"""System prompt + ambient context injection for journal conversations.

Distinct from the chat pipeline (services/llm.py) in three ways:
1. New persona: warm, curious listener; not a task manager.
2. Calibration rules: ask before structural changes; record_moment freely.
3. Auto-injects last ~48h of moments for ambient continuity (no notes-RAG).

Notes-RAG auto-injection MUST be disabled for journal sessions — the
journal's ambient context replaces it. Failing to disable would let notes
leak into journal sessions, violating the design's isolation invariant.
"""
from __future__ import annotations

import datetime

from fabledassistant.services.journal_search import search_journal
from fabledassistant.services.user_profile import build_profile_context

JOURNAL_PERSONA = (
    "You are the user's assistant. They've opened their journal — a day-anchored "
    "conversation surface where they record their day. Behave like the rest of "
    "the app's chat: respond conversationally, ask follow-up questions about what "
    "they just said, verify details from earlier in the conversation when "
    "relevant, and use tools naturally to act on their behalf when it makes sense. "
    "The day's prep message at the top of the conversation is your context — "
    "build on it, don't restate it."
)

JOURNAL_CALIBRATION = """\
JOURNAL-SPECIFIC TOOL GUIDANCE:

PEOPLE / PLACES — ask before creating new entries.
- If the user mentions a name you don't already know about, ASK them in plain
  language ("Who's Sarah to you?") and WAIT for the reply before calling
  save_person or save_place.
- For ambiguous references (multiple matches in their existing people/places),
  ask which one. Never guess.
- For unambiguous references to people they've already established, no need
  to ask — proceed normally.

MOMENTS — recording them is your primary job, not a "nice to have."

After every substantive user message, BEFORE you compose your reply,
check: did the user describe ANY of these?
  - An event that happened ("I went grocery shopping")
  - An encounter with a person ("had coffee with Sarah")
  - A decision ("I'm going to switch jobs")
  - An observation about themselves or the world ("the new place is loud")
  - A plan or commitment ("watching a show with Victoria tonight")
  - A feeling or state ("I'm tired", "feeling decompressed")
  - A small accomplishment or change they made ("installed the new AP")

If the answer is YES to ANY of those — CALL record_moment FIRST, before
composing your reply. This is not optional. The journal exists to capture
these beats; if you skip the call, the beat is lost.

Multiple distinct beats in one message → multiple record_moment calls,
one per beat.

WHEN LINKING ENTITIES: use the *_names parameters (person_names,
place_names, task_titles, note_titles). Server resolves them to IDs by
lookup. Do NOT pass *_ids unless you have an exact ID returned from
another tool call in this same turn. Never invent IDs.

The ONLY messages where you skip record_moment are purely meta-conversational
ones — about the journal itself or about a prior tool result ("thanks",
"no priority needed", "can you also add X to that one", "I meant tasks not
notes"). Those aren't journal beats; they're chat about the chat.

STATE-CHANGING TOOLS — use the confirmation flow.
- update_task / update_note that change state (status, completion, deletion)
  follow the standard confirmation pattern: pass `confirmed=false` first; the
  frontend shows a confirm UI; call again with `confirmed=true` after the
  user confirms.
- Pure-read tools (list_tasks, search_notes, search_journal, get_weather, etc.)
  don't need confirmation.

OTHER:
- Do NOT call set_rag_scope. The journal scope is implicit.
- Notes are not auto-retrieved here. If you need to reference a note, call
  search_notes explicitly.

RESPONSE STYLE:
- Don't apologize for the user's feelings ("I'm sorry you're feeling…"). Engage
  with what they said directly.
- Don't produce multi-option menus ("1. Show your calendar 2. List your tasks
  3. ..."). They feel like a help-desk bot. Ask one specific follow-up or take
  one specific action.
- Don't repeat a prior reply verbatim. If the user circles back on a theme,
  pick a specific concrete detail from the new message to react to.
- Match the user's length. Short message → short reply. Don't pad.
"""

PHASE_GREETINGS = {
    "morning": "Morning. What's the day looking like for you?",
    "midday": "How's it going so far?",
    "evening": "Wrapping up — how'd the day shake out?",
}


def determine_phase(
    *,
    now_local: datetime.datetime,
    day_rollover_hour: int = 4,
    morning_end_hour: int = 12,
    midday_end_hour: int = 18,
) -> str:
    """Return 'morning' | 'midday' | 'evening' for a local datetime."""
    h = now_local.hour
    if h < day_rollover_hour:
        return "evening"
    if h < morning_end_hour:
        return "morning"
    if h < midday_end_hour:
        return "midday"
    return "evening"


def phase_greeting(phase: str) -> str:
    return PHASE_GREETINGS.get(phase, PHASE_GREETINGS["morning"])


async def build_journal_system_prompt(
    *,
    user_id: int,
    day_date: datetime.date,
    user_timezone: str,
) -> str:
    """Static-then-dynamic system prompt.

    Static prefix (persona + calibration) is identical on every request,
    preserving Ollama KV cache. Dynamic suffix changes per-day.
    """
    static_block = f"{JOURNAL_PERSONA}\n\n{JOURNAL_CALIBRATION}"

    today_iso = day_date.isoformat()
    # Include the day-of-week explicitly. LLMs are unreliable at deriving
    # weekday names from ISO dates, which causes "this Friday" / "next
    # Monday" to land on the wrong calendar day.
    weekday = day_date.strftime("%A")
    tz_block = f"Today is {weekday}, {today_iso} ({user_timezone})."

    profile_context = await build_profile_context(user_id)
    profile_section = f"\n\n{profile_context}" if profile_context else ""

    ambient = await _ambient_moments_block(user_id=user_id, day_date=day_date)
    ambient_section = (
        f"\n\nRECENT JOURNAL CONTEXT (last 48h):\n{ambient}" if ambient else ""
    )

    return f"{static_block}\n\n{tz_block}{profile_section}{ambient_section}"


async def _ambient_moments_block(
    *, user_id: int, day_date: datetime.date
) -> str:
    """Render last 48h of moments as a compact text block.

    Capped at 20 moments / 1500 chars total. Distinct from RAG retrieval.
    """
    moments = await search_journal(
        user_id=user_id,
        date_from=day_date - datetime.timedelta(days=2),
        date_to=day_date,
        limit=20,
    )
    if not moments:
        return ""

    lines: list[str] = []
    total_chars = 0
    for m in moments:
        line = f"- [{m['occurred_at']}] {m['content']}"
        if total_chars + len(line) > 1500:
            break
        lines.append(line)
        total_chars += len(line)
    return "\n".join(lines)