src/space_service.py

"""
Non-interactive RefCheck workflow for Hugging Face Spaces.
"""
from __future__ import annotations

import copy
import tempfile
from dataclasses import dataclass, field
from functools import lru_cache
from pathlib import Path
from typing import Any
from concurrent.futures import ThreadPoolExecutor, as_completed

from main import (
    apply_fix,
    get_default_workflow,
    validate_entry,
)
from src.comparator import EntryReport, MetadataComparator
from src.fetcher import (
    ArxivFetcher,
    CrossRefFetcher,
    DBLPFetcher,
    OpenAlexFetcher,
    ScholarFetcher,
    SemanticScholarFetcher,
)
from src.local_db import LocalConferenceDB
from src.parser import BibEntry, BibParser
from src.sanitizer import BibSanitizer, SanitizeFix


@dataclass
class RefCheckOptions:
    """Options for a non-interactive RefCheck run."""

    remove_unverified: bool = True
    enable_google_scholar: bool = False
    max_workers: int = 4


@dataclass
class RefCheckResult:
    """Artifacts and summary produced by a Space run."""

    source_stem: str = "references"
    total_input: int = 0
    total_output: int = 0
    verified: int = 0
    issues: int = 0
    not_found: int = 0
    entries: list[BibEntry] = field(default_factory=list)
    review_items: list[dict[str, Any]] = field(default_factory=list)
    fixed_details: dict[str, list[str]] = field(default_factory=dict)
    removed_details: list[tuple[str, str, str]] = field(default_factory=list)
    review_details: list[dict[str, Any]] = field(default_factory=list)
    duplicate_details: dict[str, list[str]] = field(default_factory=dict)
    sanitize_fixes: dict[str, list[SanitizeFix]] = field(default_factory=dict)
    local_matches: int = 0
    local_db_loaded: bool = False
    fixed_bib_path: str = ""
    report_path: str = ""
    report_markdown: str = ""


def run_refcheck_file(file_path: str | Path, options: RefCheckOptions | None = None) -> RefCheckResult:
    """Validate and fix an uploaded BibTeX file without interactive prompts."""
    options = options or RefCheckOptions()
    source_path = Path(file_path)
    parser = BibParser()
    entries = parser.parse_file(str(source_path))
    result = RefCheckResult(source_stem=source_path.stem or "references", total_input=len(entries))

    if not entries:
        result.report_markdown = "## RefCheck Report\n\nNo BibTeX entries were found."
        result.report_path = _write_report(result.report_markdown)
        result.fixed_bib_path = _write_bib(parser, [], result.source_stem)
        return result

    sanitizer = BibSanitizer()
    result.sanitize_fixes = sanitizer.sanitize_all(entries)
    _record_sanitize_fixes(result.fixed_details, result.sanitize_fixes)
    result.duplicate_details = sanitizer.find_duplicates(entries)

    result.local_db_loaded, api_entries, result.local_matches = _apply_local_db(entries, result.fixed_details)

    fetchers = _build_fetchers()
    workflow = get_default_workflow()
    for step in workflow.steps:
        if step.name == "google_scholar":
            step.enabled = options.enable_google_scholar

    comparator = MetadataComparator()
    analysis = _analyze_entries(api_entries, workflow, fetchers, comparator, options.max_workers)

    actions: dict[str, tuple[str, Any, list[Any]]] = {}

    for entry, best_result, candidates in analysis:
        if not best_result:
            actions[entry.key] = ("keep", None, [])
        elif best_result.is_match and best_result.fetched_data:
            actions[entry.key] = ("fix", best_result, candidates)
        elif candidates:
            actions[entry.key] = ("review", best_result, candidates)
        else:
            actions[entry.key] = ("remove", best_result, candidates)

    updated_entries: list[BibEntry] = []

    for entry in entries:
        action, best_result, candidates = actions.get(entry.key, ("keep", None, []))

        if action == "fix":
            changes = apply_fix(entry, best_result.fetched_data, all_candidates=candidates)
            if changes:
                result.fixed_details.setdefault(entry.key, []).extend(changes)
            updated_entries.append(entry)
        elif action == "review":
            result.review_items.append(_review_item(entry, best_result, candidates))
            updated_entries.append(entry)
        elif action == "remove":
            if options.remove_unverified:
                result.removed_details.append((entry.key, entry.title, "No matching metadata found in any source"))
            else:
                result.review_items.append(_review_item(entry, best_result, candidates))
                updated_entries.append(entry)
        else:
            updated_entries.append(entry)

    result.entries = updated_entries
    return finalize_result(result, options)


def finalize_result(result: RefCheckResult, options: RefCheckOptions | None = None) -> RefCheckResult:
    """Write current entries, re-verify them, and refresh downloadable artifacts."""
    options = options or RefCheckOptions()
    parser = BibParser()
    fetchers = _build_fetchers()
    workflow = get_default_workflow()
    for step in workflow.steps:
        if step.name == "google_scholar":
            step.enabled = options.enable_google_scholar

    comparator = MetadataComparator()
    result.review_details = [_review_payload_from_item(item) for item in result.review_items]
    result.total_output = len(result.entries)
    fixed_path = _write_bib(parser, result.entries, result.source_stem)
    result.fixed_bib_path = fixed_path

    verified_entries = parser.parse_file(fixed_path)
    verification_reports = _verify_entries(
        verified_entries,
        workflow,
        fetchers,
        comparator,
        options.max_workers,
    )
    result.verified = sum(1 for r in verification_reports if r.comparison and r.comparison.is_match)
    result.issues = sum(1 for r in verification_reports if r.comparison and r.comparison.has_issues)
    result.not_found = sum(
        1
        for r in verification_reports
        if r.comparison and not r.comparison.is_match and not r.comparison.has_issues
    )

    result.report_markdown = _build_report(result, verification_reports)
    result.report_path = _write_report(result.report_markdown)
    return result


def preview_review_action(
    result: RefCheckResult | None,
    review_index: int,
    action: str,
    candidate_index: int | None = None,
    options: RefCheckOptions | None = None,
) -> str:
    """Preview and test a manual review action without mutating the session."""
    if not result or not result.review_items:
        return "No unresolved entries are available."
    if review_index < 0 or review_index >= len(result.review_items):
        return "Select an unresolved entry first."

    options = options or RefCheckOptions()
    item = result.review_items[review_index]
    entry = _find_entry(result.entries, item["entry_key"])
    if not entry:
        return "The selected entry is no longer in the working bibliography."

    if action == "keep":
        return _entry_preview_markdown(entry, "Keep original entry", ["No metadata changes will be applied."])
    if action == "remove":
        return _entry_preview_markdown(entry, "Remove entry", ["This entry will be removed from the exported BibTeX."])
    if action != "candidate":
        return "Select a candidate, keep, or remove action."

    candidates = item.get("candidates", [])
    if candidate_index is None or candidate_index < 0 or candidate_index >= len(candidates):
        return "Select a candidate first."

    candidate = candidates[candidate_index]
    if not _candidate_exact_match(candidate):
        return _entry_preview_markdown(
            entry,
            "Candidate blocked",
            [
                "This candidate is not an exact title/author/year match, so RefCheck will not auto-apply it.",
                f"Candidate source: {candidate.source}",
                f"Candidate confidence: {candidate.confidence:.2f}",
                *_candidate_issue_lines(candidate),
            ],
        )

    temp_entry = copy.deepcopy(entry)
    changes = apply_fix(temp_entry, candidate.fetched_data, allow_optional_updates=True)
    if not changes:
        changes = ["No field-level changes are needed for this candidate."]

    fetchers = _build_fetchers()
    workflow = get_default_workflow()
    for step in workflow.steps:
        if step.name == "google_scholar":
            step.enabled = options.enable_google_scholar
    comparator = MetadataComparator()
    best_result, _ = validate_entry(temp_entry, workflow, fetchers, comparator)
    test_lines = [
        f"Candidate source: {candidate.source}",
        f"Candidate confidence before apply: {candidate.confidence:.2f}",
    ]
    if best_result:
        test_lines.extend(
            [
                f"Verification source after apply: {best_result.source}",
                f"Verification confidence after apply: {best_result.confidence:.2f}",
                f"Verified after apply: {'yes' if best_result.is_match else 'no'}",
            ]
        )
        if best_result.issues:
            test_lines.append(f"Remaining issues: {'; '.join(best_result.issues)}")

    return _entry_preview_markdown(temp_entry, "Candidate test", changes + test_lines)


def apply_review_action(
    result: RefCheckResult | None,
    review_index: int,
    action: str,
    candidate_index: int | None = None,
    options: RefCheckOptions | None = None,
) -> RefCheckResult:
    """Apply a manual review action to the working bibliography."""
    if not result or not result.review_items:
        raise ValueError("No unresolved entries are available.")
    if review_index < 0 or review_index >= len(result.review_items):
        raise ValueError("Select an unresolved entry first.")

    options = options or RefCheckOptions()
    item = result.review_items[review_index]
    entry = _find_entry(result.entries, item["entry_key"])
    if not entry:
        raise ValueError("The selected entry is no longer in the working bibliography.")

    if action == "candidate":
        candidates = item.get("candidates", [])
        if candidate_index is None or candidate_index < 0 or candidate_index >= len(candidates):
            raise ValueError("Select a candidate first.")
        candidate = candidates[candidate_index]
        if not _candidate_exact_match(candidate):
            raise ValueError(
                "Selected candidate is not an exact title/author/year match; RefCheck will not auto-overwrite core metadata."
            )
        changes = apply_fix(entry, candidate.fetched_data, allow_optional_updates=True)
        changes.append(f"Resolved manually with candidate from {candidate.source}.")
        result.fixed_details.setdefault(entry.key, []).extend(changes)
    elif action == "remove":
        result.entries = [existing for existing in result.entries if existing.key != entry.key]
        result.removed_details.append((entry.key, entry.title, "Removed during manual review"))
    elif action == "keep":
        result.fixed_details.setdefault(entry.key, []).append("Marked as manually reviewed; kept original entry.")
    else:
        raise ValueError("Select a candidate, keep, or remove action.")

    del result.review_items[review_index]
    return finalize_result(result, options)


def _find_entry(entries: list[BibEntry], key: str) -> BibEntry | None:
    for entry in entries:
        if entry.key == key:
            return entry
    return None


def _candidate_exact_match(candidate: Any) -> bool:
    return bool(
        candidate
        and getattr(candidate, "is_match", False)
        and getattr(candidate, "title_match", False)
        and getattr(candidate, "author_match", False)
        and getattr(candidate, "year_match", False)
        and not getattr(candidate, "author_initial_conflict", False)
    )


def _candidate_issue_lines(candidate: Any) -> list[str]:
    lines = list(getattr(candidate, "issues", []) or [])
    if not getattr(candidate, "title_match", False):
        lines.append("Title is not an exact-enough match")
    if not getattr(candidate, "author_match", False):
        lines.append("Authors are not an exact-enough match")
    if not getattr(candidate, "year_match", False):
        bib_year = getattr(candidate, "bib_year", "") or "[missing]"
        fetched_year = getattr(candidate, "fetched_year", "") or "[missing]"
        lines.append(f"Year mismatch: bib={bib_year}, candidate={fetched_year}")
    return [f"Blocking issue: {line}" for line in dict.fromkeys(lines)]


def _entry_preview_markdown(entry: BibEntry, title: str, lines: list[str]) -> str:
    body = "\n".join(f"- {line}" for line in lines)
    return (
        f"### {title}\n\n"
        f"**Key:** `{entry.key}`\n\n"
        f"**Title:** {entry.title or '[missing]'}\n\n"
        f"**Authors:** {entry.author or '[missing]'}\n\n"
        f"**Year:** {entry.year or '[missing]'}\n\n"
        f"{body}"
    )


def _build_fetchers() -> dict[str, Any]:
    return {
        "arxiv": ArxivFetcher(),
        "crossref": CrossRefFetcher(),
        "scholar": ScholarFetcher(),
        "semantic": SemanticScholarFetcher(),
        "openalex": OpenAlexFetcher(),
        "dblp": DBLPFetcher(),
    }


def _analyze_entries(
    entries: list[BibEntry],
    workflow: Any,
    fetchers: dict[str, Any],
    comparator: MetadataComparator,
    max_workers: int,
) -> list[tuple[BibEntry, Any, list[Any]]]:
    if not entries:
        return []

    analysis: list[tuple[BibEntry, Any, list[Any]]] = []
    worker_count = min(max(1, max_workers), len(entries))
    with ThreadPoolExecutor(max_workers=worker_count) as executor:
        futures = {
            executor.submit(validate_entry, entry, workflow, fetchers, comparator): entry
            for entry in entries
        }
        for future in as_completed(futures):
            entry = futures[future]
            try:
                best_result, candidates = future.result()
            except Exception:
                best_result, candidates = None, []
            analysis.append((entry, best_result, candidates))
    return analysis


def _verify_entries(
    entries: list[BibEntry],
    workflow: Any,
    fetchers: dict[str, Any],
    comparator: MetadataComparator,
    max_workers: int,
) -> list[EntryReport]:
    reports: list[EntryReport] = []
    for entry, best_result, _ in _analyze_entries(entries, workflow, fetchers, comparator, max_workers):
        reports.append(EntryReport(entry=entry, comparison=best_result))
    return reports


def _record_sanitize_fixes(
    fixed_details: dict[str, list[str]],
    sanitize_fixes: dict[str, list[SanitizeFix]],
) -> None:
    for key, fixes in sanitize_fixes.items():
        fixed_details.setdefault(key, [])
        fixed_details[key].extend(fix.description for fix in fixes)


def _apply_local_db(
    entries: list[BibEntry],
    fixed_details: dict[str, list[str]],
) -> tuple[bool, list[BibEntry], int]:
    local_db = _load_local_db()
    if not local_db.is_loaded:
        return False, entries, 0

    match_count = 0
    for entry in entries:
        official = local_db.lookup(entry.title)
        if official:
            match_count += 1

    return True, entries, match_count


@lru_cache(maxsize=1)
def _load_local_db() -> LocalConferenceDB:
    local_db = LocalConferenceDB()
    local_db.load()
    return local_db


def _review_item(entry: BibEntry, best_result: Any, candidates: list[Any]) -> dict[str, Any]:
    sorted_candidates = sorted(candidates, key=lambda item: item.confidence, reverse=True)
    return {
        "entry_key": entry.key,
        "entry": entry,
        "best_result": best_result,
        "candidates": sorted_candidates,
    }


def _review_payload_from_item(item: dict[str, Any]) -> dict[str, Any]:
    return _review_payload(
        item["entry"],
        item.get("best_result"),
        item.get("candidates", []),
    )


def _review_payload(entry: BibEntry, best_result: Any, candidates: list[Any]) -> dict[str, Any]:
    return {
        "key": entry.key,
        "title": entry.title,
        "reason": "; ".join(best_result.issues) if best_result and best_result.issues else "Ambiguous match",
        "candidates": [
            {
                "source": candidate.source,
                "confidence": candidate.confidence,
                "title": getattr(candidate.fetched_data, "title", ""),
                "year": getattr(candidate.fetched_data, "year", ""),
                "doi": getattr(candidate.fetched_data, "doi", ""),
            }
            for candidate in candidates[:5]
        ],
    }


def _write_bib(parser: BibParser, entries: list[BibEntry], original_stem: str) -> str:
    out_dir = Path(tempfile.mkdtemp(prefix="refcheck_"))
    out_path = out_dir / f"{original_stem or 'references'}_refcheck_fixed.bib"
    parser.save_entries(str(out_path), entries)
    return str(out_path)


def _write_report(markdown: str) -> str:
    out_dir = Path(tempfile.mkdtemp(prefix="refcheck_report_"))
    out_path = out_dir / "refcheck_report.md"
    out_path.write_text(markdown, encoding="utf-8")
    return str(out_path)


def _build_report(result: RefCheckResult, reports: list[EntryReport]) -> str:
    lines = [
        "## RefCheck Report",
        "",
        "### Summary",
        "",
        f"- Input entries: {result.total_input}",
        f"- Output entries: {result.total_output}",
        f"- Verified after fix: {result.verified}",
        f"- Remaining issues: {result.issues}",
        f"- Not found after fix: {result.not_found}",
        f"- Local DB loaded: {'yes' if result.local_db_loaded else 'no'}",
        f"- Local DB matches: {result.local_matches}",
        "",
    ]

    gate_status, gate_reasons = _submission_safety_gate(result)
    lines.extend(["### Submission Safety Gate", ""])
    lines.append(f"- Status: **{gate_status}**")
    for reason in gate_reasons:
        lines.append(f"- {reason}")
    lines.append("")

    if result.removed_details:
        lines.extend(["### Removed", ""])
        for key, title, reason in result.removed_details:
            lines.append(f"- `{key}`: {title} ({reason})")
        lines.append("")

    if result.fixed_details:
        lines.extend(["### Fixed", ""])
        for key, changes in sorted(result.fixed_details.items()):
            lines.append(f"- `{key}`")
            for change in changes:
                lines.append(f"  - {change}")
        lines.append("")

    if result.duplicate_details:
        lines.extend(["### Duplicate Titles", ""])
        for title, keys in result.duplicate_details.items():
            lines.append(f"- `{', '.join(keys)}`: {title}")
        lines.append("")

    if result.review_details:
        lines.extend(["### Needs Review", ""])
        for item in result.review_details:
            lines.append(f"- `{item['key']}`: {item['title']}")
            lines.append(f"  - Reason: {item['reason']}")
            for candidate in item["candidates"]:
                lines.append(
                    "  - Candidate: "
                    f"{candidate['source']} "
                    f"(confidence {candidate['confidence']:.2f}) "
                    f"{candidate['title']} "
                    f"{candidate['year']} "
                    f"{candidate['doi']}".strip()
                )
        lines.append("")

    remaining = [
        report
        for report in reports
        if report.comparison and not report.comparison.is_match
    ]
    if remaining:
        lines.extend(["### Verification Issues", ""])
        for report in remaining:
            comparison = report.comparison
            issues = "; ".join(comparison.issues) if comparison.issues else "Not matched"
            lines.append(
                f"- `{report.entry.key}` via {comparison.source} "
                f"(confidence {comparison.confidence:.2f}): {issues}"
            )
        lines.append("")

    return "\n".join(lines).strip() + "\n"


def _submission_safety_gate(result: RefCheckResult) -> tuple[str, list[str]]:
    reasons = []
    if result.review_details:
        reasons.append(f"FAIL: {len(result.review_details)} reference(s) still need manual review.")
    if result.issues:
        reasons.append(f"FAIL: {result.issues} reference(s) still have strict verification issues.")
    if result.not_found:
        reasons.append(f"FAIL: {result.not_found} reference(s) could not be found in configured sources.")
    if result.removed_details:
        reasons.append(
            f"FAIL: {len(result.removed_details)} reference(s) were removed; confirm the paper text no longer cites them."
        )
    if result.total_output and result.verified != result.total_output:
        reasons.append(f"FAIL: only {result.verified}/{result.total_output} output reference(s) are strictly verified.")
    if result.duplicate_details:
        reasons.append(f"WARN: {len(result.duplicate_details)} duplicate title group(s) should be checked.")

    failures = [reason for reason in reasons if reason.startswith("FAIL")]
    if failures:
        return "FAIL - do not submit yet", reasons
    return "PASS - all output references are strictly verified", reasons or ["PASS: no unresolved reference risks detected."]