#!/usr/bin/env python3
"""
Discussion workflow automation for CascadingDev.

Phase 1 (Basic):
  • Parse VOTE: lines and update summaries

Phase 2 (AI-Enhanced):
  • Use Claude agents to extract questions, actions, decisions
  • Track @mentions and awaiting replies
  • Maintain timeline and structured summaries
  • Process only incremental changes via git diff

Always exits 0 so pre-commit hook never blocks commits.
"""
from __future__ import annotations

import argparse
import re
import subprocess
import sys
from collections import Counter
from pathlib import Path
from typing import Iterable, Mapping, Any

VOTE_TOKEN = "vote:"
DISCUSSION_SUFFIXES = (
    ".discussion.md",
    ".design.md",
    ".review.md",
    ".plan.md",
)
SUMMARY_SUFFIX = ".sum.md"
MENTION_PATTERN = re.compile(r"@(\w+|all)")


def extract_structured_basic(text: str) -> dict[str, list]:
    """
    Derive structured discussion signals using lightweight pattern matching.

    Recognises explicit markers (Q:, TODO:, DONE:, DECISION:) and @mentions.
    """
    questions: list[dict[str, str]] = []
    action_items: list[dict[str, str]] = []
    decisions: list[dict[str, str]] = []
    mentions: list[dict[str, str]] = []
    timeline_data: dict[str, str] | None = None

    in_comment = False

    for line in text.splitlines():
        participant, remainder = _extract_participant(line)
        stripped = line.strip()
        if not stripped:
            continue
        if stripped.startswith("<!--"):
            if not stripped.endswith("-->"):
                in_comment = True
            continue
        if in_comment:
            if stripped.endswith("-->"):
                in_comment = False
            continue
        if stripped.startswith("#"):
            continue
        analysis = remainder.strip() if participant else stripped
        if not analysis:
            continue
        lowered = analysis.lower()
        participant_name = participant or "unknown"

        if timeline_data is None:
            timeline_data = {
                "participant": participant_name,
                "summary": _truncate_summary(analysis),
            }

        # Questions
        if lowered.startswith("q:") or lowered.startswith("question:"):
            _, _, body = analysis.partition(":")
            question_text = body.strip()
            if question_text:
                questions.append(
                    {"participant": participant_name, "question": question_text, "status": "OPEN"}
                )
        elif analysis.endswith("?"):
            question_text = analysis.rstrip("?").strip()
            if question_text:
                questions.append(
                    {"participant": participant_name, "question": question_text, "status": "OPEN"}
                )

        # Action items
        if lowered.startswith(("todo:", "action:")):
            _, _, body = analysis.partition(":")
            action_text = body.strip()
            if action_text:
                assignee = None
                match = MENTION_PATTERN.search(line)
                if match:
                    assignee = match.group(1)
                action_items.append(
                    {
                        "participant": participant_name,
                        "action": action_text,
                        "status": "TODO",
                        "assignee": assignee,
                    }
                )
        elif lowered.startswith("assigned:"):
            _, _, body = analysis.partition(":")
            action_text = body.strip()
            if action_text:
                # Extract assignee from @mention in the line
                assignee = participant_name  # Default to participant claiming it
                match = MENTION_PATTERN.search(line)
                if match:
                    assignee = match.group(1)
                action_items.append(
                    {
                        "participant": participant_name,
                        "action": action_text,
                        "status": "ASSIGNED",
                        "assignee": assignee,
                    }
                )
        elif lowered.startswith("done:"):
            _, _, body = analysis.partition(":")
            action_text = body.strip()
            if action_text:
                action_items.append(
                    {
                        "participant": participant_name,
                        "action": action_text,
                        "status": "DONE",
                        "completed_by": participant_name,
                    }
                )

        # Decisions
        if lowered.startswith("decision:"):
            _, _, body = analysis.partition(":")
            decision_text = body.strip()
            if decision_text:
                decisions.append(
                    {
                        "participant": participant_name,
                        "decision": decision_text,
                        "rationale": "",
                        "supporters": [],
                    }
                )

        # Mentions
        for match in MENTION_PATTERN.finditer(line):
            mentions.append(
                {
                    "from": participant_name,
                    "to": match.group(1),
                    "context": stripped,
                }
            )

    return {
        "questions": questions,
        "action_items": action_items,
        "decisions": decisions,
        "mentions": mentions,
        "timeline": timeline_data,
    }


def _truncate_summary(text: str, limit: int = 120) -> str:
    return text if len(text) <= limit else text[: limit - 1].rstrip() + "…"


def get_staged_files() -> list[Path]:
    """Return staged file paths relative to the repository root."""
    result = subprocess.run(
        ["git", "diff", "--cached", "--name-only"],
        capture_output=True,
        text=True,
        check=False,
    )
    if result.returncode != 0:
        sys.stderr.write("[workflow] warning: git diff --cached failed; assuming no staged files.\n")
        return []

    files = []
    for line in result.stdout.splitlines():
        line = line.strip()
        if line:
            files.append(Path(line))
    return files


def read_staged_file(path: Path) -> str | None:
    """
    Return the staged contents of `path` from the git index.

    Falls back to working tree contents if the file is not in the index.
    """
    spec = f":{path.as_posix()}"
    result = subprocess.run(
        ["git", "show", spec],
        capture_output=True,
        text=True,
        check=False,
    )
    if result.returncode == 0:
        return result.stdout

    if path.exists():
        try:
            return path.read_text(encoding="utf-8")
        except OSError:
            sys.stderr.write(f"[workflow] warning: unable to read {path}\n")
            return None
    return None


def find_discussions(paths: Iterable[Path]) -> list[Path]:
    """Filter staged files down to Markdown discussions (excluding summaries)."""
    discussions: list[Path] = []
    for path in paths:
        name = path.name.lower()
        if name.endswith(SUMMARY_SUFFIX):
            continue
        if any(name.endswith(suffix) for suffix in DISCUSSION_SUFFIXES):
            discussions.append(path)
    return discussions


def parse_votes(path: Path) -> Mapping[str, str]:
    """
    Parse `VOTE:` lines and return the latest vote per participant.

    A participant is inferred from the leading bullet label (e.g. `- Alice:`) when present,
    otherwise the line index is used to avoid conflating multiple votes.
    """
    latest_per_participant: dict[str, str] = {}
    text = read_staged_file(path)
    if text is None:
        return {}

    for idx, line in enumerate(text.splitlines()):
        participant_name, remaining_line = _extract_participant(line)
        
        # Now, search for "VOTE:" in the remaining_line
        lower_remaining_line = remaining_line.lower()
        marker_idx = lower_remaining_line.rfind(VOTE_TOKEN)
        
        if marker_idx == -1:
            continue # No VOTE_TOKEN found in this part of the line

        # Extract the part after VOTE_TOKEN and pass to _extract_vote_value
        vote_string_candidate = remaining_line[marker_idx + len(VOTE_TOKEN):].strip()
        
        vote_value = _extract_vote_value(vote_string_candidate)
        
        if vote_value:
            # Determine the participant key
            participant_key = participant_name if participant_name else f"line-{idx}"
            latest_per_participant[participant_key] = vote_value
    return latest_per_participant


def _extract_participant(line: str) -> tuple[str | None, str]:
    stripped = line.strip()
    if not stripped:
        return None, line
    if stripped[0] in "-*":
        parts = stripped[1:].split(":", 1)
        if len(parts) == 2:
            candidate = parts[0].strip()
            if candidate:
                return candidate, parts[1].strip()
    colon_pos = stripped.find(":")
    if colon_pos > 0:
        candidate = stripped[:colon_pos].strip()
        remainder = stripped[colon_pos + 1 :].strip()
        if candidate:
            return candidate, remainder
    return None, line


def _extract_vote_value(vote_string: str) -> str | None:
    potential_vote = vote_string.strip().upper()
    if potential_vote in ("READY", "CHANGES", "REJECT"):
        return potential_vote
    return None
def get_discussion_changes(discussion_path: Path) -> str:
    """
    Return the staged additions for a discussion file.

    When the file is newly staged, the full staged contents are returned.
    Otherwise, only the added lines from the staged diff are included.
    """
    result = subprocess.run(
        ["git", "diff", "--cached", "--unified=0", "--", discussion_path.as_posix()],
        capture_output=True,
        text=True,
        check=False,
    )

    if result.returncode != 0:
        sys.stderr.write(f"[workflow] warning: git diff --cached failed for {discussion_path}; using staged contents.\n")
        staged = read_staged_file(discussion_path)
        return staged or ""

    if not result.stdout.strip():
        staged = read_staged_file(discussion_path)
        return staged or ""

    new_lines: list[str] = []
    for line in result.stdout.splitlines():
        if line.startswith("+") and not line.startswith("+++"):
            new_lines.append(line[1:])

    if new_lines:
        return "\n".join(new_lines)

    staged = read_staged_file(discussion_path)
    return staged or ""


def update_summary_votes(summary_path: Path, votes: Mapping[str, str]) -> None:
    """
    Update the VOTES section in a summary file with current vote counts.

    Creates the summary file from template if it doesn't exist.
    Updates only the content between <!-- SUMMARY:VOTES START/END --> markers.
    """
    # If summary doesn't exist, it will be created by pre-commit hook
    # We should only update if it already exists
    if not summary_path.exists():
        return

    try:
        content = summary_path.read_text(encoding="utf-8")
    except OSError:
        sys.stderr.write(f"[workflow] warning: unable to read {summary_path}\n")
        return

    # Calculate vote counts
    counts = Counter(votes.values())
    ready = counts.get("READY", 0)
    changes = counts.get("CHANGES", 0)
    reject = counts.get("REJECT", 0)

    # Build the new votes section content
    votes_content_lines = [
        f"READY: {ready} • CHANGES: {changes} • REJECT: {reject}"
    ]

    if votes:
        for participant, vote in sorted(votes.items()):
            votes_content_lines.append(f"- {participant}: {vote}")
    else:
        votes_content_lines.append("- (no votes yet)")

    new_votes_section = "\n".join(votes_content_lines)

    # Find and replace content between markers
    import re
    pattern = r"(<!-- SUMMARY:VOTES START -->)(.*?)(<!-- SUMMARY:VOTES END -->)"

    def replacer(match):
        return f"{match.group(1)}\n## Votes (latest per participant)\n{new_votes_section}\n{match.group(3)}"

    updated_content = re.sub(pattern, replacer, content, flags=re.DOTALL)

    # Write back to file
    try:
        summary_path.write_text(updated_content, encoding="utf-8")
    except OSError:
        sys.stderr.write(f"[workflow] warning: unable to write {summary_path}\n")


def print_vote_summary(path: Path, votes: Mapping[str, str]) -> None:
    rel = path.as_posix()
    print(f"[workflow] {rel}")
    if not votes:
        print("  - No votes recorded.")
        return

    counts = Counter(votes.values())
    for vote, count in sorted(counts.items()):
        plural = "s" if count != 1 else ""
        print(f"  - {vote}: {count} vote{plural}")
    
    print("  Participants' latest votes:")
    for participant, vote in sorted(votes.items()):
        print(f"    - {participant}: {vote}")


def process_discussion_with_ai(
    discussion_path: Path,
    summary_path: Path,
    incremental_content: str
) -> dict[str, Any]:
    """
    Process discussion content with AI agents to extract structured information.

    Returns a dict with: questions, action_items, decisions, mentions
    """
    structured = extract_structured_basic(incremental_content)
    if not incremental_content.strip():
        return structured

    try:
        try:
            from automation import agents
        except ImportError:
            import agents  # type: ignore
    except ImportError:
        return structured

    normalized = agents.normalize_discussion(incremental_content)
    if normalized:
        if normalized.get("questions"):
            structured["questions"] = normalized["questions"]
        if normalized.get("action_items"):
            structured["action_items"] = normalized["action_items"]
        if normalized.get("decisions"):
            structured["decisions"] = normalized["decisions"]
        if normalized.get("mentions"):
            structured["mentions"] = normalized["mentions"]
        if normalized.get("timeline"):
            structured["timeline"] = normalized["timeline"]
    else:
        if not structured["mentions"]:
            structured["mentions"] = agents.extract_mentions(incremental_content)

    return structured


def _run_status() -> int:
    staged = get_staged_files()
    discussions = find_discussions(staged)
    if not discussions:
        print("[workflow] No staged discussion files.")
        return 0

    for discussion in discussions:
        # Parse votes from the full file (maintains latest vote per participant)
        votes = parse_votes(Path(discussion))

        # Print summary to terminal
        print_vote_summary(discussion, votes)

        # Update the corresponding .sum.md file if it exists
        dir_path = discussion.parent
        base_name = discussion.stem  # e.g., "feature-x.discussion" from "feature-x.discussion.md"
        summary_path = dir_path / f"{base_name}.sum.md"

        if summary_path.exists():
            # Get incremental changes for AI processing
            incremental_content = get_discussion_changes(Path(discussion))

            # Process with AI if available
            ai_data = process_discussion_with_ai(
                Path(discussion),
                summary_path,
                incremental_content
            )

            # Update summary file with all extracted information
            try:
                # Try both import styles (for different execution contexts)
                try:
                    from automation import summary as summary_module
                except ImportError:
                    import summary as summary_module  # type: ignore

                timeline_entry = None
                timeline_info = ai_data.get("timeline")
                if isinstance(timeline_info, dict):
                    participant = timeline_info.get("participant", "unknown")
                    summary_text = timeline_info.get("summary", "")
                    if summary_text:
                        timeline_entry = summary_module.format_timeline_entry(participant, summary_text)

                success = summary_module.update_summary_file(
                    summary_path,
                    votes=votes,
                    questions=ai_data.get("questions"),
                    action_items=ai_data.get("action_items"),
                    decisions=ai_data.get("decisions"),
                    mentions=ai_data.get("mentions"),
                    timeline_entry=timeline_entry,
                )

                if success:
                    # Stage the updated summary file
                    subprocess.run(
                        ["git", "add", str(summary_path)],
                        capture_output=True,
                        check=False,
                    )
                    print(f"[workflow]   → Updated {summary_path.as_posix()}")

            except ImportError:
                # Fall back to basic vote update
                update_summary_votes(summary_path, votes)
                subprocess.run(
                    ["git", "add", str(summary_path)],
                    capture_output=True,
                    check=False,
                )
                print(f"[workflow]   → Updated {summary_path.as_posix()} (votes only)")

    return 0


def main(argv: list[str] | None = None) -> int:
    parser = argparse.ArgumentParser(
        prog="workflow.py",
        description="CascadingDev automation workflow (Phase 1: status reporter)",
    )
    parser.add_argument(
        "--status",
        action="store_true",
        help="Print vote status for staged discussion files (default).",
    )
    args = parser.parse_args(argv)

    # Status is currently the only command; run it for --status or no args.
    return _run_status()


if __name__ == "__main__":
    sys.exit(main())