"""compliance_analyst node — multimodal fusion of images + code for compliance determination."""
from __future__ import annotations

import json
import re
from datetime import datetime

from google import genai
from google.genai import types

from config import ANALYZER_MODEL, GOOGLE_API_KEY
from prompts.compliance_analyst import COMPLIANCE_ANALYST_SYSTEM_PROMPT
from state import AgentMessage, CodeQuery, ComplianceState, CropTask
from tools.image_store import ImageStore


def compliance_analyst(state: ComplianceState, image_store: ImageStore) -> dict:
    """Review all cropped images AND code sections to produce compliance findings."""
    question = state["question"]
    image_refs = state.get("image_refs", [])
    code_report = state.get("code_report", "")
    legend_pages = set(state.get("legend_pages", []))
    investigation_round = state.get("investigation_round", 0)
    discussion_log = state.get("discussion_log", [])

    client = genai.Client(api_key=GOOGLE_API_KEY)

    # Build multimodal content
    content_parts: list[types.Part] = []

    # 1. User question
    content_parts.append(types.Part.from_text(text=f"USER COMPLIANCE QUESTION: {question}"))

    # 2. Code report (legal requirements)
    if code_report:
        content_parts.append(
            types.Part.from_text(
                text=f"\n=== LEGAL REQUIREMENTS FROM NYC CODE ===\n{code_report}"
            )
        )
    else:
        content_parts.append(
            types.Part.from_text(text="\n=== NO CODE SECTIONS RETRIEVED ===\n")
        )

    # 3. Discussion log summary (what previous agents found)
    if discussion_log:
        log_summary = "\n".join(
            f"[{m['timestamp']}] {m['agent']}: {m['summary']}"
            for m in discussion_log[-10:]  # Last 10 messages
        )
        content_parts.append(
            types.Part.from_text(
                text=f"\n=== AGENT DISCUSSION LOG ===\n{log_summary}"
            )
        )

    # 4. Images — legends first, then detail crops, then annotated
    legend_refs = [r for r in image_refs if r["page_num"] in legend_pages and r["crop_type"] == "crop"]
    detail_crops = [r for r in image_refs if r["page_num"] not in legend_pages and r["crop_type"] == "crop"]
    annotated_refs = [r for r in image_refs if r["crop_type"] == "annotated"]

    ordered_refs = legend_refs + detail_crops + annotated_refs

    if legend_refs:
        content_parts.append(
            types.Part.from_text(text="\n=== LEGEND / SCHEDULE CROPS (study these first) ===")
        )

    first_detail_id = detail_crops[0]["id"] if detail_crops else None
    first_annotated_id = annotated_refs[0]["id"] if annotated_refs else None

    for ref in ordered_refs:
        if first_detail_id is not None and ref["id"] == first_detail_id:
            content_parts.append(types.Part.from_text(text="\n=== DETAIL CROPS ==="))
        if first_annotated_id is not None and ref["id"] == first_annotated_id:
            content_parts.append(
                types.Part.from_text(text="\n=== ANNOTATED CROPS (highlighted versions) ===")
            )

        content_parts.append(types.Part.from_text(text=f"\nImage: {ref['label']}"))
        try:
            content_parts.append(image_store.to_gemini_part(ref))
        except Exception as e:
            content_parts.append(
                types.Part.from_text(text=f"(Could not load image: {e})")
            )

    # 5. Investigation round context
    content_parts.append(
        types.Part.from_text(
            text=(
                f"\nThis is investigation round {investigation_round + 1}. "
                "Analyze the drawings against the code requirements. "
                "If you need more evidence (crops or code lookups), include a JSON block at the end."
            )
        )
    )

    # Call Gemini
    response = client.models.generate_content(
        model=ANALYZER_MODEL,
        contents=[types.Content(role="user", parts=content_parts)],
        config=types.GenerateContentConfig(
            system_instruction=COMPLIANCE_ANALYST_SYSTEM_PROMPT,
        ),
    )

    analysis_text = response.text

    # Parse additional investigation requests
    needs_more = False
    additional_crops: list[CropTask] = []
    additional_code_queries: list[CodeQuery] = []

    json_match = re.search(
        r"```json\s*(\{.*?\"needs_more\"\s*:\s*true.*?\})\s*```",
        analysis_text,
        re.DOTALL,
    )
    if json_match:
        try:
            extra = json.loads(json_match.group(1))
            if extra.get("needs_more"):
                needs_more = True

                for t in extra.get("additional_crops", []):
                    raw_page = int(t.get("page_num", 1))
                    additional_crops.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", "Additional crop"),
                            priority=int(t.get("priority", 1)),
                        )
                    )

                for q in extra.get("additional_code_queries", []):
                    additional_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, KeyError):
            pass

        # Clean the JSON block from the analysis text
        analysis_text = analysis_text[: json_match.start()].strip()

    # Build discussion message
    if needs_more:
        summary = (
            f"Round {investigation_round + 1} analysis complete. "
            f"Requesting {len(additional_crops)} more crops and "
            f"{len(additional_code_queries)} more code lookups."
        )
    else:
        summary = f"Round {investigation_round + 1} compliance analysis complete."

    discussion_msg = AgentMessage(
        timestamp=datetime.now().strftime("%H:%M:%S"),
        agent="compliance_analyst",
        action="analyze" if not needs_more else "request_more",
        summary=summary,
        detail=analysis_text[:1500],
        evidence_refs=[ref["id"] for ref in image_refs[:5]],
    )

    result: dict = {
        "compliance_analysis": analysis_text,
        "investigation_round": investigation_round + 1,
        "needs_more_investigation": needs_more,
        "discussion_log": [discussion_msg],
        "status_message": [summary],
    }

    if additional_crops:
        result["crop_tasks"] = additional_crops
        result["additional_crop_tasks"] = additional_crops
    if additional_code_queries:
        result["additional_code_queries"] = additional_code_queries

    return result