import os
import io
import json
import hashlib
import gradio as gr

from pipelines.openai_ingest import (
    extract_text_with_openai,
    structure_with_openai,
    summarize_with_openai,
)
from pipelines.parsing import normalize_resume
from pipelines.merge import merge_normalized_records
from pipelines.skills import extract_skills
from pipelines.anonymize import anonymize_text, render_anonymized_pdf
from pipelines.scoring import compute_quality_score
from pipelines.storage import persist_to_hf
from pipelines.utils import detect_filetype, load_doc_text

APP_TITLE = "候補者インテーク & レジュメ標準化（OpenAI版）"


def process_resumes(files, candidate_id: str, additional_notes: str = ""):
    """
    files: gr.Files(type="filepath") から渡る「ファイルパスのリスト」
    返り値は Gradio の API スキーマ生成エラーを避けるため、**全て文字列 or ファイル**に統一する。
    """
    if not files:
        raise gr.Error("少なくとも1ファイルをアップロードしてください。")

    partial_records = []
    raw_texts = []

    # Files(type="filepath") → files はパスのリスト
    for path in files:
        try:
            with open(path, "rb") as rf:
                raw_bytes = rf.read()
        except Exception as e:
            raise gr.Error(f"ファイル読み込みに失敗しました: {path}: {e}")

        fname = os.path.basename(path)
        filetype = detect_filetype(fname, raw_bytes)

        # 1) テキスト抽出：画像/PDFはOpenAI Vision OCR、docx/txtは生文面＋OpenAI整形
        if filetype in {"pdf", "image"}:
            text = extract_text_with_openai(raw_bytes, filename=fname, filetype=filetype)
        else:
            base_text = load_doc_text(filetype, raw_bytes)
            # 生テキストをそのままOpenAIへ渡し、軽く整形した全文を返す
            text = extract_text_with_openai(base_text.encode("utf-8"), filename=fname, filetype="txt")

        raw_texts.append({"filename": fname, "text": text})

        # 2) OpenAIでセクション構造化 → ルールベース正規化
        structured = structure_with_openai(text)
        normalized = normalize_resume({
            "work_experience": structured.get("work_experience_raw", ""),
            "education": structured.get("education_raw", ""),
            "certifications": structured.get("certifications_raw", ""),
            "skills": ", ".join(structured.get("skills_list", [])),
        })
        partial_records.append({
            "source": fname,
            "text": text,
            "structured": structured,
            "normalized": normalized,
        })

    # 3) 統合（複数ファイル→1候補者）
    merged = merge_normalized_records([r["normalized"] for r in partial_records])

    # 4) スキル抽出（辞書/正規表現）
    merged_text = "\n\n".join([r["text"] for r in partial_records])
    skills = extract_skills(merged_text, {
        "work_experience": merged.get("raw_sections", {}).get("work_experience", ""),
        "education": merged.get("raw_sections", {}).get("education", ""),
        "certifications": merged.get("raw_sections", {}).get("certifications", ""),
        "skills": ", ".join(merged.get("skills", [])),
    })

    # 5) 匿名化
    anonymized_text, anon_map = anonymize_text(merged_text)
    anon_pdf_bytes = render_anonymized_pdf(anonymized_text)

    # 6) 品質スコア
    score = compute_quality_score(merged_text, merged)

    # 7) 要約（300/100/1文）
    summaries = summarize_with_openai(merged_text)

    # 8) 構造化出力（文字列化して返す）
    result_json = {
        "candidate_id": candidate_id or hashlib.sha256(merged_text.encode("utf-8")).hexdigest()[:16],
        "files": [os.path.basename(p) for p in files],
        "merged": merged,
        "skills": skills,
        "quality_score": score,
        "summaries": summaries,
        "anonymization_map": anon_map,
        "notes": additional_notes,
    }

    # 9) HF Datasets 保存
    dataset_repo = os.environ.get("DATASET_REPO")
    commit_info = None
    if dataset_repo:
        file_hash = result_json["candidate_id"]
        commit_info = persist_to_hf(
            dataset_repo=dataset_repo,
            record=result_json,
            anon_pdf_bytes=anon_pdf_bytes,
            parquet_path=f"candidates/{file_hash}.parquet",
            json_path=f"candidates/{file_hash}.json",
            pdf_path=f"candidates/{file_hash}.anon.pdf",
        )

    # gr.File 用の (filename, bytes) タプル
    anon_pdf = (result_json["candidate_id"] + ".anon.pdf", anon_pdf_bytes)

    # 返り値は**すべて文字列**（と1つのファイル）に統一
    return (
        json.dumps(result_json, ensure_ascii=False, indent=2),
        json.dumps(skills, ensure_ascii=False, indent=2),
        json.dumps(score, ensure_ascii=False, indent=2),
        summaries.get("300chars", ""),
        summaries.get("100chars", ""),
        summaries.get("onesent", ""),
        anon_pdf,
        json.dumps(commit_info or {"status": "skipped (DATASET_REPO not set)"}, ensure_ascii=False, indent=2),
    )


with gr.Blocks(title=APP_TITLE) as demo:
    gr.Markdown(f"# {APP_TITLE}\n複数ファイルを統合→OpenAIで読み込み/構造化/要約→匿名化→Datasets保存")

    with gr.Row():
        in_files = gr.Files(
            label="レジュメ類 (PDF/画像/Word/テキスト) 複数可",
            file_count="multiple",
            file_types=[".pdf", ".png", ".jpg", ".jpeg", ".tiff", ".bmp", ".docx", ".txt"],
            type="filepath",  # ← 重要: 'file' は無効。'filepath' か 'binary'
        )
        candidate_id = gr.Textbox(label="候補者ID（任意。未入力なら自動生成）")
    notes = gr.Textbox(label="補足メモ（任意）", lines=3)

    run_btn = gr.Button("実行")

    with gr.Tab("構造化JSON"):
        out_json = gr.Code(label="統合出力 (JSON)")

    with gr.Tab("抽出スキル"):
        # gr.JSON は API スキーマ生成で例外が出るケースがあるため回避し、文字列(JSON)を表示
        out_skills = gr.Code(label="スキル一覧(JSON表示)")

    with gr.Tab("品質スコア"):
        out_score = gr.Code(label="品質評価(JSON)")

    with gr.Tab("要約 (300/100/1文)"):
        out_sum_300 = gr.Textbox(label="300字要約")
        out_sum_100 = gr.Textbox(label="100字要約")
        out_sum_1 = gr.Textbox(label="1文要約")

    with gr.Tab("匿名PDF"):
        out_pdf = gr.File(label="匿名PDFダウンロード")

    with gr.Tab("Datasets 保存ログ"):
        out_commit = gr.Code(label="コミット情報")

    run_btn.click(
        process_resumes,
        inputs=[in_files, candidate_id, notes],
        outputs=[out_json, out_skills, out_score, out_sum_300, out_sum_100, out_sum_1, out_pdf, out_commit],
    )


if __name__ == "__main__":
    # Spaces 等で localhost 非公開環境を考慮
    demo.launch(
        server_name="0.0.0.0",
        server_port=int(os.environ.get("PORT", "7860")),
        share=True,
        show_error=True,
        analytics_enabled=False,
    )