breakpilot-compliance/backend-compliance/compliance/api/agent_doc_check_routes.py

"""
Agent Document Check Routes — Multi-URL document verification.

The user provides explicit URLs + document types. No crawling needed.
Each document is loaded, expanded (accordions/tabs), text extracted,
and checked against its type-specific legal checklist.

POST /api/compliance/agent/doc-check
"""

import asyncio
import logging
import os
import uuid as _uuid
from datetime import datetime, timezone

import httpx
from fastapi import APIRouter
from pydantic import BaseModel

from compliance.services.dsi_document_checker import (
    check_document_completeness, classify_document_type,
)
from compliance.services.smtp_sender import send_email

logger = logging.getLogger(__name__)

router = APIRouter(prefix="/compliance/agent", tags=["agent"])

CONSENT_TESTER_URL = "http://bp-compliance-consent-tester:8094"


class DocCheckEntry(BaseModel):
    doc_type: str  # dse, agb, impressum, cookie, widerruf, other
    label: str
    url: str


class DocCheckRequest(BaseModel):
    entries: list[DocCheckEntry]
    recipient: str = "dsb@breakpilot.local"
    check_cookie_banner: bool = False


class CheckItem(BaseModel):
    id: str
    label: str
    passed: bool
    severity: str
    matched_text: str = ""
    level: int = 1
    parent: str | None = None
    skipped: bool = False
    hint: str = ""


class DocCheckResult(BaseModel):
    label: str
    url: str
    doc_type: str
    word_count: int = 0
    completeness_pct: int = 0
    correctness_pct: int = 0
    checks: list[CheckItem] = []
    findings_count: int = 0
    error: str = ""


class DocCheckResponse(BaseModel):
    results: list[DocCheckResult]
    cookie_banner_result: dict | None = None
    total_documents: int
    total_findings: int
    email_status: str = ""
    checked_at: str


# In-memory job store for async processing
_doc_check_jobs: dict[str, dict] = {}


class DocCheckStartResponse(BaseModel):
    check_id: str
    status: str = "running"


class DocCheckStatusResponse(BaseModel):
    check_id: str
    status: str
    progress: str = ""
    result: DocCheckResponse | None = None
    error: str = ""


@router.post("/doc-check")
async def start_doc_check(req: DocCheckRequest):
    """Start async multi-URL document check."""
    check_id = str(_uuid.uuid4())[:8]
    _doc_check_jobs[check_id] = {"status": "running", "progress": "Pruefung gestartet...", "result": None, "error": ""}
    asyncio.create_task(_run_doc_check(check_id, req))
    return DocCheckStartResponse(check_id=check_id, status="running")


@router.get("/doc-check/{check_id}")
async def get_doc_check_status(check_id: str):
    """Poll document check status."""
    job = _doc_check_jobs.get(check_id)
    if not job:
        return {"check_id": check_id, "status": "not_found"}
    return DocCheckStatusResponse(
        check_id=check_id, status=job["status"],
        progress=job.get("progress", ""), result=job.get("result"),
        error=job.get("error", ""),
    )


async def _run_doc_check(check_id: str, req: DocCheckRequest):
    """Background task: check each document."""
    try:
        results: list[DocCheckResult] = []
        total_findings = 0

        for i, entry in enumerate(req.entries):
            _doc_check_jobs[check_id]["progress"] = (
                f"Dokument {i+1}/{len(req.entries)}: {entry.label}..."
            )

            doc_results = await _check_single_document(entry)
            results.extend(doc_results)
            total_findings += sum(r.findings_count for r in doc_results)

        # Optional: Cookie banner check on first URL
        cookie_result = None
        if req.check_cookie_banner and req.entries:
            _doc_check_jobs[check_id]["progress"] = "Cookie-Banner wird geprueft..."
            cookie_result = await _check_cookie_banner(req.entries[0].url)

        # Build email report
        _doc_check_jobs[check_id]["progress"] = "Report wird erstellt..."
        summary = _build_report(results, cookie_result)
        email_result = send_email(
            recipient=req.recipient,
            subject=f"[DOKUMENTEN-PRUEFUNG] {len(results)} Dokumente geprueft",
            body_html=summary,
        )

        response = DocCheckResponse(
            results=results,
            cookie_banner_result=cookie_result,
            total_documents=len(results),
            total_findings=total_findings,
            email_status=email_result.get("status", "failed"),
            checked_at=datetime.now(timezone.utc).isoformat(),
        )

        _doc_check_jobs[check_id]["status"] = "completed"
        _doc_check_jobs[check_id]["result"] = response
        _doc_check_jobs[check_id]["progress"] = "Fertig"

    except Exception as e:
        logger.error("Doc check %s failed: %s", check_id, e)
        _doc_check_jobs[check_id]["status"] = "failed"
        _doc_check_jobs[check_id]["error"] = str(e)[:500]


async def _check_single_document(entry: DocCheckEntry) -> list[DocCheckResult]:
    """Load a single URL, expand content, extract text, split into sections,
    and check each section against its type-specific checklist.

    Returns multiple results if the page contains sub-documents
    (e.g. Cookies section, Social Media section on a DSI page).
    """
    try:
        async with httpx.AsyncClient(timeout=90.0) as client:
            resp = await client.post(
                f"{CONSENT_TESTER_URL}/dsi-discovery",
                json={"url": entry.url, "max_documents": 1},
            )
            if resp.status_code != 200:
                return [DocCheckResult(
                    label=entry.label, url=entry.url, doc_type=entry.doc_type,
                    error=f"Seite nicht erreichbar (HTTP {resp.status_code})",
                )]

            data = resp.json()
            docs = data.get("documents", [])

            doc_text = ""
            word_count = 0
            if docs:
                doc_text = docs[0].get("full_text", "") or docs[0].get("text_preview", "")
                word_count = docs[0].get("word_count", 0)

            if not doc_text or len(doc_text) < 50:
                return [DocCheckResult(
                    label=entry.label, url=entry.url, doc_type=entry.doc_type,
                    error="Kein Text extrahierbar",
                )]

            # Split text into sections and check each
            sections = _split_into_sections(doc_text, entry.label, entry.url)
            all_results: list[DocCheckResult] = []

            # Main document check (full text against primary type)
            main_result = await _run_checklist(doc_text, entry.doc_type, entry.label, entry.url, word_count)

            # Control Library deep check — DISABLED until doc-check-specific
            # Master Controls with binary pass/fail criteria are available.
            # See: zeroclaw/INSTRUCTION-master-controls-for-doc-check.md
            # Code: compliance/services/rag_document_checker.py (ready to re-enable)

            all_results.append(main_result)

            # Sub-section checks (auto-detected from headings)
            for section in sections:
                if section["word_count"] < 100:
                    continue
                sub_result = await _run_checklist(
                    section["text"], section["doc_type"],
                    section["title"], entry.url,
                    section["word_count"],
                )
                all_results.append(sub_result)

            return all_results

    except Exception as e:
        logger.warning("Doc check failed for %s: %s", entry.url, e)
        return [DocCheckResult(
            label=entry.label, url=entry.url, doc_type=entry.doc_type,
            error=str(e)[:200],
        )]


async def _run_checklist(text: str, doc_type: str, label: str, url: str, word_count: int = 0) -> DocCheckResult:
    """Run checklist against text, then LLM-verify failed checks."""
    findings = check_document_completeness(text, doc_type, label, url)

    all_checks: list[CheckItem] = []
    completeness = 0
    correctness = 0
    for f in findings:
        if "SCORE" in f.get("code", ""):
            for c in f.get("all_checks", []):
                all_checks.append(CheckItem(
                    id=c["id"], label=c["label"], passed=c["passed"],
                    severity=c["severity"], matched_text=c.get("matched_text", ""),
                    level=c.get("level", 1),
                    parent=c.get("parent"),
                    skipped=c.get("skipped", False),
                    hint=c.get("hint", ""),
                ))
            completeness = f.get("completeness_pct", 0)
            correctness = f.get("correctness_pct", 0)

    # LLM verification: re-check regex FAILs to eliminate false positives
    failed = [c for c in all_checks if not c.passed and not c.skipped and c.hint]
    if failed:
        try:
            from compliance.services.doc_checks.llm_verify import verify_failed_checks
            overturns = await verify_failed_checks(
                text,
                [{"id": c.id, "label": c.label, "hint": c.hint} for c in failed],
                label,
            )
            for c in all_checks:
                if c.id in overturns and overturns[c.id]["overturned"]:
                    c.passed = True
                    c.matched_text = f"[LLM] {overturns[c.id]['evidence']}"
                    logger.info("LLM overturned: %s in %s", c.label, label)
            # Recompute correctness after overturns
            l2_active = [c for c in all_checks if c.level == 2 and not c.skipped]
            l2_passed = sum(1 for c in l2_active if c.passed)
            if l2_active:
                correctness = round(l2_passed / len(l2_active) * 100)
        except Exception as e:
            logger.warning("LLM verification skipped: %s", e)

    non_score = [f for f in findings if "SCORE" not in f.get("code", "")]
    return DocCheckResult(
        label=label, url=url, doc_type=doc_type,
        word_count=word_count or len(text.split()),
        completeness_pct=completeness,
        correctness_pct=correctness,
        checks=all_checks, findings_count=len(non_score),
    )


# Section heading patterns → document type mapping
# ONLY sections that are genuinely separate document types with their own checklists.
# Everything else (Social Media, Betroffenenrechte, Dienste von Drittanbietern)
# is part of the parent DSI and inherits its checks.
SECTION_TYPE_MAP = [
    (r"^cookie", "cookie"),
    (r"widerrufsrecht|widerrufsbelehrung", "widerruf"),
    (r"^impressum$", "impressum"),
    (r"^(?:agb|allgemeine geschäftsbedingungen|nutzungsbedingungen)$", "agb"),
    # DSFA MUST be checked BEFORE social_media (both can contain "Social Media")
    (r"datenschutzfolge|dsfa|risikoanalyse", "dsfa"),
    (r"^social\s*media$", "social_media"),  # Standalone heading "Social Media" = DSE
    (r"datenschutzerkl(?:ae|ä)rung.*social|datenschutz\s+f(?:ue|ü)r\s+social", "social_media"),
]


def _split_into_sections(text: str, parent_label: str, url: str) -> list[dict]:
    """Split document text at major headings into sub-sections.

    Detects sections like 'Cookies', 'Social Media', 'Dienste von Drittanbietern'
    and classifies each by document type for separate checking.
    Deduplicates: if the same doc_type appears twice, texts are merged.
    """
    import re as _re
    sections: list[dict] = []
    seen_types: dict[str, int] = {}  # doc_type -> index in sections

    lines = text.split("\n")
    current_heading = ""
    current_text: list[str] = []

    def _save_section(heading: str, text_lines: list[str]) -> None:
        sec_text = "\n".join(text_lines)
        if len(sec_text.split()) < 100:
            return
        sec_type = _classify_section(heading)
        if not sec_type:
            return
        # Merge duplicate doc_types (e.g. two "Social Media" headings)
        if sec_type in seen_types:
            idx = seen_types[sec_type]
            sections[idx]["text"] += "\n\n" + sec_text
            sections[idx]["word_count"] = len(sections[idx]["text"].split())
        else:
            seen_types[sec_type] = len(sections)
            sections.append({
                "title": f"{parent_label} > {heading}",
                "text": sec_text,
                "doc_type": sec_type,
                "word_count": len(sec_text.split()),
            })

    prev_blank = False
    for line in lines:
        stripped = line.strip()
        is_heading = (
            5 < len(stripped) < 80
            and not stripped.endswith(".")
            and not stripped.endswith(",")
            and stripped[0].isupper()
            # Require preceding blank line OR line > 15 chars to avoid
            # table column headers ("Funktion", "Speicherdauer") being
            # treated as section headings
            and (prev_blank or len(stripped) > 15)
        )
        is_skip = is_heading and stripped.lower().strip() in SKIP_HEADINGS

        if is_heading and not is_skip and current_heading:
            _save_section(current_heading, current_text)

        if is_heading and not is_skip:
            current_heading = stripped
            current_text = []
        else:
            current_text.append(line)

        prev_blank = len(stripped) == 0

    # Last section
    if current_heading:
        _save_section(current_heading, current_text)

    return sections


# Headings to skip — sub-sections of other documents, not standalone
SKIP_HEADINGS = {
    "nutzungskonzept social media",  # Internal concept, no legal checklist
    "risikoabwägung und datenschutzfolgenabschätzung",  # Sub-section of DSFA
    "risikoabwaegung und datenschutzfolgenabschaetzung",
}

# Track already-seen section types to avoid duplicate sub-documents
# (e.g. two "Social Media" headings on the same page)
_DEDUP_TYPES = {"social_media", "cookie", "dsfa", "widerruf", "impressum"}


def _classify_section(heading: str) -> str | None:
    """Classify a section heading into a document type."""
    import re as _re
    heading_lower = heading.lower().strip()
    # Skip known sub-sections
    if heading_lower in SKIP_HEADINGS:
        return None
    for pattern, doc_type in SECTION_TYPE_MAP:
        if _re.search(pattern, heading_lower):
            return doc_type
    return None


async def _check_cookie_banner(url: str) -> dict | None:
    """Run cookie banner consent test on a URL."""
    try:
        async with httpx.AsyncClient(timeout=120.0) as client:
            resp = await client.post(
                f"{CONSENT_TESTER_URL}/scan",
                json={"url": url, "timeout_per_phase": 8},
            )
            if resp.status_code == 200:
                return resp.json()
    except Exception as e:
        logger.warning("Cookie banner check failed: %s", e)
    return None


def _build_report(results: list[DocCheckResult], cookie_result: dict | None) -> str:
    from .agent_doc_check_report import build_html_report
    return build_html_report(results, cookie_result)