terms or [],
        blocked_terms=blocked,
        compile_pdf=True,
        out_dir=out_dir,
        job_title=company or job_title or "resume",
    )
    return report, out_dir


async def _generate_from_latex(
    latex_src: str, jd_text: str, job_title: str, company: str,
    max_ats: bool, conf_terms: list,
) -> JSONResponse:
    """Run the LaTeX-first generation flow and build the JSON response."""
    out_dir: str | None = None
    try:
        loop = asyncio.get_event_loop()
        report, out_dir = await loop.run_in_executor(
            None, latex_flow_for_api, latex_src, jd_text, job_title, company,
            max_ats, conf_terms,
        )

        pct = int(report.get("pct", 0) or 0)
        gated = report.get("gated", {}) or {}
        unresolved_high = [t for t, r in gated.items()
                           if str(r).startswith("ask_user")]
        status = _latex_status(pct, max_ats, bool(unresolved_high))

        # Encode the compiled PDF (if an engine produced one) and the .tex.
        pdf_b64 = None
        pdf_path = report.get("pdf_path")
        if pdf_path and os.path.exists(pdf_path):
            with open(pdf_path, "rb") as f:
                pdf_b64 = base64.b64encode(f.read()).decode("ascii")
        tex_src = report.get("tex", "")
        tex_b64 = base64.b64encode(tex_src.encode("utf-8")).decode("ascii") if tex_src else None

        # Surface WHY no PDF was produced so the panel can tell the user (engine
        # missing vs a LaTeX compile error). The .tex is always returned.
        pdf_error = None
        if not pdf_b64:
            engine = report.get("engine")
            pdf_error = "engine_missing" if not engine else "latex_error"

        # GUARANTEE a downloadable PDF even if the LaTeX engine crashed/missing:
        # render a plain reportlab PDF from the resume text. The .tex (Overleaf)
        # stays the full-design output; this is a clearly-labelled fallback.
        pdf_fallback = False
        if not pdf_b64:
            try:
                from src.latex_resume import latex_to_text, render_text_to_pdf
                fb_path = os.path.join(out_dir, "resume_fallback.pdf")
                if (render_text_to_pdf(latex_to_text(tex_src or ""), fb_path)
                        and os.path.exists(fb_path)):
                    with open(fb_path, "rb") as f:
                        pdf_b64 = base64.b64encode(f.read()).decode("ascii")
                    pdf_fallback = True
            except Exception as exc:
                print(f"[api/generate:latex] fallback pdf render failed: {exc}")

        from src.fit_gate import MAX_ATS_READY_STATUSES
        download_allowed = status in MAX_ATS_READY_STATUSES

        payload = {
            "status": status,
            "source": "latex",
            "download_allowed": bool(download_allowed),
            "scores": {
                "jd_match": pct,                  # external-style coverage is the real signal
                "ats_readability": 100,           # LaTeX → clean, parse-safe PDF
                "independent_jd_match": pct,
            },
            "maximum_ats_mode": max_ats,
            "external_coverage": {
                "expected": report.get("expected"),
                "found": report.get("found"),
                "pct": pct,
                "missing": report.get("missing", []),
            },
            "external_coverage_pct": pct,
            "coverage_report": {
                "keywords": report.get("keywords", []),
                "coverage_count": report.get("coverage_count", ""),
                "added": report.get("injected", []),
                "gated": gated,
                "needs_confirmation": unresolved_high,
            },
            "injected_terms": report.get("injected", []),
            "needs_confirmation_terms": unresolved_high,
            "latex_engine": report.get("engine"),
            "latex_compiled": bool(report.get("compiled")),
            "compile_log": (report.get("compile_log", "") or "")[-1500:],
            "pdf_error": pdf_error,
            "pdf_fallback": pdf_fallback,
            "tex_b64": tex_b64,
            "pdf_b64": pdf_b64,
            "docx_b64": None,
        }
        print(f"[api/generate:latex] max_ats={max_ats} status={status} "
              f"external_cov={pct}% compiled={payload['latex_compiled']} "
              f"engine={payload['latex_engine']} injected={len(payload['injected_terms'])}")
        # Persist generated resume to Supabase so it survives HF Space restarts
        try:
            from src.supabase_client import get_service_client, is_configured, get_owner_user_id
            if is_configured() and tex_src:
                uid = get_owner_user_id()
                if uid:
                    get_service_client().table("generated_resumes").insert({
                        "job_title": job_title,
                        "company": company,
                        "tex_source": tex_src,
                        "ats_score": pct,
                        "user_id": uid,
                    }).execute()
        except Exception as _sb_exc:
            print(f"[api/generate:latex] supabase save failed (non-fatal): {_sb_exc}")
        return JSONResponse(payload)
    except Exception as exc:
        return JSONResponse(
            {"error": "latex_generation_failed", "detail": str(exc)},
            status_code=500,
        )
    finally:
        if out_dir:
            shutil.rmtree(out_dir, ignore_errors=True)


async def _generate_from_latex_v2(
    latex_src: str, jd_text: str, job_title: str, company: str,
    max_ats: bool, conf_terms: list,
) -> JSONResponse:
    """V2: sentence-based keyword integration via LLM, same waterfall as V1."""
    out_dir: str | None = None
    try:
        from src.resume_v2_natural import generate_v2
        loop = asyncio.get_event_loop()
        out_dir = tempfile.mkdtemp(prefix="latex_v2_")
        report = await loop.run_in_executor(
            None,
            lambda: generate_v2(
                latex_src, jd_text, job_title=job_title, company=company,
                out_dir=out_dir, compile_pdf=True,
            ),
        )
        pct = int(report.get("pct") or 0)
        pdf_path = report.get("pdf_path")
        tex = report.get("tex") or latex_src
        compiled = report.get("compiled", False)

        tex_b64 = base64.b64encode(tex.encode("utf-8")).decode("ascii")
        pdf_b64 = None
        if pdf_path:
            try:
                with open(pdf_path, "rb") as _f:
                    pdf_b64 = base64.b64encode(_f.read()).decode("ascii")
            except Exception:
                pass

        pdf_error = None
        if not pdf_b64:
            engine = report.get("engine")
            pdf_error = "engine_missing" if not engine else "latex_error"

        pdf_fallback = False
        if not pdf_b64:
            try:
                from src.latex_resume import latex_to_text, render_text_to_pdf
                fb_path = os.path.join(out_dir, "resume_v2_fallback.pdf")
                if (render_text_to_pdf(latex_to_text(tex or ""), fb_path)
                        and os.path.exists(fb_path)):
                    with open(fb_path, "rb") as _f:
                        pdf_b64 = base64.b64encode(_f.read()).decode("ascii")
                    pdf_fallback = True
            except Exception as exc:
                print(f"[api/generate:v2] fallback pdf render failed: {exc}")

        from src.fit_gate import MAX_ATS_READY_STATUSES
        status = _latex_status(pct, max_ats, False)
        download_allowed = status in MAX_ATS_READY_STATUSES

        payload = {
            "status": status,
            "source": "latex_v2",
            "version": "v2",
            "download_allowed": bool(download_allowed),
            "maximum_ats_mode": max_ats,
            "scores": {
                "jd_match": pct,
                "ats_readability": 100,
                "independent_jd_match": pct,
            },
            "external_coverage": {
                "expected": report.get("expected", 0),
                "found": report.get("found", 0),
                "pct": pct,
                "missing": report.get("missing", []),
            },
            "external_coverage_pct": pct,
            "coverage_report": {
                "keywords": report.get("keywords", []),
                "coverage_count": f"{report.get('found', 0)}/{report.get('expected', 0)}",
            },
            "latex_engine": report.get("engine"),
            "latex_compiled": compiled,
            "compile_log": "",
            "pdf_error": pdf_error,
            "pdf_fallback": pdf_fallback,
            "tex_b64": tex_b64,
            "pdf_b64": pdf_b64,
            "docx_b64": None,
            "v2_models_used": report.get("v2_models_used", []),
            "v2_winner": report.get("v2_winner", ""),
            "judge_note": report.get("judge_note", ""),
            "keywords": report.get("keywords", []),
        }
        print(f"[api/generate:v2] max_ats={max_ats} status={status} "
              f"external_cov={pct}% compiled={compiled} "
              f"winner={report.get('v2_winner')} judge={report.get('judge_note')}")

        try:
            from src.supabase_client import get_service_client, is_configured, get_owner_user_id
            if is_configured() and tex:
                uid = get_owner_user_id()
                if uid:
                    get_service_client().table("generated_resumes").insert({
                        "job_title": job_title,
                        "company": company,
                        "tex_source": tex,
                        "ats_score": pct,
                        "user_id": uid,
                    }).execute()
        except Exception as _sb_exc:
            print(f"[api/generate:v2] supabase save failed (non-fatal): {_sb_exc}")

        return JSONResponse(payload)
    except Exception as exc:
        return JSONResponse(
            {"error": "v2_engine_error", "detail": str(exc)[:400]}, status_code=200
        )
    finally:
        if out_dir:
            shutil.rmtree(out_dir, ignore_errors=True)


async def _repair_from_latex(
    latex_src: str, jd_text: str, job_title: str, company: str,
    max_ats: bool, conf_terms: list, pasted_terms: list,
) -> JSONResponse:
    """LaTeX repair: inject externally-reported missing keywords + recompile."""
    out_dir: str | None = None
    try:
        from src.latex_resume import optimize_latex_resume
        from src.candidate_vault import user_blocked_terms, confirm_expansion_terms

        out_dir = tempfile.mkdtemp(prefix="latex_repair_")
        try:
            blocked = list(user_blocked_terms())
        except Exception:
            blocked = []

        loop = asyncio.get_event_loop()
        report = await loop.run_in_executor(
            None,
            lambda: optimize_latex_resume(
                latex_src, jd_text, maximum_ats_mode=max_ats,
                confirmed_terms=conf_terms, pasted_terms=pasted_terms,
                blocked_terms=blocked, compile_pdf=True, out_dir=out_dir,
                job_title=company or job_title or "resume",
            ),
        )

        pct = int(report.get("pct", 0) or 0)
        gated = report.get("gated", {}) or {}
        unresolved_high = [t for t, r in gated.items() if str(r).startswith("ask_user")]
        status = _latex_status(pct, max_ats, bool(unresolved_high))

        pdf_b64 = None
        pdf_path = report.get("pdf_path")
        if pdf_path and os.path.exists(pdf_path):
            with open(pdf_path, "rb") as f:
                pdf_b64 = base64.b64encode(f.read()).decode("ascii")
        tex_src = report.get("tex", "")
        tex_b64 = base64.b64encode(tex_src.encode("utf-8")).decode("ascii") if tex_src else None

        pdf_error = None
        if not pdf_b64:
            engine = report.get("engine")
            pdf_error = "engine_missing" if not engine else "latex_error"

        # Guarantee a downloadable PDF even if the engine crashed/missing.
        pdf_fallback = False
        if not pdf_b64:
            try:
                from src.latex_resume import latex_to_text, render_text_to_pdf
                fb_path = os.path.join(out_dir, "resume_repair_fallback.pdf")
                if (render_text_to_pdf(latex_to_text(tex_src or ""), fb_path)
                        and os.path.exists(fb_path)):
                    with open(fb_path, "rb") as f:
                        pdf_b64 = base64.b64encode(f.read()).decode("ascii")
                    pdf_fallback = True
            except Exception as exc:
                print(f"[api/repair:latex] fallback pdf render failed: {exc}")

        injected = report.get("injected", [])
        try:
            if max_ats and injected:
                confirm_expansion_terms(injected)
        except Exception:
            pass

        from src.fit_gate import MAX_ATS_READY_STATUSES
        print(f"[api/repair:latex] max_ats={max_ats} status={status} "
              f"external_cov={pct}% injected={len(injected)} "
              f"compiled={report.get('compiled')}")
        return JSONResponse({
            "status": status,
            "source": "latex",
            "download_allowed": status in MAX_ATS_READY_STATUSES,
            "scores": {"jd_match": pct, "ats_readability": 100,
                       "independent_jd_match": pct},
            "external_score": pct,
            "maximum_ats_mode": max_ats,
            "after_coverage": {"expected": report.get("expected"),
                               "found": report.get("found"), "pct": pct,
                               "missing": report.get("missing", [])},
            "added_terms": injected,
            "blocked_terms": [t for t, r in gated.items() if "blocked" in str(r)],
            "unresolved_high_risk_terms": unresolved_high,
            "coverage_report": {
                "keywords": report.get("keywords", []),
                "coverage_count": report.get("coverage_count", ""),
                "added": injected, "gated": gated,
                "needs_confirmation": unresolved_high,
            },
            "vault_added": injected if max_ats else [],
            "latex_engine": report.get("engine"),
            "latex_compiled": bool(report.get("compiled")),
            "compile_log": (report.get("compile_log", "") or "")[-1500:],
            "pdf_error": pdf_error,
            "pdf_fallback": pdf_fallback,
            "tex_b64": tex_b64,
            "pdf_b64": pdf_b64,
            "docx_b64": None,
        })
    except Exception as exc:
        return JSONResponse(
            {"error": "latex_repair_failed", "detail": str(exc)}, status_code=500
        )
    finally:
        if out_dir:
            shutil.rmtree(out_dir, ignore_errors=True)


# ── /api/health ───────────────────────────────────────────────────────────────
@app.get("/api/health")
async def health():
    """Health check. Returns 200 with {status: ok} when the server is up."""
    return {
        "status": "ok",
        "version": "1.0",
        "streamlit_pid": _streamlit_proc.pid if _streamlit_proc else None,
    }


# ── /telegram/diag ────────────────────────────────────────────────────────────
@app.get("/telegram/diag")
async def telegram_diag():
    """Report whether the Space can reach api.telegram.org (egress check). Returns
    reachability/status only — never the bot token."""
    try:
        from src.telegram_bot import diagnose
        return diagnose()
    except Exception as exc:  # noqa: BLE001
        return JSONResponse({"error": str(exc)[:200]}, status_code=500)


# ── /telegram/webhook ─────────────────────────────────────────────────────────
@app.post("/telegram/webhook")
async def telegram_webhook(request: Request, background_tasks: BackgroundTasks):
    """Telegram pushes job-link/JD messages here. We ACK 200 instantly and run the
    (slow) generation in a background task, replying with the PDF when ready."""
    # Validate the shared secret Telegram echoes back (set via setWebhook).
    secret = os.getenv("TELEGRAM_WEBHOOK_SECRET", "")
    if secret and request.headers.get("x-telegram-bot-api-secret-token") != secret:
        return JSONResponse({"ok": False, "error": "bad_secret"}, status_code=403)
    try:
        update = await request.json()
    except Exception:
        return JSONResponse({"ok": False, "error": "bad_json"}, status_code=400)

    try:
        from src.telegram_bot import process_update, is_configured
        if is_configured():
            background_tasks.add_task(process_update, update)
    except Exception as exc:  # noqa: BLE001 - never fail the webhook ACK
        print(f"[telegram] dispatch error: {exc}")
    return {"ok": True}


# ── /api/form-assist ──────────────────────────────────────────────────────────
@app.post("/api/form-assist")
async def form_assist(
    fields_json: str = Form("[]"),
    profile_json: str = Form("{}"),
    resume_latex: str = Form(""),
    jd_text: str = Form(""),
    job_title: str = Form(""),
    company: str = Form(""),
    x_api_token: str = Header(None),
):
    """Answer job-application form fields from the candidate profile, resume
    LaTeX, and optional job-description context. Used by the browser extension's
    autofill flow for the fields that can't be filled deterministically."""
    _check_token(x_api_token)

    try:
        fields = json.loads(fields_json or "[]")
        if not isinstance(fields, list):
            fields = []
    except Exception:
        fields = []
    if not fields:
        return JSONResponse({"answers": [], "answered_count": 0, "field_count": 0})

    if not (resume_latex or "").strip():
        try:
            from src.default_resume import get_default_resume_latex
            resume_latex = get_default_resume_latex()
        except Exception:  # noqa: BLE001
            resume_latex = ""

    try:
        from src.form_autofill import answer_application_fields

        loop = asyncio.get_event_loop()
        answers = await loop.run_in_executor(
            None,
            lambda: answer_application_fields(
                resume_latex=resume_latex,
                jd_text=jd_text,
                job_title=job_title,
                company=company,
                profile_json=profile_json,
                fields=fields,
            ),
        )
        return JSONResponse({
            "answers": answers,
            "answered_count": len([a for a in answers if (a.get("value") or "").strip()]),
            "field_count": len(fields),
        })
    except Exception as exc:
        return JSONResponse(
            {"error": "form_assist_failed", "detail": str(exc)[:240]},
            status_code=500,
        )


# ── /api/generate ─────────────────────────────────────────────────────────────
@app.post("/api/generate")
async def generate(
    jd_text: str = Form(""),
    jd_url: str = Form(""),                  # Telegram relay: fetch JD from a link
    job_title: str = Form(""),
    company: str = Form(""),
    maximum_ats_mode: str = Form(""),       # "1"/"true" enables Maximum ATS Mode
    user_confirmed_expansion: str = Form(""),  # alias for maximum_ats_mode
    confirmed_terms: str = Form(""),        # optional comma/newline list
    resume_latex: str = Form(""),           # LaTeX source (PRIORITISED over PDF)
    version: str = Form(""),               # "v1" or "v2"; empty → GEN_VERSION_DEFAULT env
    resume: UploadFile = None,
    x_api_token: str = Header(None),
):
    """
    Generate a tailored resume DOCX (and optional PDF) for the supplied JD.

    Request: multipart/form-data
      - jd_text   — full job description text (required unless jd_url given)
      - jd_url    — a job link; server fetches + extracts the JD (Telegram relay)
      - job_title (optional) — for recruiter pitch header
      - company   (optional) — for recruiter pitch header
      - version   — "v1" or "v2"; empty → GEN_VERSION_DEFAULT env (default v1)
      - resume    — PDF bytes; OPTIONAL — falls back to the bundled default resume
      Header: X-Api-Token — must match API_SECRET_TOKEN env var (when set)

    Response: JSON with docx_b64, pdf_b64, status, scores, quality_flag, etc.
    """
    _check_token(x_api_token)

    max_ats = _truthy(maximum_ats_mode) or _truthy(user_confirmed_expansion)
    conf_terms = _term_list(confirmed_terms)

    # ── jd_url → fetch + extract the JD server-side (used by the Telegram relay) ──
    if not (jd_text or "").strip() and (jd_url or "").strip():
        try:
            from src.jd_from_url import fetch_jd_from_url
            res = fetch_jd_from_url(jd_url.strip())
            if res.get("ok"):
                jd_text = res["jd_text"]
                job_title = job_title or res.get("job_title", "")
            else:
                return JSONResponse(
                    {"error": "jd_unreadable",
                     "detail": "Couldn't read that link server-side. Paste the job "
                               "description text instead."},
                    status_code=200,
                )
        except Exception as exc:  # noqa: BLE001
            return JSONResponse({"error": "jd_fetch_failed", "detail": str(exc)[:200]},
                                status_code=200)

    if not (jd_text or "").strip():
        return JSONResponse({"error": "jd_required",
                             "detail": "Provide jd_text or jd_url."}, status_code=200)

    # ── Resume fallback: no LaTeX and no PDF → use the bundled default resume. ────
    if not (resume_latex or "").strip() and resume is None:
        try:
            from src.default_resume import get_default_resume_latex
            resume_latex = get_default_resume_latex()
        except Exception:  # noqa: BLE001
            pass

    # ── LaTeX-first: if the user supplied LaTeX source, use it (priority over the
    #    uploaded PDF) for keyword matching + scoring, then compile to PDF. ──────
    if (resume_latex or "").strip():
        _version = (version or "").strip().lower() or os.getenv("GEN_VERSION_DEFAULT", "v1")
        if _version == "v2":
            return await _generate_from_latex_v2(
                resume_latex, jd_text, job_title, company, max_ats, conf_terms
            )
        return await _generate_from_latex(
            resume_latex, jd_text, job_title, company, max_ats, conf_terms
        )

    if resume is None:
        raise HTTPException(status_code=422, detail="resume_required")

    pdf_bytes = await resume.read()
    if not pdf_bytes:
        raise HTTPException(status_code=422, detail="resume_required")

    tmp_out: str | None = None
    result_path: str | None = None

    try:
        # Run the blocking pipeline in a thread pool to keep the event loop free.
        loop = asyncio.get_event_loop()
        result_path, v2_report = await loop.run_in_executor(
            None, generate_resume_for_api, pdf_bytes, jd_text, job_title, company,
            max_ats, conf_terms,
        )

        if not result_path:
            return JSONResponse(
                {"error": "generation_failed", "detail": "pipeline returned no output"},
                status_code=500,
            )

        # Encode DOCX as base64.
        with open(result_path, "rb") as f:
            docx_bytes = f.read()
        docx_b64 = base64.b64encode(docx_bytes).decode("ascii")

        # Look for an optional PDF sidecar (same stem, .pdf) from pdf_writer.
        pdf_b64: str | None = None
        pdf_sidecar = os.path.splitext(result_path)[0] + ".pdf"
        if os.path.exists(pdf_sidecar):
            with open(pdf_sidecar, "rb") as f:
                pdf_b64 = base64.b64encode(f.read()).decode("ascii")

        # Map report keys to the response contract (see 06-RESEARCH.md Section 2).
        est_scores = v2_report.get("estimated_scores", {}) or {}
        response_payload = {
            "status": v2_report.get("status", "NEEDS_USER_INPUT"),
            "download_allowed": bool(v2_report.get("download_allowed", False)),
            "scores": {
                "jd_match": est_scores.get("jd_match"),
                "ats_readability": est_scores.get("ats_readability"),
                "independent_jd_match": v2_report.get("independent_jd_match"),
            },
            "quality_flag": v2_report.get("quality_flag", ""),
            "review_terms": v2_report.get("review_terms", []),
            "high_risk_terms": v2_report.get("high_risk_terms",
                                            v2_report.get("high_risk_terms_for_confirmation", [])),
            "maximum_ats_mode": max_ats,
            "external_coverage": v2_report.get("external_coverage", {}),
            "external_coverage_pct": v2_report.get("external_coverage_pct"),
            "coverage_report": v2_report.get("coverage_report", {}),
            "keyword_coverage": v2_report.get("keyword_coverage", {}),
            "docx_b64": docx_b64,
            "pdf_b64": pdf_b64,
        }
        print(f"[api/generate] maximum_ats_mode={max_ats} status={response_payload['status']} "
              f"internal={response_payload['scores'].get('jd_match')} "
              f"external_cov={response_payload.get('external_coverage_pct')}")
        return JSONResponse(response_payload)

    except HTTPException:
        raise
    except Exception as exc:
        return JSONResponse(
            {"error": "generation_failed", "detail": str(exc)},
            status_code=500,
        )
    finally:
        # Clean up the temp directory that generate_resume_for_api created.
        if result_path:
            parent = os.path.dirname(result_path)
            shutil.rmtree(parent, ignore_errors=True)


# ── External ATS Feedback Repair helper ──────────────────────────────────────
def repair_resume_for_api(
    pdf_bytes: bytes, jd_text: str, job_title: str, company: str,
    feedback: str, missing_keywords: list, external_score,
    maximum_ats_mode: bool = False, confirmed_terms: list | None = None,
    target_external_score=None,
) -> dict:
    """Re-tailor using pasted external-checker feedback. Honest: every keyword is
    risk-classified; BLOCKED + unconfirmed HIGH terms are never added. Returns the
    enriched repair result dict (incl. resume_path + coverage_report)."""
    from src.resume_parser_v2 import parse_resume_pdf
    from src.llm_client import LLMClient
    from src.jobalytics_repair import repair_with_external_feedback

    pdf_hash = hashlib.sha256(pdf_bytes).hexdigest()
    if pdf_hash in _RESUME_CACHE:
        base_resume = _RESUME_CACHE[pdf_hash]
    else:
        with tempfile.NamedTemporaryFile(suffix=".pdf", delete=False) as tmp_pdf:
            tmp_pdf.write(pdf_bytes)
            tmp_pdf_path = tmp_pdf.name
        try:
            base_resume = parse_resume_pdf(tmp_pdf_path)
        finally:
            try:
                os.unlink(tmp_pdf_path)
            except OSError:
                pass
        _RESUME_CACHE[pdf_hash] = base_resume

    tmp_out = tempfile.mkdtemp()
    job = {
        "title": job_title, "company": company,
        "description": (jd_text or "")[:16000], "ats_keywords": "", "_raw_assessment": {},
    }
    result = repair_with_external_feedback(
        job, feedback_text=feedback or None,
        missing_keywords=missing_keywords or None,
        external_score=external_score, llm=LLMClient(),
        base_resume=base_resume, output_dir=tmp_out,
        maximum_ats_mode=maximum_ats_mode, confirmed_terms=confirmed_terms,
        target_external_score=target_external_score,
    )
    # Render a PDF sidecar for the repaired DOCX (reportlab fallback off-Windows)
    # so the repair PDF button enables too. Non-fatal on failure.
    try:
        rp = result.get("resume_path")
        if rp:
            from src.pdf_writer import docx_to_pdf
            docx_to_pdf(rp)
    except Exception:
        pass
    # Persist user-confirmed expansion terms to the vault so future resumes treat
    # them as safe (only the terms actually added under confirmation).
    try:
        if maximum_ats_mode and not result.get("error"):
            from src.candidate_vault import confirm_expansion_terms
            confirm_expansion_terms(result.get("added_terms", []))
            if confirmed_terms:
                confirm_expansion_terms(confirmed_terms)
            result["vault_added"] = result.get("added_terms", [])
    except Exception:
        pass
    return result


# ── /api/repair-with-feedback ─────────────────────────────────────────────────
@app.post("/api/repair-with-feedback")
async def repair_with_feedback(
    jd_text: str = Form(...),
    job_title: str = Form(""),
    company: str = Form(""),
    feedback: str = Form(""),
    missing_keywords: str = Form(""),   # optional comma/newline list
    external_score: str = Form(""),
    maximum_ats_mode: str = Form(""),
    user_confirmed_expansion: str = Form(""),
    confirmed_terms: str = Form(""),
    target_external_score: str = Form(""),
    resume_latex: str = Form(""),           # LaTeX source (PRIORITISED over PDF)
    resume: UploadFile = None,
    x_api_token: str = Header(None),
):
    """External ATS Feedback Repair Mode. Paste Jobalytics/Simplify feedback (or
    an explicit missing-keyword list); re-tailor honestly and re-score from the
    re-parsed export. BLOCKED + unconfirmed HIGH terms are never fabricated."""
    _check_token(x_api_token)
    if not feedback.strip() and not missing_keywords.strip():
        raise HTTPException(status_code=422, detail="feedback_or_keywords_required")

    max_ats = _truthy(maximum_ats_mode) or _truthy(user_confirmed_expansion)
    conf_terms = _term_list(confirmed_terms) or None

    # ── LaTeX-first repair: inject the externally-reported missing keywords into
    #    the user's LaTeX and recompile. Pasted terms come from an explicit list
    #    AND/OR the parsed Jobalytics/Simplify feedback text. ───────────────────
    if (resume_latex or "").strip():
        pasted = _term_list(missing_keywords)
        if feedback.strip():
            try:
                from src.jobalytics_repair import parse_external_feedback
                parsed = parse_external_feedback(feedback)
                pasted = list(dict.fromkeys(pasted + parsed.get("missing_keywords", [])))
            except Exception:
                pass
        return await _repair_from_latex(
            resume_latex, jd_text, job_title, company, max_ats,
            conf_terms or [], pasted,
        )

    if resume is None:
        raise HTTPException(status_code=422, detail="resume_required")
    pdf_bytes = await resume.read()
    if not pdf_bytes:
        raise HTTPException(status_code=422, detail="resume_required")

    mk = _term_list(missing_keywords) or None
    ext = None
    try:
        ext = int(re.sub(r"[^\d]", "", external_score)) if external_score.strip() else None
    except ValueError:
        ext = None
    tgt = None
    try:
        tgt = int(re.sub(r"[^\d]", "", target_external_score)) if target_external_score.strip() else None
    except ValueError:
        tgt = None
    max_ats = _truthy(maximum_ats_mode) or _truthy(user_confirmed_expansion)
    conf_terms = _term_list(confirmed_terms) or None

    result_path = None
    try:
        loop = asyncio.get_event_loop()
        result = await loop.run_in_executor(
            None, repair_resume_for_api, pdf_bytes, jd_text, job_title, company,
            feedback, mk, ext, max_ats, conf_terms, tgt,
        )
        print(f"[api/repair] maximum_ats_mode={max_ats} status={result.get('status')} "
              f"after_cov={result.get('after_coverage', {}).get('pct')} "
              f"added={len(result.get('added_terms', []))}")
        if result.get("error"):
            return JSONResponse(
                {"error": result["error"], "detail": result.get("detail", "")},
                status_code=400 if result["error"] == "no_missing_keywords" else 500,
            )

        result_path = result.get("resume_path")
        docx_b64 = pdf_b64 = None
        if result_path and os.path.exists(result_path):
            with open(result_path, "rb") as f:
                docx_b64 = base64.b64encode(f.read()).decode("ascii")
            pdf_sidecar = os.path.splitext(result_path)[0] + ".pdf"
            if os.path.exists(pdf_sidecar):
                with open(pdf_sidecar, "rb") as f:
                    pdf_b64 = base64.b64encode(f.read()).decode("ascii")

        sc = result.get("scores", {})
        return JSONResponse({
            "status": result.get("status", "NEEDS_USER_INPUT"),
            "download_allowed": bool(result.get("download_allowed", False)),
            "scores": {
                "jd_match": sc.get("internal_jd_match"),
                "ats_readability": sc.get("ats_readability"),
                "independent_jd_match": sc.get("independent_jd_match"),
            },
            "external_score": result.get("external_score"),
            "target_external_score": result.get("target_external_score"),
            "maximum_ats_mode": result.get("maximum_ats_mode", max_ats),
            "before_coverage": result.get("before_coverage"),
            "after_coverage": result.get("after_coverage"),
            "added_terms": result.get("added_terms", []),
            "review_flag_terms": result.get("review_flag_terms", []),
            "unresolved_high_risk_terms": result.get("unresolved_high_risk_terms", []),
            "blocked_terms": result.get("blocked_terms", []),
            "already_present_terms": result.get("already_present_terms", []),
            "still_missing_repairable": result.get("still_missing_repairable", []),
            "coverage_report": result.get("coverage_report", {}),
            "below_target_explanation": result.get("below_target_explanation", ""),
            "vault_added": result.get("vault_added", []),
            "docx_b64": docx_b64,
            "pdf_b64": pdf_b64,
        })
    except HTTPException:
        raise
    except Exception as exc:
        return JSONResponse({"error": "repair_failed", "detail": str(exc)}, status_code=500)
    finally:
        if result_path:
            shutil.rmtree(os.path.dirname(result_path), ignore_errors=True)


# ── Streamlit subprocess ──────────────────────────────────────────────────────
_streamlit_proc: subprocess.Popen | None = None
_STREAMLIT_PORT = 8501
_DEBUG_LOG_PATH = "debug-03623a.log"


def _debug_log(run_id: str, hypothesis_id: str, location: str, message: str, data: dict) -> None:
    """Append one NDJSON runtime debug event for this session."""
    payload = {
        "sessionId": "03623a",
        "runId": run_id,
        "hypothesisId": hypothesis_id,
        "location": location,
        "message": message,
        "data": data,
        "timestamp": int(time.time() * 1000),
    }
    try:
        with open(_DEBUG_LOG_PATH, "a", encoding="utf-8") as f:
            f.write(json.dumps(payload, separators=(",", ":")) + "\n")
    except Exception:
        pass


def start_streamlit() -> None:
    """Launch Streamlit on an internal port so the proxy can reach it."""
    global _streamlit_proc
    _streamlit_proc = subprocess.Popen(
        [
            sys.executable,
            "-m",
            "streamlit",
            "run",
            "ui.py",
            "--server.port",
            str(_STREAMLIT_PORT),
            "--server.address",
            "127.0.0.1",
            "--server.headless",
            "true",
            # Required for reverse-proxy operation: without these, Streamlit's
            # CORS/XSRF middleware rejects requests whose Origin header comes
            # from the public HF Space domain rather than 127.0.0.1:8501.
            "--server.enableCORS",
            "false",
            "--server.enableXsrfProtection",
            "false",
        ]
    )
    # #region agent log
    _debug_log(
        run_id="open-check-1",
        hypothesis_id="H4",
        location="api_server.py:start_streamlit",
        message="streamlit_process_started",
        data={"pid": _streamlit_proc.pid, "port": _STREAMLIT_PORT},
    )
    # #endregion


# ── WebSocket proxy for /_stcore/stream (Streamlit live updates) ──────────────
@app.websocket("/_stcore/stream")
async def ws_proxy(websocket: WebSocket):
    """
    Bidirectional WebSocket proxy to Streamlit's internal port.
    httpx does NOT handle WebSocket upgrades — this route handles that separately.
    """
    import websockets as _ws
    import websockets.exceptions as _ws_exc

    # Streamlit 1.45+ requires the 'streamlit' WebSocket subprotocol to be
    # negotiated during the handshake. Without it the upstream closes immediately.
    # Extract whatever subprotocols the browser offered and mirror them back.
    subprotocols_raw = websocket.headers.get("sec-websocket-protocol", "")
    subprotocols = [p.strip() for p in subprotocols_raw.split(",") if p.strip()]

    await websocket.accept(subprotocol=subprotocols[0] if subprotocols else None)
    # #region agent log
    _debug_log(
        run_id="open-check-1",
        hypothesis_id="H3",
        location="api_server.py:ws_proxy",
        message="ws_client_connected",
        data={"client": str(websocket.client), "subprotocols": subprotocols},
    )
    # #endregion
    upstream_url = (
        f"ws://127.0.0.1:{_STREAMLIT_PORT}/_stcore/stream"
        f"?{websocket.scope.get('query_string', b'').decode()}"
    )

    try:
        # Pass the same subprotocol(s) to the upstream Streamlit WebSocket.
        connect_kwargs: dict = {}
        if subprotocols:
            connect_kwargs["subprotocols"] = subprotocols
        async with _ws.connect(upstream_url, **connect_kwargs) as upstream:

            async def client_to_upstream():
                # Use receive() directly to avoid consuming a frame on type
                # mismatch (receive_bytes/receive_text each consume the frame
                # before raising, so the double-receive pattern loses messages).
                try:
                    while True:
                        msg = await websocket.receive()
                        if msg["type"] == "websocket.disconnect":
                            break
                        data = msg.get("bytes")
                        text = msg.get("text")
                        if data is not None:
                            await upstream.send(data)
                        elif text is not None:
                            await upstream.send(text)
                except Exception:
                    pass

            async def upstream_to_client():
                try:
                    async for message in upstream:
                        if isinstance(message, bytes):
                            await websocket.send_bytes(message)
                        else:
                            await websocket.send_text(message)
                except _ws_exc.ConnectionClosed:
                    pass

            await asyncio.gather(client_to_upstream(), upstream_to_client())

    except Exception as exc:
        # #region agent log
        _debug_log(
            run_id="open-check-1",
            hypothesis_id="H3",
            location="api_server.py:ws_proxy",
            message="ws_upstream_connect_or_proxy_failed",
            data={"error": str(exc)[:200]},
        )
        # #endregion
    finally:
        try:
            await websocket.close()
        except Exception:
            pass


# ── HTTP reverse proxy catch-all → Streamlit ─────────────────────────────────
# FastAPI routes (/api/*) take priority; everything else is proxied to Streamlit.
@app.api_route(
    "/{path:path}",
    methods=["GET", "POST", "PUT", "DELETE", "OPTIONS", "HEAD"],
)
async def proxy(request: Request, path: str):
    """Proxy all non-/api/* requests to the internal Streamlit server."""
    # Never proxy /api/* to Streamlit — if it reaches here it's an unknown API
    # route; return a clean JSON 404 (prevents a confusing blank Streamlit page).
    if path == "api" or path.startswith("api/"):
        return JSONResponse({"error": "not_found", "path": "/" + path}, status_code=404)

    # #region agent log
    _debug_log(
        run_id="open-check-1",
        hypothesis_id="H2",
        location="api_server.py:proxy_entry",
        message="proxy_request_received",
        data={"method": request.method, "path": path},
    )
    # #endregion
    url = f"http://127.0.0.1:{_STREAMLIT_PORT}/{path}"
    params = dict(request.query_params)
    headers = {
        k: v for k, v in request.headers.items() if k.lower() != "host"
    }
    body = await request.body()

    async with httpx.AsyncClient(timeout=60.0) as client:
        try:
            resp = await client.request(
                method=request.method,
                url=url,
                headers=headers,
                content=body,
                params=params,
            )
            # httpx already DECOMPRESSED resp.content, so we must drop the
            # upstream content-encoding/length (and hop-by-hop headers) — copying
            # them makes the browser try to gunzip plain bytes → blank page.
            _DROP = {"content-encoding", "content-length", "transfer-encoding",
                     "connection", "keep-alive"}
            clean_headers = {k: v for k, v in resp.headers.items()
                             if k.lower() not in _DROP}
            # #region agent log
            _debug_log(
                run_id="open-check-1",
                hypothesis_id="H2",
                location="api_server.py:proxy_response",
                message="proxy_upstream_response",
                data={
                    "path": path,
                    "status_code": resp.status_code,
                    "content_type": resp.headers.get("content-type", ""),
                },
            )
            # #endregion
            return StarletteResponse(
                content=resp.content,
                status_code=resp.status_code,
                headers=clean_headers,
                media_type=resp.headers.get("content-type"),
            )
        except httpx.ConnectError as exc:
            # #region agent log
            _debug_log(
                run_id="open-check-1",
                hypothesis_id="H4",
                location="api_server.py:proxy_connect_error",
                message="streamlit_connect_error",
                data={"path": path, "error": str(exc)[:200]},
            )
            # #endregion
            # Streamlit may still be starting up — return a friendly retry message.
            return StarletteResponse(
                content=b"Streamlit is starting, please wait...",
                status_code=503,
                headers={"Retry-After": "5"},
            )


# ── Entrypoint ────────────────────────────────────────────────────────────────
if __name__ == "__main__":
    start_streamlit()
    uvicorn.run(app, host="0.0.0.0", port=7860, log_level="info")