"""Gemini(Nano Banana 등) 기반 조감도 AI 렌더링 워커.

scanvas_maker.SCanvasApp의 백그라운드 스레드에서 호출되며, app 인스턴스를 통해
상태(capture_image / job_logger / log / after / dxf_path / 등)에 접근한다.
scanvas_maker로부터 ~264줄을 분리해 AI 호출 로직을 모듈 단위로 격리.

공개 API:
    run_gemini_render(app, credential, prompt, use_vertex=False, location="us-central1")
"""
from __future__ import annotations

import io
import os
import time as _time
from pathlib import Path
from tkinter import messagebox

from PIL import Image
import contextlib


# Harness 의존 (선택적 — 없어도 동작)
try:
    from harness.seed_manager import SeedManager
    from harness.logger import get_db_session
    _HARNESS_OK = True
except Exception:
    SeedManager = None  # type: ignore
    get_db_session = None  # type: ignore
    _HARNESS_OK = False


def run_gemini_render(app, credential: str, prompt: str,
                       use_vertex: bool = False,
                       location: str = "us-central1") -> None:
    """Gemini 자동 호출 + Harness 통합. app = SCanvasApp 인스턴스.

    Args:
        app: scanvas_maker.SCanvasApp (상태/로그/UI scheduling 접근)
        credential: use_vertex=True이면 GCP Project ID, 아니면 API Key
        prompt: 렌더 프롬프트
        use_vertex: True면 Vertex AI (google-genai SDK), False면 API Key 경로
        location: Vertex AI region ("global"=gemini-3.x, "us-central1"=2.x)
    """
    t_start = _time.time()
    job_id = None
    db = None

    dxf_hash = app._get_dxf_hash()
    prompt_hash = app._get_prompt_hash(prompt)
    prompt_ver = "v1"
    seed = 0

    if app.job_logger and _HARNESS_OK:
        try:
            db = get_db_session()
            job = app.job_logger.create_job(db, app.dxf_path or "unknown", dxf_hash)
            job_id = job.id
            seed = app.seed_mgr.get_or_create_seed(db, job_id, dxf_hash)
            if app.prompt_reg:
                prompt_ver = app.prompt_reg.latest_version() or "v1"
            app.job_logger.start_job(db, job_id, seed, prompt_ver, prompt_hash)
            app.after(0, lambda: app.log(
                f"  Harness: job#{job_id}, {SeedManager.describe(seed)}, prompt={prompt_ver}"))
        except Exception as e:
            app.after(0, lambda e=e: app.log(f"  Harness 초기화 경고: {e}"))

    try:
        from google import genai
        from google.genai import types as gtypes
        sdk_version = "vertex" if use_vertex else "new"
    except ImportError:
        if use_vertex:
            if app.job_logger and db and job_id:
                app.job_logger.fail_job(db, job_id, "google-genai SDK 미설치")
            app.after(0, lambda: messagebox.showerror("패키지 필요",
                "Vertex AI는 google-genai SDK가 필요합니다.\n"
                "pip install google-genai\n"
                "그리고 gcloud auth application-default login"))
            return
        try:
            import google.generativeai as genai_legacy  # type: ignore
            sdk_version = "legacy"
        except ImportError:
            if app.job_logger and db and job_id:
                app.job_logger.fail_job(db, job_id, "google-genai SDK 미설치")
            app.after(0, lambda: messagebox.showerror("패키지 필요",
                "pip install google-generativeai\n또는\npip install google-genai"))
            return

    try:
        source_img = app.capture_image if app.capture_image else app.guide_image
        input_path = os.path.abspath("capture_for_ai.png")
        source_img.save(input_path)
        app.after(0, lambda: app.log(f"  입력 이미지: {source_img.size}"))

        with open(input_path, "rb") as f:
            img_bytes = f.read()

        render_prompt = (
            f"Transform this aerial/satellite terrain capture into a photorealistic "
            f"bird's-eye view architectural rendering. "
            f"CRITICAL: Preserve the EXACT terrain layout, roads, water, structures. "
            f"The scene may combine a high-detail DXF area (center) with a lower-detail "
            f"real DEM+satellite outer ring (edges). Render BOTH areas seamlessly — "
            f"the outer ring is actual surrounding terrain, NOT filler to crop out. "
            f"Keep the full image frame; do NOT trim to the central bbox. "
            f"Dark road areas = freshly paved asphalt. Cut slopes = exposed earth. "
            f"Enhance vegetation textures, water reflections, natural lighting. "
            f"Style: {prompt}"
        )

        app.after(0, lambda: app.log(
            f"  Gemini API 호출 중... (SDK: {sdk_version}, "
            f"loc: {location if use_vertex else '-'})"))

        rendered = None

        if sdk_version == "vertex":
            try:
                client = genai.Client(
                    vertexai=True,
                    project=credential,
                    location=location,
                )
            except Exception as ce:
                err = str(ce)[:160]
                app.after(0, lambda: app.log(
                    f"  Vertex AI 클라이언트 생성 실패: {err}"))
                has_sa = bool(os.environ.get("GOOGLE_APPLICATION_CREDENTIALS"))
                if has_sa:
                    app.after(0, lambda: app.log(
                        "    → gcp-key.json 경로 확인. 서비스 계정에 "
                        "aiplatform.user 권한이 있는지 확인하세요."))
                else:
                    app.after(0, lambda: app.log(
                        "    → gcp-key.json을 프로젝트 루트에 배치하거나 "
                        "gcloud auth application-default login 실행"))
                if app.job_logger and db and job_id:
                    app.job_logger.fail_job(db, job_id, "Vertex 인증 실패")
                return

            model_priority = [
                ("gemini-3.1-flash-image-preview", location),
                ("gemini-3-pro-image-preview",      location),
                ("gemini-2.5-flash-image",          "us-central1"),
                ("gemini-2.5-flash-image-preview",  "us-central1"),
                ("gemini-2.0-flash-preview-image-generation", "us-central1"),
            ]
            current_loc = location
            for model_name, model_loc in model_priority:
                if model_loc != current_loc:
                    try:
                        client = genai.Client(
                            vertexai=True,
                            project=credential,
                            location=model_loc,
                        )
                        current_loc = model_loc
                    except Exception:
                        continue
                try:
                    response = client.models.generate_content(
                        model=model_name,
                        contents=[
                            gtypes.Part.from_bytes(data=img_bytes, mime_type="image/png"),
                            render_prompt
                        ],
                        config=gtypes.GenerateContentConfig(
                            response_modalities=["IMAGE"],
                        )
                    )
                    for part in response.candidates[0].content.parts:
                        if hasattr(part, 'inline_data') and part.inline_data and \
                           part.inline_data.mime_type.startswith("image/"):
                            rendered = Image.open(io.BytesIO(part.inline_data.data))
                            break
                    if rendered:
                        _m, _l = model_name, current_loc
                        app.after(0, lambda _m=_m, _l=_l: app.log(
                            f"    [Vertex] 모델 {_m} @ {_l} 성공"))
                        break
                except Exception as exc:
                    _m, _e = model_name, str(exc)[:120]
                    app.after(0, lambda _m=_m, _e=_e: app.log(f"    [Vertex] {_m}: {_e}"))

        elif sdk_version == "new":
            client = genai.Client(api_key=credential)
            for model_name in ["gemini-2.5-flash-image",
                                 "gemini-2.0-flash-preview-image-generation"]:
                try:
                    response = client.models.generate_content(
                        model=model_name,
                        contents=[
                            gtypes.Part.from_bytes(data=img_bytes, mime_type="image/png"),
                            render_prompt
                        ],
                        config=gtypes.GenerateContentConfig(
                            response_modalities=["IMAGE", "TEXT"],
                        )
                    )
                    for part in response.candidates[0].content.parts:
                        if hasattr(part, 'inline_data') and part.inline_data and \
                           part.inline_data.mime_type.startswith("image/"):
                            rendered = Image.open(io.BytesIO(part.inline_data.data))
                            break
                    if rendered:
                        _m = model_name
                        app.after(0, lambda _m=_m: app.log(f"    모델 {_m} 성공"))
                        break
                except Exception as exc:
                    _m, _e = model_name, str(exc)[:80]
                    app.after(0, lambda _m=_m, _e=_e: app.log(f"    {_m}: {_e}"))

        else:
            genai_legacy.configure(api_key=credential)
            pil_img = Image.open(io.BytesIO(img_bytes))
            for model_name in ["gemini-2.5-flash-image",
                                 "gemini-2.0-flash-preview-image-generation"]:
                try:
                    model = genai_legacy.GenerativeModel(model_name)
                    response = model.generate_content(
                        [pil_img, render_prompt]
                    )
                    if hasattr(response, 'candidates') and response.candidates:
                        for part in response.candidates[0].content.parts:
                            if (hasattr(part, 'inline_data') and part.inline_data
                                    and part.inline_data.mime_type.startswith("image/")):
                                rendered = Image.open(io.BytesIO(part.inline_data.data))
                                break
                    if rendered:
                        _m = model_name
                        app.after(0, lambda _m=_m: app.log(f"    모델 {_m} 성공"))
                        break
                except Exception as exc:
                    _m, _e = model_name, str(exc)[:80]
                    app.after(0, lambda _m=_m, _e=_e: app.log(f"    {_m}: {_e}"))

        if not rendered:
            if app.job_logger and db and job_id:
                app.job_logger.fail_job(db, job_id, "Gemini 이미지 생성 실패")
            app.after(0, lambda: app.log("  Gemini 이미지 생성 실패. API Key와 모델을 확인하세요."))
            app.after(0, lambda: app.set_status("이미지 생성 실패", "#E74C3C"))
            return

        # 출력 화질 후처리 — Step 4에서 고른 HD/FHD/UHD 로 리사이즈
        tgt = getattr(app, "target_resolution", None)
        if tgt and tgt[0] > 0 and tgt[1] > 0 and rendered.size != tuple(tgt):
            src_size = rendered.size
            app.after(0, lambda s=src_size, t=tgt: app.log(
                f"  화질 리사이즈: {s[0]}x{s[1]} → {t[0]}x{t[1]}"))
            rendered = rendered.resize(tuple(tgt), Image.LANCZOS)

        output_path = "rendered_birdseye.png"
        rendered.save(output_path)
        latency_ms = (_time.time() - t_start) * 1000

        quality_score = 0.0
        if app.quality_val:
            try:
                vr = app.quality_val.validate(Path(output_path))
                quality_score = vr.score
                app.after(0, lambda: app.log(f"  품질검증: {vr.summary}"))
            except Exception as e:
                app.after(0, lambda e=e: app.log(f"  품질검증 오류: {e}"))

        if app.job_logger and db and job_id:
            with contextlib.suppress(Exception):
                app.job_logger.complete_job(db, job_id, output_path, quality_score, latency_ms)

        app.after(0, lambda: app.log(
            f"  Gemini 렌더링 완료! → {output_path} ({rendered.size}) "
            f"[{latency_ms:.0f}ms, 품질={quality_score:.2f}]"))
        app.after(0, lambda: app.set_status("AI 렌더링 완료", "#2ECC71"))
        app.after(0, lambda: app._show_rendered_result(output_path))

    except Exception as e:
        if app.job_logger and db and job_id:
            with contextlib.suppress(Exception):
                app.job_logger.fail_job(db, job_id, str(e))
        err_msg = str(e)[:300]
        app.after(0, lambda: app.log(f"  Gemini 오류: {err_msg}"))
        app.after(0, lambda: app.set_status("렌더링 실패", "#E74C3C"))
        app.after(0, lambda: messagebox.showerror("Gemini 오류", f"API 호출 오류:\n{err_msg}"))
    finally:
        if db:
            with contextlib.suppress(Exception):
                db.close()