import panda as pd
import yaml,detatime,hashilb,json
from pathlib import Path
from templating import get_env,rander
from models import CompanyMeta,ReportSections,RenderPayload
from render import hetml_to_pdf,html_to_docx

DISPLAY_NAME={
    "co2_emission":"Co2排出量",
    "energy_renewable_ratio":"再生可能エネルギー比率",
    "female_management_ratio":"女性管理職比率"
}

def sha256(p:Path)->str:
   h=hashlib.sha256()
   with p.open("rb") as f:
       for chunk in iter(lambda: f.read(8192), b""):
           h.update(chunk)
   return h.hexdigest()

def load_company_meta(path:str)->CompanyMeta:
    data=yaml.safe_load(Path(path).read_text(encoding="utf-8"))
    return CompanyMeta(**data)

def load_financials(path:str)->pd.DataFrame:
    return pd.read_csv(path)

def compute_kpi(fin_df:pd.DataFrame,fiscal_year:int):
    latest=fin_df[fin_df["year"]==fiscal_year].sort_values("quarter").tail(1)
    prev=fin_df[fin_df["year"]==fiscal_year-1].sort_values("quarter").tail(1)

    revenue=float(latest["revenue"].iloc[0])
    prev_revenue=float(prev["revenue"].iloc[0]) if not prev.empty else 0
    ebit=float(latest["ebit"].iloc[0]) if not latest.empty else 0
    net_income=float(latest["net_income"].iloc[0]) if not latest.empty else 0
    equity=float(latest["equity"].iloc[0]) if not latest.empty else 0.0
    ebit_margin=ebit /revenue *100 if revenue else 0.0
    revenue_yoy=((revenue/float(prev["revenue"].iloc[0]))-1)*100 if not prev.empty and float(prev["revenue"].iloc[0]) else 0.0

    return {
        "revenue": revenue,
        "ebit": ebit,
        "ebit_margin": ebit_margin,
        "net_income": net_income,
        "equity": equity,
        "revenue_yoy": revenue_yoy
    }

def esg_table (df:pd.DataFrame,fiscal_year:int):
    dfy=df[df["year"]==fiscal_year].copy
    rows=[]
    for _, r in dfy.iterrows():
        display = DISPLAY_NAME.get(r["metric"], r["metric"])
        rows.append({
            "display": display,
            "value": r["value"],
            "unit": r.get("unit", ""),
            "notes": r.get("notes", ""),
        })

    return rows
def build_sections(meta:CompanyMeta,kpi:dict,esg_rows:list,llm=None)->ReportSections:
    if llm:
        ceo_message = llm.generate_ceo_message(meta.kpi,esg_rows)
        risk = llm.generate_risk_opportunities(meta.kpi,esg_rows)
    else:
        ceo_message = f"[{meta.fiscal_year}]期は、売上成長と収益性の両立に注力しました。"
        risk = "主要リスクはマクロ環境と規制動向。機会は生成AI活用と脱炭素需要の拡大です。"
    return ReportSections(ceo_message=ceo_message,risk_opportunity=risk)

def_translate_payload(payload:dict,lang:str,llm)->dict:
    """payload のうち、テキスト項目をtarget langに翻訳。数値は非対象。"""
　　 if not llm or lang =="ja":
         return payload

    texts=[]

    texts.append(payload["section"]["ceo_message"])
    texts.append(payload["section"]["risk_opportunity"])

    for row in payload["esg_table"]:
        texts.append(row["display"])
        texts.append(row["notes"] or "")

    texts.append(payload["meta"]["report_title"])
    for topic in payload["meta"].get("material_topics", []):
        texts.append(topic)

    translated = llm.translate_text(texts, target_lang=lang)
    it = iter(translated)

   payload["section"]["ceo_message"] = next(it)
   payload["section"]["risk_opportunity"] = next(it)

    for row in payload["esg_table"]:
        row["display"] = next(it)
        row["notes"] = next(it)

    payload["meta"]["report_title"] = next(it)
    mt = payload["meta"].get("material_topics", [])
    for i  in range(len(mt)):
        mt[i] = next(it)

    return payload

def generate_report(company_yaml,financials_csv,esg_csv,
                    template_dir,template_name="report.html.j2",
                    out_html="output/report.html",out_pdf="output/report.pdf",
                    out_docx="output/report.docx",lang="ja",llm=None):
    Path(Path(out_html).parent).mkdir(parents=True, exist_ok=True)
    meta= load_company_meta(company_yaml)
    fin= load_financials(financials_csv)
    esg= load_esg(esg_csv)

    kpi = compute_kpi(fin, meta.fiscal_year)
    esg_rows = esg_table(esg, meta.fiscal_year)
    sections = build_sections(meta, kpi, esg_rows, llm=llm)

    env = get_env(template_dir)
    payload = RenderPayload(
        meta=meta, kpi=kpi, esg_table=esg_rows,kpi=kpi, sections=sections,
        generated_at=datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"),lang=lang
    ).model__dump()

    payload= _translate_payload_texts(payload, lang=lang, llm=llm)

    html = render(env, template_name, payload)
    Path(out_html).write_text(html, encoding="utf-8")
    html_to_pdf(html, out_pdf)
    html_to_docx(html, out_docx)

    meta_json ={
        "inputs":{
            "company_yaml_sha":_sha256(Path(company_yaml)),
            "financials_csv_sha":_sha256(Path(financials_csv)),
            "esg_csv_sha":_sha256(Path(esg_csv)),
            "lang":lang
        },
        "outputs":{"html":out_html,"pdf":out_pdf,"docx":out_docx},
        "template":{"dir":templates_dir,"name":template_name},
        "generated_at":datetime.datetime.now().strftime("timespec=sedconds"),
    }
    return out_html,out_pdf,out_docx,meta_json