"""compliance_planner node — dual-plan generation (crops + code queries)."""
from __future__ import annotations

import json
import re
from datetime import datetime

from google import genai
from google.genai import types

from config import GOOGLE_API_KEY, PLANNER_MODEL
from prompts.compliance_planner import COMPLIANCE_PLANNER_SYSTEM_PROMPT
from state import AgentMessage, CodeQuery, ComplianceState, CropTask


def compliance_planner(state: ComplianceState) -> dict:
    """Analyze page metadata + user question and produce dual plans for
    image cropping AND code lookup."""
    question = state["question"]
    num_pages = state.get("num_pages", 0)
    page_metadata_json = state.get("page_metadata_json", "")
    investigation_round = state.get("investigation_round", 0)

    client = genai.Client(api_key=GOOGLE_API_KEY)

    question_text = (
        f"USER COMPLIANCE QUESTION: {question}\n\n"
        f"The PDF has {num_pages} pages (1-indexed, from page 1 to page {num_pages}).\n"
        f"This is investigation round {investigation_round + 1}.\n\n"
    )

    if page_metadata_json:
        question_text += f"PAGE METADATA:\n{page_metadata_json}"
    else:
        question_text += (
            "No page metadata available. Based on the question alone, "
            "plan what code lookups are needed. Crop tasks will use default pages."
        )

    response = client.models.generate_content(
        model=PLANNER_MODEL,
        contents=[types.Content(role="user", parts=[types.Part.from_text(text=question_text)])],
        config=types.GenerateContentConfig(
            system_instruction=COMPLIANCE_PLANNER_SYSTEM_PROMPT,
        ),
    )

    response_text = response.text.strip()

    # Parse JSON response
    json_match = re.search(r"\{.*\}", response_text, re.DOTALL)

    target_pages: list[int] = []
    legend_pages: list[int] = []
    crop_tasks: list[CropTask] = []
    code_queries: list[CodeQuery] = []

    if json_match:
        try:
            parsed = json.loads(json_match.group())
            valid_0indexed = set(range(num_pages))

            target_pages = [
                int(p) - 1 for p in parsed.get("target_pages", [])
                if int(p) - 1 in valid_0indexed
            ]
            legend_pages = [
                int(p) - 1 for p in parsed.get("legend_pages", [])
                if int(p) - 1 in valid_0indexed
            ]

            for t in parsed.get("crop_tasks", []):
                raw_page = int(t.get("page_num", 1))
                crop_tasks.append(
                    CropTask(
                        page_num=raw_page - 1,
                        crop_instruction=t.get("crop_instruction", ""),
                        annotate=bool(t.get("annotate", False)),
                        annotation_prompt=t.get("annotation_prompt", ""),
                        label=t.get("label", f"Page {raw_page} crop"),
                        priority=int(t.get("priority", 1)),
                    )
                )

            for q in parsed.get("code_queries", []):
                code_queries.append(
                    CodeQuery(
                        query=q.get("query", ""),
                        focus_area=q.get("focus_area", ""),
                        context=q.get("context", ""),
                        priority=int(q.get("priority", 0)),
                    )
                )

        except (json.JSONDecodeError, ValueError, KeyError):
            pass

    # Sort crop tasks by priority
    crop_tasks.sort(key=lambda t: t["priority"])

    # Fallback: if nothing identified, use first 5 pages
    if not target_pages and not crop_tasks:
        target_pages = list(range(min(num_pages, 5)))

    # Build discussion log message
    crop_summary = f"{len(crop_tasks)} crop tasks on pages {', '.join(str(p + 1) for p in target_pages[:5])}"
    code_summary = f"{len(code_queries)} code queries"
    if code_queries:
        code_summary += f" ({', '.join(q['focus_area'] for q in code_queries[:3])})"

    discussion_msg = AgentMessage(
        timestamp=datetime.now().strftime("%H:%M:%S"),
        agent="planner",
        action="plan",
        summary=f"Planned {crop_summary} and {code_summary}.",
        detail=response_text,
        evidence_refs=[],
    )

    return {
        "target_pages": target_pages,
        "legend_pages": legend_pages,
        "crop_tasks": crop_tasks,
        "code_queries": code_queries,
        "discussion_log": [discussion_msg],
        "status_message": [
            f"Selected {len(target_pages)} pages ({len(legend_pages)} legends), "
            f"planned {len(crop_tasks)} crop tasks, {len(code_queries)} code queries."
        ],
    }