"""uofa extract — extract assessment data from evidence documents into an Excel template."""

from __future__ import annotations

from pathlib import Path

from uofa_cli.output import step_header, result_line, info, error, warn
from uofa_cli import paths

HELP = "extract assessment data from evidence documents into an Excel template"


def _model_bypasses_local_setup(model: str) -> bool:
    """True if *model* points at a remote provider that doesn't need uofa setup.

    The setup_state guard is only meaningful for the local Ollama daemon
    path. litellm targets like ``openai/gpt-4o`` or ``anthropic/claude-...``
    talk to a cloud API and have their own credential plumbing.
    """
    if model.startswith("ollama/"):
        return False
    return "/" in model  # litellm provider/model_name convention


def add_arguments(parser):
    parser.add_argument("source", nargs="*", type=Path,
                        help="file or folder path(s) containing evidence documents")
    parser.add_argument("--model", default=None,
                        help="legacy litellm model string (e.g. 'ollama/qwen3.5:4b'). "
                             "For new configs, prefer --extract-backend + --extract-model.")
    parser.add_argument("--output", "-o", type=Path,
                        help="output Excel path (default: {source}-extracted.xlsx)")
    parser.add_argument("--glob", default=None,
                        help="file filter pattern (e.g. '*.pdf' or '*.pdf,*.docx')")
    parser.add_argument("--thinking", action="store_true", default=False,
                        help="enable thinking/reasoning mode (slower, may improve accuracy)")
    parser.add_argument("--prompt-version", default=None,
                        help="tag for scoring log tracking (e.g. 'v2-detailed')")
    # New unified-LLM-config flags (spec v0.4 §3.6). Override the [llm]
    # section in uofa.toml / ~/.uofa/config.toml for this invocation only.
    parser.add_argument("--extract-backend", default=None,
                        choices=["ollama", "anthropic", "openai", "openai-compatible", "bundled", "mock"],
                        help="LLM backend for extract (overrides [llm] backend)")
    parser.add_argument("--extract-model", default=None,
                        help="model name on the chosen backend (overrides [llm] model)")
    parser.add_argument("--extract-base-url", default=None,
                        help="base URL for openai-compatible backends (e.g. Together AI, vLLM)")


def run(args) -> int:
    from uofa_cli.document_reader import discover_files, read_corpus
    from uofa_cli.llm_extractor import extract
    from uofa_cli.excel_writer import write_extraction
    from uofa_cli import setup_state

    # ── Project-aware defaults ───────────────────────────────
    project_root = paths.find_project_root()
    config = paths.load_project_config(project_root) if project_root else {}

    # Resolve pack: CLI dispatcher already sets active pack from --pack flag.
    # If no --pack was given and we're in a project, override with toml pack.
    packs = paths.resolve_active_packs(args)
    if not getattr(args, "pack", None) and config.get("pack"):
        packs = [config["pack"]]
        args.active_packs = packs
    pack_name = packs[0]

    # Resolve LLM target. Two paths:
    # - New: any --extract-* flag triggers the unified [llm] config resolver.
    # - Legacy: --model / [extract] model / setup_state.model_tag (unchanged).
    #
    # The legacy path stays bit-for-bit identical because users with existing
    # uofa.toml configs and CI scripts must continue to work. The new path is
    # only entered when the user explicitly opts in via the new flags.
    llm_config = None
    new_flags_used = any((args.extract_backend, args.extract_model, args.extract_base_url))

    if new_flags_used:
        from uofa_cli.llm import resolve_llm_config
        from uofa_cli.llm.errors import ConfigError as LLMConfigError
        cli_overrides: dict = {}
        if args.extract_backend:
            cli_overrides["backend"] = args.extract_backend
        if args.extract_model:
            cli_overrides["model"] = args.extract_model
        if args.extract_base_url:
            cli_overrides["base_url"] = args.extract_base_url
        # Convention env-var defaults so users don't have to set api_key_env
        # for the common backends.
        if cli_overrides.get("backend") in ("anthropic", "openai"):
            cli_overrides.setdefault(
                "api_key_env",
                {"anthropic": "ANTHROPIC_API_KEY", "openai": "OPENAI_API_KEY"}[cli_overrides["backend"]],
            )
        try:
            llm_config = resolve_llm_config(cli_overrides=cli_overrides)
        except LLMConfigError as exc:
            error(f"LLM configuration error: {exc.diagnostic}")
            if exc.suggestion:
                info(f"  {exc.suggestion}")
            return 1
        model = f"{llm_config.backend}/{llm_config.model}"  # for display + setup-state guard
    else:
        # Legacy resolution (preserved verbatim for back-compat).
        model = args.model
        if not model:
            model = config.get("model")
        if not model:
            cfg = setup_state.load_config()
            model = cfg.model_tag if cfg else "qwen3.5:4b"

    # ── REQ-DIST-002 AC 3: extract requires `uofa setup` for live LLM use.
    # mock + non-Ollama litellm targets (e.g. cloud APIs) bypass the check.
    if model != "mock" and not _model_bypasses_local_setup(model):
        try:
            setup_state.assert_ready()
        except setup_state.SetupNotReadyError as e:
            error(str(e))
            return 1

    # Resolve sources: CLI > uofa.toml evidence dir > error
    sources = args.source
    if not sources and config.get("evidence"):
        evidence_dir = config["evidence"]
        if evidence_dir.is_dir():
            sources = [evidence_dir]
    if not sources:
        error("No source files or directories specified.")
        info("  Usage: uofa extract <file-or-folder> [...]")
        info("  Or run inside a project with uofa.toml (evidence dir)")
        return 1

    # ── Step 1: Discover files ───────────────────────────────
    step_header(f"Discovering files...")

    file_paths, discover_warnings = discover_files(sources, glob_pattern=args.glob)
    for w in discover_warnings:
        warn(w)

    if not file_paths:
        error("No supported files found.")
        if args.glob:
            info(f"  Glob filter: {args.glob}")
        info("  Supported: .pdf, .docx, .xlsx, .csv, .tsv, .txt, .log, .md")
        return 1

    info(f"  Found {len(file_paths)} file(s):")
    for i, fp in enumerate(file_paths, 1):
        suffix = fp.suffix.lower().lstrip(".")
        info(f"    {i:>3}. {fp.name}  ({suffix.upper()})")

    # ── Step 2: Read corpus ──────────────────────────────────
    step_header("Reading files...")

    corpus = read_corpus(file_paths)
    for w in corpus.warnings:
        warn(w)

    if getattr(args, "verbose", False):
        for entry in corpus.file_manifest:
            info(f"  ✓ {entry['name']:<45} {entry['tokens']:>6} tokens")

    info(f"  Total corpus: {corpus.total_tokens:,} tokens")

    if not corpus.chunks:
        error("No text could be extracted from the source files.")
        return 1

    # ── Step 3: LLM extraction ───────────────────────────────
    step_header(f"Extracting with {model}...")

    pack_prompt_path = paths.extract_prompt()

    try:
        result = extract(
            corpus, model, pack_name, pack_prompt_path,
            thinking=getattr(args, "thinking", False),
            llm_config=llm_config,
        )
    except Exception as exc:
        error(f"Extraction failed: {exc}")
        if getattr(args, "verbose", False):
            raise
        return 1

    # Extraction summary
    n_summary = sum(1 for fe in result.assessment_summary.values() if fe.value is not None)
    n_entities = len(result.model_and_data)
    n_vr = len(result.validation_results)
    n_factors = len(result.credibility_factors)
    n_decision = sum(1 for fe in result.decision.values() if fe.value is not None)

    result_line("Assessment Summary", True, f"{n_summary} fields")
    result_line("Model & Data", True, f"{n_entities} entities")
    result_line("Validation Results", True, f"{n_vr} results")
    result_line("Credibility Factors", True, f"{n_factors} factors mapped")
    result_line("Decision", True, f"{n_decision} fields")

    # Confidence distribution
    all_confidences = []
    for fe in result.assessment_summary.values():
        if fe.value is not None:
            all_confidences.append(fe.confidence)
    for factor in result.credibility_factors:
        for fe in factor.values():
            if fe.value is not None:
                all_confidences.append(fe.confidence)
    for fe in result.decision.values():
        if fe.value is not None:
            all_confidences.append(fe.confidence)

    if all_confidences:
        high = sum(1 for c in all_confidences if c >= 0.85)
        medium = sum(1 for c in all_confidences if 0.50 <= c < 0.85)
        low = sum(1 for c in all_confidences if c < 0.50)
        if low > 0:
            warn(f"  {low} cell(s) low confidence (red)")

    # ── Step 4: Write Excel ──────────────────────────────────
    step_header("Writing Excel output...")

    # Resolve template
    template = _find_pack_template(pack_name)

    # Resolve output path
    output = args.output

    # If --output points to an existing directory, build the filename inside it.
    # (Without this, openpyxl would error downstream with the cryptic
    # "openpyxl does not support  file format" when handed a directory path.)
    if output is not None and output.exists() and output.is_dir():
        source_name = sources[0].stem if sources else "extract"
        output = output / f"{source_name}-extracted.xlsx"
    elif output is None:
        if config.get("output") and config["output"].is_dir():
            source_name = sources[0].stem if sources else "extract"
            output = config["output"] / f"{source_name}-extracted.xlsx"
        else:
            source_name = sources[0].stem if sources else "extract"
            output = Path(f"{source_name}-extracted.xlsx")

    # Validate extension before handing off to openpyxl. Catches the case where
    # --output is a filename without a supported extension, or a directory path
    # that doesn't yet exist (argparse strips trailing slashes, so we can't
    # distinguish ./out from ./out/ once it's a Path).
    _SUPPORTED_EXTS = (".xlsx", ".xlsm", ".xltx", ".xltm")
    if output.suffix.lower() not in _SUPPORTED_EXTS:
        error(f"--output must end in .xlsx (got: {output})")
        info("  Pass an existing directory to auto-generate the filename, "
             "or specify a full path ending in .xlsx")
        return 1

    output.parent.mkdir(parents=True, exist_ok=True)
    write_extraction(result, template, output, pack_name)

    # Count filled cells and confidence levels
    n_filled = len(all_confidences)
    result_line("Output", True, str(output))
    info(f"  {n_filled} cells pre-filled")
    if all_confidences:
        info(f"  {high} high confidence (green), {medium} review suggested (yellow)")

    print()
    info("Done. Review the spreadsheet, then run:")
    info(f"  uofa import {output} --sign --key <your-key> --check")

    return 0


def _find_pack_template(pack_name: str) -> Path | None:
    """Find the Excel template for a given pack."""
    try:
        manifest = paths.pack_manifest(pack_name)
        template_rel = manifest.get("template")
        if template_rel:
            template_path = paths.pack_dir(pack_name) / template_rel
            if template_path.exists():
                return template_path
    except (FileNotFoundError, KeyError):
        pass
    # Fallback: core pack template
    try:
        manifest = paths.pack_manifest("core")
        template_rel = manifest.get("template")
        if template_rel:
            template_path = paths.pack_dir("core") / template_rel
            if template_path.exists():
                return template_path
    except (FileNotFoundError, KeyError):
        pass
    return None