app/feedback.py

import csv
import os
import tempfile
from datetime import datetime, timezone
from pathlib import Path

from .google_oauth import GoogleAuthRequiredError, append_feedback_to_sheet, load_credentials
from .memory import salvar_memoria_negativa


DEFAULT_LOGS_DIR = Path(os.getenv("LOGS_DIR", "/data/logs"))
FALLBACK_LOGS_DIR = Path(tempfile.gettempdir()) / "tcc2_agent" / "logs"
FEEDBACK_HEADERS = [
    "timestamp",
    "search_id",
    "query",
    "rank",
    "product_id",
    "product_name",
    "categoria_inferida",
    "categoria_produto",
    "rating",
    "is_helpful",
    "feedback",
    "motivo",
    "score_final",
    "score_semantico",
    "bonus_lexical",
    "penalidade_feedback",
    "user_message",
    "note",
]


def _resolve_logs_dir():
    preferred_dir = os.environ.get("LOGS_DIR", "").strip()
    candidates = [Path(preferred_dir)] if preferred_dir else []
    candidates.extend([DEFAULT_LOGS_DIR, FALLBACK_LOGS_DIR])

    for directory in candidates:
        try:
            directory.mkdir(parents=True, exist_ok=True)
            return directory
        except OSError:
            continue

    raise OSError(
        "Nao foi possivel criar um diretorio para armazenar os logs de feedback. "
        "Defina LOGS_DIR para um caminho gravavel."
    )


def caminho_feedback():
    return str(_resolve_logs_dir() / "feedback.csv")


def inicializar_arquivo_feedback():
    feedback_file = caminho_feedback()

    if not os.path.exists(feedback_file):
        with open(feedback_file, mode="w", newline="", encoding="utf-8") as f:
            writer = csv.writer(f)
            writer.writerow(FEEDBACK_HEADERS)


def google_sheets_habilitado():
    try:
        return load_credentials() is not None and bool(os.getenv("GOOGLE_SPREADSHEET_ID", "").strip())
    except Exception:
        return False


def _append_feedback_csv(row):
    inicializar_arquivo_feedback()
    feedback_file = caminho_feedback()

    with open(feedback_file, mode="a", newline="", encoding="utf-8") as f:
        writer = csv.writer(f)
        writer.writerow([row.get(header, "") for header in FEEDBACK_HEADERS])


def _derive_feedback_fields(rating=None, is_helpful=None, feedback=None):
    derived_feedback = feedback
    derived_is_helpful = is_helpful

    if derived_feedback is None:
        if rating is not None and rating >= 4:
            derived_feedback = "positivo"
        elif rating is not None and rating <= 2:
            derived_feedback = "negativo"
        elif rating == 3:
            derived_feedback = "neutro"
        else:
            derived_feedback = ""

    if derived_is_helpful is None:
        if rating is not None and rating >= 4:
            derived_is_helpful = True
        elif rating is not None and rating <= 2:
            derived_is_helpful = False
        elif rating == 3:
            derived_is_helpful = ""
        else:
            derived_is_helpful = ""

    return derived_feedback, derived_is_helpful


def _build_feedback_payload(
    search_id,
    query,
    rank,
    product_id,
    product_name,
    categoria_inferida=None,
    categoria_produto=None,
    rating=None,
    is_helpful=None,
    feedback=None,
    motivo=None,
    score_final=None,
    score_semantico=None,
    bonus_lexical=None,
    penalidade_feedback=None,
    user_message=None,
    note=None,
):
    derived_feedback, derived_is_helpful = _derive_feedback_fields(
        rating=rating,
        is_helpful=is_helpful,
        feedback=feedback,
    )

    return {
        "timestamp": datetime.now(timezone.utc).isoformat(),
        "search_id": search_id or "",
        "query": query,
        "rank": rank if rank is not None else "",
        "product_id": product_id,
        "product_name": product_name,
        "categoria_inferida": categoria_inferida or "",
        "categoria_produto": categoria_produto or "",
        "rating": rating if rating is not None else "",
        "is_helpful": derived_is_helpful,
        "feedback": derived_feedback,
        "motivo": motivo or "",
        "score_final": score_final if score_final is not None else "",
        "score_semantico": score_semantico if score_semantico is not None else "",
        "bonus_lexical": bonus_lexical if bonus_lexical is not None else "",
        "penalidade_feedback": penalidade_feedback if penalidade_feedback is not None else "",
        "user_message": user_message or "",  # ajuste aqui
        "note": note or "",
    }


def salvar_feedback(
    search_id,
    query,
    rank,
    product_id,
    product_name,
    categoria_inferida=None,
    categoria_produto=None,
    rating=None,
    is_helpful=None,
    feedback=None,
    motivo=None,
    score_final=None,
    score_semantico=None,
    bonus_lexical=None,
    penalidade_feedback=None,
    user_message=None,
    note=None,
):
    row = _build_feedback_payload(
        search_id=search_id,
        query=query,
        rank=rank,
        product_id=product_id,
        product_name=product_name,
        categoria_inferida=categoria_inferida,
        categoria_produto=categoria_produto,
        rating=rating,
        is_helpful=is_helpful,
        feedback=feedback,
        motivo=motivo,
        score_final=score_final,
        score_semantico=score_semantico,
        bonus_lexical=bonus_lexical,
        penalidade_feedback=penalidade_feedback,
        user_message=user_message,
        note=note,
    )

    _append_feedback_csv(row)
    saved_google_sheets = False
    warning = None

    try:
        append_feedback_to_sheet(row)
        saved_google_sheets = True
    except GoogleAuthRequiredError:
        warning = "Feedback salvo localmente, mas nao enviado ao Google Sheets. Acesse /auth/google para autorizar."
    except Exception as exc:
        warning = (
            "Feedback salvo localmente, mas nao enviado ao Google Sheets. "
            f"Erro de sincronizacao: {exc}"
        )

    # 🔥 ajuste aqui (tratamento de string → int)
    try:
        rating_num = int(row.get("rating"))
    except (TypeError, ValueError):
        rating_num = None

    is_helpful_value = row.get("is_helpful")

    if (
        (isinstance(rating_num, int) and rating_num <= 2)
        or is_helpful_value is False
    ):
        salvar_memoria_negativa(
            query=query,
            product_id=product_id,
            product_name=product_name,
            rating=rating_num,
            motivo=row.get("motivo") or row.get("feedback") or "feedback_negativo",
            search_id=row.get("search_id"),
            rank=row.get("rank"),
        )

    response = {
        "ok": True,
        "saved_local": True,
        "saved_google_sheets": saved_google_sheets,
    }
    if warning:
        response["warning"] = warning
    return response