"""Runtime context injection for agent operation.

Generates dynamic context about the current pipeline state,
recent issues, and accumulated knowledge.
"""

from __future__ import annotations

import logging
import os
from datetime import datetime
from typing import Any, Dict

from agent_tools.database import query_pipeline_status

logger = logging.getLogger(__name__)


def build_context(db_path: str) -> Dict[str, Any]:
    """Build a comprehensive context dict for the agent.

    This is injected into the agent's prompt at session start.
    """
    status = query_pipeline_status(db_path)

    context = {
        "timestamp": datetime.now().isoformat(),
        "database_path": db_path,
        "pipeline": status,
        "recent_reports": _list_recent_reports(),
        "accumulated_knowledge": _read_context_md(),
    }

    return context


def render_context_markdown(db_path: str) -> str:
    """Render context as markdown for prompt injection."""
    ctx = build_context(db_path)

    lines = [
        "## Current Pipeline State",
        f"",
        f"- **Motions:** {ctx['pipeline'].get('motion_count', 0):,}",
        f"- **Latest motion:** {ctx['pipeline'].get('latest_motion_date', 'N/A')}",
        f"- **SVD windows:** {ctx['pipeline'].get('svd_window_count', 0)}",
        f"- **Embeddings:** {ctx['pipeline'].get('embedding_count', 0):,}",
        f"- **Healthy:** {'Yes' if ctx['pipeline'].get('healthy') else 'No'}",
        f"",
    ]

    recent = ctx.get("recent_reports", [])
    if recent:
        lines.extend([
            "## Recent Reports",
            f"",
        ])
        for r in recent[:5]:
            lines.append(f"- {r}")
        lines.append("")

    knowledge = ctx.get("accumulated_knowledge", "")
    if knowledge:
        lines.extend([
            "## Accumulated Knowledge",
            f"",
            knowledge,
            f"",
        ])

    return "\n".join(lines)


def _list_recent_reports() -> list:
    """List recently generated reports."""
    try:
        reports_dir = "reports"
        if not os.path.exists(reports_dir):
            return []
        files = sorted(
            (f for f in os.listdir(reports_dir) if f.endswith(".md")),
            key=lambda f: os.path.getmtime(os.path.join(reports_dir, f)),
            reverse=True,
        )
        return files[:10]
    except Exception:
        return []


def _read_context_md() -> str:
    """Read accumulated knowledge from context.md."""
    try:
        path = os.path.join("agent_tools", "context.md")
        if os.path.exists(path):
            with open(path, "r", encoding="utf-8") as f:
                return f.read()
        return ""
    except Exception:
        return ""


def append_context_note(note: str) -> None:
    """Append a learning to context.md."""
    try:
        path = os.path.join("agent_tools", "context.md")
        timestamp = datetime.now().isoformat()
        with open(path, "a", encoding="utf-8") as f:
            f.write(f"\n## {timestamp}\n\n{note}\n")
    except Exception:
        logger.exception("Failed to append context note")