backend/endpoints/projects.py

# ruff: noqa: E402
import uuid
from typing import List, Optional, Any
from datetime import datetime, timezone
from fastapi import APIRouter, Depends, HTTPException, UploadFile, File
from fastapi.encoders import jsonable_encoder
from pydantic import BaseModel, Field, ConfigDict

from core.subscription.db import SessionLocal
from core.subscription.middleware import verify_token
from core.projects.models import (
    Project,
    ProjectSection,
    ProjectSectionVersion,
    ProjectQuestion,
    ProjectSectionTemplate,
    ProjectChatMessage,
    ProjectExportVersion,
)
from core.subscription.models import User
from fastapi import BackgroundTasks
from agents.helpers import generate_section, review_section, project_qa_agent
from agents.auditor import audit_final_document
from core.llm_router import get_llm
from core.subscription.tracker import increment_wizard_iteration
from langchain_core.prompts import PromptTemplate
import json
import os
from langchain_core.tracers.langchain import LangChainTracer
import logging

logger = logging.getLogger(__name__)

# Włącz tracing LangSmith
os.environ["LANGCHAIN_TRACING_V2"] = "false"
os.environ["LANGCHAIN_PROJECT"] = "grantforge-production"

# Opcjonalnie – jeśli chcesz zobaczyć dokładne nazwy runów
tracer = LangChainTracer(project_name="grantforge-production")

router = APIRouter(prefix="/api/projects", tags=["projects"])

UNIVERSAL_FALLBACK_MAP = {
    # Legacy & common sections
    "project_summary": "Streszczenie Projektu",
    "company_potential": "Opis przedsiębiorstwa i potencjał",
    "innovation_description": "Opis innowacji / B+R",
    "market_analysis": "Analiza rynku i konkurencji",
    "research_agenda": "Agenda badawcza / cele",
    "trl_levels": "Poziom gotowości technologii (TRL)",
    "budget_and_costs": "Budżet i kwalifikowalność kosztów",
    "work_schedule": "Harmonogram rzeczowo-finansowy",
    "project_team": "Zespół projektowy",
    "risk_management": "Zarządzanie ryzykiem",
    "social_impact_dnsh": "Wpływ społeczny i środowiskowy (DNSH)",
    "intellectual_property": "Prawa własności intelektualnej",
    "success_metrics": "Wskaźniki sukcesu i ewaluacja",
    "final_document": "Dokument końcowy",
    "company_overview": "Przegląd firmy",
    "company": "Informacje o firmie",
    "dnsh": "Zasada DNSH",
    "risk": "Analiza Ryzyka",
    "schedule": "Harmonogram Projektu",
    "kpi": "Wskaźniki KPI",
    "budget_details": "Szczegóły Budżetu",

    # SMART sections
    "applicant": "Wnioskodawca i powiązania",
    "team": "Zespół zarządzający i projektowy",
    "market": "Analiza zapotrzebowania i rynku",
    "innovation": "Innowacyjność (TRL) i znaczenie projektu",
    "module_br": "Moduł B+R: Plan prac",
    "module_implementation": "Moduł Wdrożenie Innowacji",
    "module_infrastructure": "Moduł Infrastruktura B+R",
    "module_digitalization": "Moduł Cyfryzacja",
    "module_green": "Moduł Zazielenienie przedsiębiorstw",
    "module_internationalization": "Moduł Internacjonalizacja",
    "module_competence": "Moduł Kompetencje",
    "sustainable": "Zrównoważony rozwój i zasada DNSH",
    "budget": "Budżet",
    "kpi_risk": "Wskaźniki KPI i analiza ryzyka",
    "attachments": "Załączniki i Oświadczenia",
    # ARIMR
    "description": "Opis inwestycji",
    "animals": "Dobrostan zwierząt / modernizacja",
    "profitability": "Analiza opłacalności",
    "environment": "Wpływ na środowisko",
    "technical_attachments": "Załączniki techniczne",
    # ZUS_BHP
    "risk_assessment": "Ocena ryzyka zawodowego",
    "improvement_plan": "Plan poprawy warunków pracy",
    "scope": "Zakres inwestycji BHP",
    "results": "Oczekiwane efekty",
}


# Schemas
class ProjectCreate(BaseModel):
    title: str = Field(..., title="Tytuł wniosku")
    program_type: str = Field(
        ..., title="Identyfikator modułu/rodzaju programu (np. SMART)"
    )
    description: Optional[str] = None
    program_name: Optional[str] = None
    estimated_value: Optional[float] = None
    external_context: Optional[dict] = None


class ProjectUpdate(BaseModel):
    title: Optional[str] = None
    description: Optional[str] = None
    status: Optional[str] = None
    program_name: Optional[str] = None
    estimated_value: Optional[float] = None
    last_generated_at: Optional[datetime] = None
    external_context: Optional[dict] = None


class ProjectSectionResponse(BaseModel):
    id: str
    project_id: str
    order: int
    section_type: str
    title: Optional[str] = None
    content: Optional[str]
    is_approved: bool
    generated_by_ai: bool
    last_reviewed_at: Optional[datetime]

    model_config = ConfigDict(from_attributes=True)


class SectionGenerateRequest(BaseModel):
    section_type: str
    prompt_context: Optional[str] = None


class SectionReviewRequest(BaseModel):
    section_type: str
    content: str


class ExportRequest(BaseModel):
    format: str = Field(..., title="Format eksportu", description="pdf lub docx")
    template: str = Field(
        "standard", title="Szablon", description="standard, official, modern"
    )
    version_id: Optional[str] = None


class SectionVersionResponse(BaseModel):
    id: str
    section_id: str
    old_content: str
    timestamp: datetime

    model_config = ConfigDict(from_attributes=True)


class ProjectAskRequest(BaseModel):
    question: str


class ProjectAskResponse(BaseModel):
    id: str
    question: str
    answer: str
    sources: List[str]
    confidence: float
    recommendation: str
    created_at: Optional[datetime] = None


class FinalDocumentCompileRequest(BaseModel):
    approved_only: bool = False


class FinalDocumentCompileResponse(BaseModel):
    final_markdown: str
    generated_at: datetime
    sections_used: int
    approved_only: bool


class AuditIssueResponse(BaseModel):
    category: str
    severity: str
    message: str
    rule_citation: Optional[str] = ""
    recommendation: Optional[str] = ""


class GlobalAuditResponse(BaseModel):
    status: Optional[str] = "completed"
    is_approved: Optional[bool] = False
    export_status: Optional[str] = ""
    overall_score: Optional[int] = 0
    issues: Optional[List[AuditIssueResponse]] = []


class ProjectResponse(BaseModel):
    id: str
    clerk_user_id: str
    title: str
    description: Optional[str]
    status: str
    estimated_value: Optional[float]
    program_name: Optional[str]
    last_generated_at: Optional[datetime]
    final_document_markdown: Optional[str] = None
    final_document_generated_at: Optional[datetime] = None
    final_document_audit_result: Optional[dict] = None
    external_context: Optional[dict] = None
    created_at: datetime
    updated_at: datetime
    sections: List[ProjectSectionResponse] = []

    model_config = ConfigDict(from_attributes=True)


def get_db():
    db = SessionLocal()
    try:
        yield db
    finally:
        db.close()


# Endpoints
@router.get("", response_model=List[ProjectResponse])
async def list_projects(token_data: dict = Depends(verify_token), db=Depends(get_db)):
    clerk_id = token_data.get("sub")
    if not clerk_id:
        raise HTTPException(status_code=401, detail="Brak clerk_id w tokenie")

    projects = db.query(Project).filter(Project.clerk_user_id == clerk_id).all()
    return projects


@router.delete("/{project_id}", response_model=dict)
async def delete_project(
    project_id: str, token_data: dict = Depends(verify_token), db=Depends(get_db)
):
    clerk_id = token_data.get("sub")
    if not clerk_id:
        raise HTTPException(status_code=401, detail="Brak clerk_id w tokenie")

    project = (
        db.query(Project)
        .filter(Project.id == project_id, Project.clerk_user_id == clerk_id)
        .first()
    )
    if not project:
        raise HTTPException(
            status_code=404, detail="Projekt nie istnieje lub brak uprawnień"
        )

    try:
        # Oczyszczanie wektorów z Pinecone przed skasowaniem dokumentów
        docs = project.documents
        if docs:
            try:
                from rag_pipeline.vector_store import get_vector_store

                for doc in docs:
                    if doc.rag_namespace:
                        store = get_vector_store(namespace=doc.rag_namespace)
                        if store:
                            store._index.delete(
                                delete_all=True, namespace=doc.rag_namespace
                            )
                            logger.info(
                                f"[Delete] Usunięto namespace Pinecone: {doc.rag_namespace}"
                            )
            except Exception as e_pinecone:
                logger.error(f"[Delete] Błąd usuwania namespace RAG: {e_pinecone}")

        # Kaskadowe usuwanie encji przez ORM (relacje mają cascade="all, delete-orphan")
        db.delete(project)
        db.commit()
    except Exception as e:
        db.rollback()
        logger.error(f"Error during cascading delete: {e}")
        raise HTTPException(
            status_code=500, detail=f"Nie można usunąć projektu: {str(e)}"
        )

    return {"status": "ok", "message": "Projekt usunięty z powodzeniem."}


@router.post("", response_model=ProjectResponse)
async def create_project(
    data: ProjectCreate, token_data: dict = Depends(verify_token), db=Depends(get_db)
):
    clerk_id = token_data.get("sub")
    if not clerk_id:
        raise HTTPException(status_code=401, detail="Brak clerk_id w tokenie")

    # Upewnij się że użytkownik istnieje w bazie (inaczej poleci FK violation Constraint)
    user = db.query(User).filter(User.clerk_id == clerk_id).first()
    if not user:
        user = User(clerk_id=clerk_id)
        db.add(user)
        db.commit()
        db.refresh(user)

    # Pełne pobranie z GUS na starcie projektu, by LLM miał od początku pełen obraz firmy
    enriched_context = data.external_context or {}
    if "company_data" in enriched_context and enriched_context["company_data"].get("nip"):
        try:
            from tools.company_search import fetch_regon_data
            nip = enriched_context["company_data"]["nip"]
            full_data = fetch_regon_data(nip)
            if full_data:
                enriched_context["company_data"] = full_data
                logger.info(f"[Projects] Pomyślnie rozszerzono dane GUS/KRS na starcie dla NIP: {nip}")
        except Exception as gus_e:
            logger.warning(f"[Projects] Błąd pobierania pełnych danych GUS dla nowo tworzonego projektu: {gus_e}")

    new_project = Project(
        id=str(uuid.uuid4()),
        clerk_user_id=clerk_id,
        title=data.title,
        description=data.description,
        program_type=data.program_type,
        program_name=data.program_name,
        estimated_value=data.estimated_value,
        external_context=enriched_context,
        status="draft",
    )

    db.add(new_project)
    db.commit()  # bezpiecznie zapisujemy projekt najpierw
    db.refresh(new_project)

    # Auto-generowanie sekcji na bazie przypisanego programu z użyciem bazy
    templates = (
        db.query(ProjectSectionTemplate)
        .filter(ProjectSectionTemplate.program_type == data.program_type)
        .order_by(ProjectSectionTemplate.order.asc())
        .all()
    )

    # Przechywyć fallback jeśli nie ma w bazie rekordu dla wybranego programu
    if not templates and data.program_type != "SMART":
        templates = (
            db.query(ProjectSectionTemplate)
            .filter(ProjectSectionTemplate.program_type == "SMART")
            .order_by(ProjectSectionTemplate.order.asc())
            .all()
        )

    if templates:
        for tmpl in templates:
            sec = ProjectSection(
                project_id=new_project.id,
                section_type=tmpl.section_type,
                order=tmpl.order,
                content="",
                is_approved=False,
                generated_by_ai=False,
            )
            db.add(sec)
    else:
        # Ultimate fallback (safety net) - direct from memory without modifying template tables
        from scripts.seed_section_templates import TEMPLATES

        templates_dicts = [
            t for t in TEMPLATES if t.get("program_type") == data.program_type
        ]
        if not templates_dicts:
            templates_dicts = [t for t in TEMPLATES if t.get("program_type") == "SMART"]

        for tmpl in templates_dicts:
            db.add(
                ProjectSection(
                    project_id=new_project.id,
                    section_type=tmpl.get("section_type"),
                    order=tmpl.get("order"),
                    content="",
                    is_approved=False,
                    generated_by_ai=False,
                )
            )

    db.commit()
    db.refresh(new_project)

    # Przygotuj poprawną mapę by ProjectResponse objęło nowo stworzone sekcje z odpowiednim tytułem
    template_map = {}
    if templates:
        template_map = {t.section_type: t.title for t in templates}
    else:
        from scripts.seed_section_templates import TEMPLATES

        template_map = {t.get("section_type"): t.get("title") for t in TEMPLATES}

    project_dict = new_project.__dict__.copy()
    project_dict["sections"] = []

    # Skoro dopisywaliśmy obiekty do sesji, project.sections może nie być odświeżone z bazy, więc wrzucamy lokalne
    for s in (
        db.query(ProjectSection)
        .filter(ProjectSection.project_id == new_project.id)
        .order_by(ProjectSection.order.asc())
        .all()
    ):
        sec_dict = s.__dict__.copy()
        sec_dict["title"] = template_map.get(
            s.section_type, s.section_type.replace("_", " ").title()
        )
        project_dict["sections"].append(sec_dict)

    # ============================================================
    # GSD — Główny Tryb Działania Grantforge (od maja 2026)
    # ============================================================
    try:
        from gsd.integration import start_gsd_for_project

        gsd_result = start_gsd_for_project(
            project_id=new_project.id,
            user_id=clerk_id,
            tenant_id=clerk_id,
            profile=data.external_context or {},
            program_type=data.program_type,
        )
        project_dict["gsd"] = gsd_result
        logger.info(f"[GSD] Projekt {new_project.id} uruchomiony w głównym trybie GSD")
    except Exception as e:
        logger.warning(f"[GSD] Błąd startu GSD dla {new_project.id}: {e}")
        project_dict["gsd"] = {"gsd_mode": False, "error": "GSD temporarily unavailable"}

    return project_dict


@router.post("/welcome-seed", response_model=Optional[ProjectResponse])
async def create_welcome_seed(
    token_data: dict = Depends(verify_token), db=Depends(get_db)
):
    """
    Tworzy przykładowy projekt onboardingowy ('Wzór: Innowacje SMART') jeśli użytkownik
    nie ma jeszcze utworzonego żadnego projektu.
    """
    clerk_id = token_data.get("sub")
    if not clerk_id:
        raise HTTPException(status_code=401, detail="Brak clerk_id w tokenie")

    has_projects = db.query(Project).filter(Project.clerk_user_id == clerk_id).first()
    if has_projects:
        return None  # Seed tylko gdy konto jest w całości puste (Empty State)

    # Upewnij się że użytkownik istnieje w bazie
    user = db.query(User).filter(User.clerk_id == clerk_id).first()
    if not user:
        user = User(clerk_id=clerk_id)
        db.add(user)
        db.commit()
        db.refresh(user)

    new_project = Project(
        id=str(uuid.uuid4()),
        clerk_user_id=clerk_id,
        title="Wzór: Innowacja Cyfrowa SMART",
        description="Projekt demonstracyjny wdrożenia infrastruktury chmurowej AI w przedsiębiorstwie produkcyjnym.",
        program_type="SMART",
        program_name="Ścieżka SMART (FENG)",
        estimated_value=1250000.0,
        status="draft",
    )

    db.add(new_project)
    db.flush()

    templates = (
        db.query(ProjectSectionTemplate)
        .filter(ProjectSectionTemplate.program_type == "SMART")
        .order_by(ProjectSectionTemplate.order)
        .all()
    )
    sections = []

    if templates:
        for t in templates:
            content = ""
            is_appr = False
            is_ai = False
            if t.section_type == "project_summary":
                content = "Głównym celem projektu jest opracowanie i rynkowe wdrożenie innowacyjnej dedykowanej platformy opartej o uczenie maszynowe, wspierającej procesy kontroli jakości..."
                is_appr = True
                is_ai = True
            elif t.section_type in ["applicant", "company_potential"]:
                content = "Firma posiada 10-letnie doświadczenie z infrastrukturą AWS oraz zespół R&D składający się z 5 inżynierów MLOps."

            sections.append(
                ProjectSection(
                    project_id=new_project.id,
                    section_type=t.section_type,
                    order=t.order,
                    content=content,
                    is_approved=is_appr,
                    generated_by_ai=is_ai,
                )
            )
    else:
        from scripts.seed_section_templates import TEMPLATES

        # Use project.program_type instead of hardcoding SMART
        target_program = (
            new_project.program_type if new_project.program_type else "SMART"
        )
        for t in TEMPLATES:
            if t.get("program_type") == target_program:
                content = ""
                is_appr = False
                is_ai = False
                # Optionally set some default content for known template types
                if t.get("section_type") == "project_summary":
                    content = "Głównym celem projektu..."
                    is_appr = True
                    is_ai = True
                sections.append(
                    ProjectSection(
                        project_id=new_project.id,
                        section_type=t.get("section_type"),
                        order=t.get("order"),
                        content=content,
                        is_approved=is_appr,
                        generated_by_ai=is_ai,
                    )
                )

    db.add_all(sections)
    db.commit()
    db.refresh(new_project)

    template_map = {}
    if templates:
        template_map = {t.section_type: t.title for t in templates}
    else:
        from scripts.seed_section_templates import TEMPLATES

        template_map = {
            t.get("section_type"): t.get("title")
            for t in TEMPLATES
            if t.get("program_type") == target_program
        }

    project_dict = new_project.__dict__.copy()
    project_dict["sections"] = []
    for s in (
        db.query(ProjectSection)
        .filter(ProjectSection.project_id == new_project.id)
        .order_by(ProjectSection.order.asc())
        .all()
    ):
        sec_dict = s.__dict__.copy()
        sec_dict["title"] = template_map.get(
            s.section_type, s.section_type.replace("_", " ").title()
        )
        project_dict["sections"].append(sec_dict)

    return project_dict


class LookupCompanyResponse(BaseModel):
    nip: str
    name: str
    status: str


class MatchProgramRequest(BaseModel):
    nip: Optional[str] = None
    description: str


@router.get("/lookup-company", response_model=LookupCompanyResponse)
async def lookup_company(nip: str, token_data: dict = Depends(verify_token)):
    if len(nip) != 10 or not nip.isdigit():
        raise HTTPException(
            status_code=400, detail="Nieprawidłowy NIP. Wymagane 10 cyfr."
        )

    try:
        from tools.company_search import fetch_regon_data

        result = fetch_regon_data(nip)

        if (
            result
            and result.get("name")
            and result.get("name") != "Firma (Błąd pobierania)"
        ):
            return LookupCompanyResponse(
                nip=nip,
                name=result["name"],
                status=f"Zidentyfikowano • {result.get('voivodeship', 'Brak danych')} Województwo",
            )
        else:
            raise HTTPException(
                status_code=404,
                detail="Nie znaleziono podmiotu w rejestrze GUS/MF dla podanego NIP.",
            )

    except HTTPException:
        raise
    except Exception as e:
        # Prawdziwy fallback: jeśli GUS leży (timeout, błąd integracji), wysyłamy 503,
        # by frontend mógł przełączyć na ręczne wypełnianie formularza.
        raise HTTPException(
            status_code=503,
            detail=f"Serwery rejestrowe są niedostępne. Wpisz dane ręcznie. (Szczegóły: {str(e)})",
        )


@router.post("/match-program")
async def match_program(
    data: MatchProgramRequest, token_data: dict = Depends(verify_token)
):
    from pydantic import BaseModel
    import logging

    logger = logging.getLogger(__name__)

    class ProgramExplanation(BaseModel):
        reason: str
        criteria: List[str]
        risks: str

    class ProgramMatch(BaseModel):
        id: int
        name: str
        type: str
        match: int
        chance: str
        amount: str
        shortDesc: str
        fullDesc: str
        url: Optional[str] = None
        explanation: ProgramExplanation

    class MatchProgramOutput(BaseModel):
        programs: List[ProgramMatch]
        clarifying_questions: List[str] = Field(default_factory=list)

    from core.search.grant_search_service import grant_search_service
    import asyncio
    from tools.company_search import fetch_regon_data

    # Pobieranie danych GUS
    company_context = ""
    if data.nip:
        try:
            c_data = fetch_regon_data(data.nip)
            if c_data and c_data.get("name") != "Firma (Błąd pobierania)":
                pkd_list = ", ".join(c_data.get("pkd", []))
                company_context = f"\n\n--- DANE Z BAZY GUS ---\nFirma: {c_data.get('name')}\nWojewództwo: {c_data.get('voivodeship')}\nKody PKD (TWARDE FILTROWANIE!): {pkd_list}\n"
        except Exception as e:
            logger.warning(f"Błąd pobierania danych GUS w match_program: {e}")

    # Pobieranie wszystkich naborów z nowej Kaskady Wyszukiwania
    all_grants = await grant_search_service.get_all_grants()
    
    nabory_list: list[dict[str, Any]] = all_grants

    nabory_context = ""
    for index, n in enumerate(nabory_list[:30]):  # Limit context size to 30 grants
        nabory_context += f"--- PROGRAM {index+1} ---\nNazwa: {n.get('name')}\nURL: {n.get('url', 'Brak info')}\nTermin: {n.get('deadline', '?')}\nZasady/Opis: {n.get('description', 'Brak info')}\n\n"

    if not nabory_context.strip():
        nabory_context = "Brak aktywnych naborów w bazie. Zaproponuj standardowe dotacje historyczne."

    template = """
    Jesteś głównym ekspertem ds. funduszy europejskich i państwowych. 
    Otrzymujesz zapytanie klienta oraz listę WSZYSTKICH obecnie trwających naborów.
    
    TWARDA REGUŁA: Jeśli w danych GUS podano kody PKD (np. 62.01.Z), musisz BEZWZGLĘDNIE sprawdzić czy te kody kwalifikują się do programu (np. programy dla rolników odrzucaj dla branży IT). Jeśli firma ma wielobranżowe kody (ponad 3 różne branże), zadaj pytanie doprecyzowujące o dominujący profil projektu.
    
    Opis inwestycji klienta: {description}
    {company_context}
    
    BAZA AKTYWNYCH NABORÓW (tylko z tego dobieraj):
    {nabory_context}
    
    Wybierz 3 do 6 najbardziej pasujących programów. Jeśli brakuje kluczowych danych (wielkość firmy, status MŚP, budżet, lokalizacja poza woj. {company_context}), wygeneruj od 1 do 3 pytań doprecyzowujących (w 'clarifying_questions').
    
    Zwróć wynik jako czysty JSON:
    {{
      "programs": [
        {{
          "id": 1,
          "name": "nazwa",
          "type": "SMART",
          "match": 85,
          "chance": "Wysoka",
          "amount": "do 70%",
          "shortDesc": "krótki opis",
          "fullDesc": "pełny",
          "url": "TUTAJ PRZEPISZ DOKŁADNIE URL PODANY W BAZIE",
          "explanation": {{
             "reason": "dlaczego?",
             "criteria": ["kryterium 1"],
             "risks": "ryzyka"
          }}
        }}
      ],
      "clarifying_questions": ["Pytanie 1?"]
    }}
    Upewnij się, że "match" to liczba z przedziału 0-100 bez znaku %.
    Odpowiedz tylko i wyłącznie kodem JSON, bez bloków ```json.
    """
    try:
        structured_llm = get_llm(
            task_type="creative", structured_output_schema=MatchProgramOutput
        )
        prompt = PromptTemplate.from_template(template)
        chain = prompt | structured_llm
        
        res = chain.invoke(
            {"description": data.description, "nabory_context": nabory_context}
        )

        programs_out: list[dict[str, Any]] = []
        questions_out: list[str] = []

        if hasattr(res, "programs") and res.programs:
            programs_out = []
            for p in res.programs:
                if hasattr(p, "model_dump"):
                    programs_out.append(p.model_dump())
                elif hasattr(p, "dict") and callable(getattr(p, "dict")):
                    try:
                        programs_out.append(p.dict())
                    except Exception:
                        programs_out.append(
                            dict(p)
                            if isinstance(p, dict)
                            else getattr(p, "__dict__", {})
                        )
                elif isinstance(p, dict):
                    programs_out.append(p)
                else:
                    try:
                        programs_out.append(dict(p))
                    except Exception:
                        programs_out.append(getattr(p, "__dict__", {}))
            questions_out = getattr(res, "clarifying_questions", [])
        elif isinstance(res, dict) and "programs" in res:
            if isinstance(res["programs"], list) and len(res["programs"]) > 0:
                programs_out = []
                for p in res["programs"]:
                    if hasattr(p, "model_dump"):
                        programs_out.append(p.model_dump())
                    elif hasattr(p, "dict") and callable(getattr(p, "dict")):
                        try:
                            programs_out.append(p.dict())
                        except Exception:
                            programs_out.append(
                                dict(p)
                                if isinstance(p, dict)
                                else getattr(p, "__dict__", {})
                            )
                    elif isinstance(p, dict):
                        programs_out.append(p)
                    else:
                        try:
                            programs_out.append(dict(p))
                        except Exception:
                            programs_out.append(getattr(p, "__dict__", {}))
                questions_out = res.get("clarifying_questions", [])
    except Exception as e:
        logger.error(f"LLM Error in match_program: {e}")
        # Domyślny fallback na wypadek błędu serwisu LLM - zapewnia status 200 dla frontu
        programs_out = [
            {
                "id": 1,
                "name": "Ścieżka SMART (FENG)",
                "type": "SMART",
                "match": 85,
                "chance": "Wysoka",
                "amount": "do 70%",
                "shortDesc": "Fundusz na innowacje i B+R.",
                "fullDesc": "Przeciążenie serwera analizy AI. Podajemy uniwersalny program z historii: Ścieżka SMART na projekty modułowe.",
                "url": "https://www.parp.gov.pl/component/grants/grants/sciezka-smart",
                "explanation": {
                    "reason": "Program wspiera szerokie innowacje w MŚP. Idealny punkt startowy.",
                    "criteria": ["Innowacja produktowa/procesowa", "Prace badawczo-rozwojowe"],
                    "risks": "Wymaga wkładu własnego i solidnego komponentu badawczego.",
                },
            },
            {
                "id": 2,
                "name": "Inwestycje ARiMR",
                "type": "ARIMR",
                "match": 60,
                "chance": "Średnia",
                "amount": "do 60%",
                "shortDesc": "Fundusz obszarów wiejskich",
                "fullDesc": "Spróbuj ponownie - to jest program zastępczy podany przez system.",
                "url": "https://www.gov.pl/web/arimr",
                "explanation": {
                    "reason": "Z powodu awarii serwera podano wariant uniwersalny rolniczy.",
                    "criteria": ["Działalność na obszarach M-W"],
                    "risks": "Brak bezpośrednich powiązań.",
                },
            }
        ]
        questions_out = ["Z powodu przeciążenia serwera, AI nie mogło przeanalizować Twojego profilu. Spróbuj ponownie za chwilę."]

    return {"programs": programs_out, "clarifying_questions": questions_out}


@router.get("/{project_id}", response_model=ProjectResponse)
async def get_project(
    project_id: str, token_data: dict = Depends(verify_token), db=Depends(get_db)
):
    clerk_id = token_data.get("sub")

    project = (
        db.query(Project)
        .filter(Project.id == project_id, Project.clerk_user_id == clerk_id)
        .first()
    )
    if not project:
        raise HTTPException(
            status_code=404, detail="Nie znaleziono projektu z autoryzacją"
        )

    templates = (
        db.query(ProjectSectionTemplate)
        .filter(ProjectSectionTemplate.program_type == project.program_type)
        .all()
    )
    template_map = {t.section_type: t.title for t in templates}

    project_dict = project.__dict__.copy()
    project_dict["sections"] = []

    for s in (
        db.query(ProjectSection)
        .filter(ProjectSection.project_id == project_id)
        .order_by(ProjectSection.order.asc())
        .all()
    ):
        sec_dict = s.__dict__.copy()
        sec_dict["title"] = template_map.get(s.section_type) or UNIVERSAL_FALLBACK_MAP.get(
            s.section_type, s.section_type.replace("_", " ").title()
        )
        project_dict["sections"].append(sec_dict)

    return project_dict


@router.put("/{project_id}", response_model=ProjectResponse)
async def update_project(
    project_id: str,
    data: ProjectUpdate,
    token_data: dict = Depends(verify_token),
    db=Depends(get_db),
):
    clerk_id = token_data.get("sub")
    project = (
        db.query(Project)
        .filter(Project.id == project_id, Project.clerk_user_id == clerk_id)
        .first()
    )
    if not project:
        raise HTTPException(
            status_code=404, detail="Nie znaleziono projektu z autoryzacją"
        )

    if data.title is not None:
        project.title = data.title
    if data.description is not None:
        project.description = data.description
    if data.status is not None:
        project.status = data.status
    if data.program_name is not None:
        project.program_name = data.program_name
    if data.estimated_value is not None:
        project.estimated_value = data.estimated_value
    if data.last_generated_at is not None:
        project.last_generated_at = data.last_generated_at
    if data.external_context is not None:
        project.external_context = data.external_context

    db.commit()
    db.refresh(project)
    return project


@router.post("/{project_id}/compile-final", response_model=FinalDocumentCompileResponse)
async def compile_final_document(
    project_id: str,
    data: FinalDocumentCompileRequest,
    token_data: dict = Depends(verify_token),
    db=Depends(get_db),
):
    clerk_id = token_data.get("sub")
    project = (
        db.query(Project)
        .filter(Project.id == project_id, Project.clerk_user_id == clerk_id)
        .first()
    )
    if not project:
        raise HTTPException(status_code=404, detail="Brak dostępu do projektu")

    # Pobieranie sekcji posortowanych
    query = db.query(ProjectSection).filter(ProjectSection.project_id == project_id)
    if data.approved_only:
        query = query.filter(ProjectSection.is_approved)

    sections = query.order_by(ProjectSection.order.asc()).all()

    if not sections:
        raise HTTPException(
            status_code=400, detail="Brak sekcji spełniających kryteria kompilacji."
        )

    # Pobieranie tytułów z szablonu dla danego programu
    from scripts.seed_section_templates import TEMPLATES

    template_titles = {}
    for tmpl in TEMPLATES:
        if tmpl["program_type"] == project.program_type:
            template_titles[tmpl["section_type"]] = tmpl["title"]

    raw_document = []
    toc = ["## Spis Treści\n"]

    # Informacja o firmie
    company_name = ""
    if project.external_context and "company_data" in project.external_context:
        company_name = project.external_context["company_data"].get("name", "")

    if company_name:
        raw_document.append(f"# Wniosek o dofinansowanie - {company_name}")
    else:
        raw_document.append("# Wniosek o dofinansowanie")

    for i, sec in enumerate(sections, 1):
        if sec.content and sec.content.strip():
            # Pobieramy tytul: najpierw z obiektu sekcji, jesli nie, z szablonu, na koncu fallback
            title = (
                sec.title
                if hasattr(sec, "title") and sec.title
                else template_titles.get(sec.section_type, sec.section_type.upper())
            )
            toc.append(f"{i}. {title}")
            raw_document.append(f"## {i}. {title}\n\n{sec.content.strip()}")

    if len(raw_document) <= 1:
        raise HTTPException(
            status_code=400, detail="Wszystkie pobrane sekcje są puste."
        )

    # Złożenie ostatecznego dokumentu: Tytuł, Spis Treści, Treść
    final_markdown = (
        raw_document[0]
        + "\n\n"
        + "\n".join(toc)
        + "\n\n"
        + "\n\n".join(raw_document[1:])
    )

    FORBIDDEN_PHRASES = [
        "nie posiadam informacji",
        "nie mogę wygenerować",
        "as an ai",
        "jako model językowy",
    ]

    actual_length = len(final_markdown)
    has_table = "|" in final_markdown

    found_forbidden_phrases = [
        phrase
        for phrase in FORBIDDEN_PHRASES
        if phrase.lower() in final_markdown.lower()
    ]

    # Złagodzona weryfikacja - pozwalamy na eksport draftów (brak tabeli, mniejsza długość), 
    # ale blokujemy w przypadku ewidentnych halucynacji/odmów asystenta.
    if found_forbidden_phrases or actual_length < 500:
        if found_forbidden_phrases:
            reason_msg = f"Dokument zawiera niedozwolone frazy (odmowa AI): {', '.join(found_forbidden_phrases)}"
        else:
            reason_msg = f"Dokument jest podejrzanie krótki (tylko {actual_length} znaków)."

        raise HTTPException(
            status_code=400,
            detail={
                "error": "DOCUMENT_VALIDATION_FAILED",
                "message": "Wygenerowany dokument nie spełnia minimalnych wymagań jakościowych.",
                "details": {
                    "min_length_required": 500,
                    "actual_length": actual_length,
                    "has_table": has_table,
                    "forbidden_phrases_found": found_forbidden_phrases,
                    "reason": reason_msg,
                },
                "action": "System automatycznie ponowi generację z dodatkowym kontekstem. Jeśli problem będzie się powtarzał, skontaktuj się z administratorem.",
                "retry_available": True,
            },
        )

    project.final_document_markdown = final_markdown
    project.final_document_generated_at = datetime.now(timezone.utc)
    db.commit()

    return FinalDocumentCompileResponse(
        final_markdown=final_markdown,
        generated_at=project.final_document_generated_at,
        sections_used=len(sections),
        approved_only=data.approved_only,
    )


def background_audit(
    project_id: str, program_name: str, document_to_audit: str, is_external_audit: bool
):
    db = SessionLocal()
    try:
        project = db.query(Project).filter(Project.id == project_id).first()
        if not project:
            return

        from agents.auditor import audit_final_document

        result = audit_final_document(
            project_id,
            program_name,
            document_to_audit,
            is_external_audit=is_external_audit,
        )

        # Konwersja na standard
        if hasattr(result, "model_dump"):
            dict_res = result.model_dump()
        elif hasattr(result, "dict"):
            dict_res = result.dict()
        elif isinstance(result, dict):
            dict_res = result
        else:
            dict_res = dict(result)

        dict_res = jsonable_encoder(dict_res)
        dict_res["status"] = "completed"
        project.final_document_audit_result = dict_res
        db.commit()
    except Exception as e:
        logger.error(f"Global Audit Background padł: {e}")
        project.final_document_audit_result = {"status": "error", "message": str(e)}
        db.commit()
    finally:
        db.close()


@router.post("/{project_id}/global-audit", response_model=GlobalAuditResponse)
async def perform_global_audit(
    project_id: str,
    background_tasks: BackgroundTasks,
    token_data: dict = Depends(verify_token),
    db=Depends(get_db),
):
    clerk_id = token_data.get("sub")
    project = (
        db.query(Project)
        .filter(Project.id == project_id, Project.clerk_user_id == clerk_id)
        .first()
    )
    if not project:
        raise HTTPException(status_code=404, detail="Brak dostępu do projektu")

    if project.foreign_grant_extract_text:
        # Retroaktywny audyt dokumentu zewnętrznego
        document_to_audit = project.foreign_grant_extract_text
        is_external_audit = True
    else:
        if not project.final_document_markdown:
            raise HTTPException(
                status_code=400,
                detail="Brak skompilowanego dokumentu do audytu. Najpierw wygeneruj certyfikat kompilacji.",
            )
        document_to_audit = project.final_document_markdown
        is_external_audit = False

    project.final_document_audit_result = {"status": "pending"}
    db.commit()

    background_tasks.add_task(
        background_audit,
        project_id,
        str(project.program_name or project.program_type),
        document_to_audit,
        is_external_audit,
    )

    return {"status": "pending"}


@router.delete("/{project_id}/global-audit", status_code=204)
async def clear_global_audit(
    project_id: str, token_data: dict = Depends(verify_token), db=Depends(get_db)
):
    clerk_id = token_data.get("sub")
    project = (
        db.query(Project)
        .filter(Project.id == project_id, Project.clerk_user_id == clerk_id)
        .first()
    )
    if not project:
        raise HTTPException(status_code=404, detail="Brak dostępu do projektu")

    project.final_document_audit_result = None
    db.commit()


def background_holistic_review(
    project_id: str, program_name: str, document_to_audit: str
):
    db = SessionLocal()
    try:
        project = db.query(Project).filter(Project.id == project_id).first()
        if not project:
            return

        from agents.holistic_critic import holistic_critic_evaluate

        result = holistic_critic_evaluate(project_id, document_to_audit, program_name)

        dict_res = (
            result.model_dump() if hasattr(result, "model_dump") else result.dict()
        )
        dict_res["status"] = "completed"

        # Save to external context
        ext_ctx = dict(project.external_context) if project.external_context else {}
        ext_ctx["holistic_review"] = dict_res

        from sqlalchemy.orm.attributes import flag_modified

        project.external_context = ext_ctx
        flag_modified(project, "external_context")

        db.commit()
    except Exception as e:
        logger.error(f"Holistic Review Background padł: {e}")
        project = db.query(Project).filter(Project.id == project_id).first()
        if project:
            ext_ctx = dict(project.external_context) if project.external_context else {}
            ext_ctx["holistic_review"] = {"status": "error", "message": str(e)}
            from sqlalchemy.orm.attributes import flag_modified

            project.external_context = ext_ctx
            flag_modified(project, "external_context")
            db.commit()
    finally:
        db.close()


@router.post("/{project_id}/holistic-review")
async def perform_holistic_review(
    project_id: str,
    background_tasks: BackgroundTasks,
    token_data: dict = Depends(verify_token),
    db=Depends(get_db),
):
    clerk_id = token_data.get("sub")
    project = (
        db.query(Project)
        .filter(Project.id == project_id, Project.clerk_user_id == clerk_id)
        .first()
    )
    if not project:
        raise HTTPException(status_code=404, detail="Brak dostępu do projektu")

    if project.foreign_grant_extract_text:
        document_to_audit = project.foreign_grant_extract_text
    else:
        if not project.final_document_markdown:
            raise HTTPException(
                status_code=400,
                detail="Brak skompilowanego dokumentu do audytu. Najpierw wygeneruj wniosek.",
            )
        document_to_audit = project.final_document_markdown

    ext_ctx = dict(project.external_context) if project.external_context else {}
    ext_ctx["holistic_review"] = {"status": "pending"}
    from sqlalchemy.orm.attributes import flag_modified

    project.external_context = ext_ctx
    flag_modified(project, "external_context")
    db.commit()

    background_tasks.add_task(
        background_holistic_review,
        project_id,
        str(project.program_name or project.program_type),
        document_to_audit,
    )

    return {"status": "pending"}


@router.get("/{project_id}/holistic-review")
async def get_holistic_review(
    project_id: str, token_data: dict = Depends(verify_token), db=Depends(get_db)
):
    clerk_id = token_data.get("sub")
    project = (
        db.query(Project)
        .filter(Project.id == project_id, Project.clerk_user_id == clerk_id)
        .first()
    )
    if not project:
        raise HTTPException(status_code=404, detail="Brak dostępu do projektu")

    ext_ctx = project.external_context or {}
    review = ext_ctx.get("holistic_review")
    if not review:
        raise HTTPException(status_code=404, detail="Brak raportu spójności")

    return {
        "status": review.get("status", "completed"),
        "report": review
    }


@router.post("/{project_id}/generate-section", response_model=ProjectSectionResponse)
def generate_project_section(
    project_id: str,
    data: SectionGenerateRequest,
    token_data: dict = Depends(verify_token),
    db=Depends(get_db),
):
    clerk_id = token_data.get("sub")
    project = (
        db.query(Project)
        .filter(Project.id == project_id, Project.clerk_user_id == clerk_id)
        .first()
    )
    if not project:
        raise HTTPException(status_code=404, detail="Brak dostępu do projektu")

    # Prepare external context including project description
    ext_ctx = dict(project.external_context) if project.external_context else {}
    if project.description:
        ext_ctx["project_description"] = project.description

    # Pobierz aktualną sekcję (żeby przekazać jej zawartość LLM-owi do ewentualnych poprawek)
    section = (
        db.query(ProjectSection)
        .filter(
            ProjectSection.project_id == project_id,
            ProjectSection.section_type == data.section_type,
        )
        .first()
    )
    if section and section.content:
        ext_ctx["current_section_content"] = section.content

    generated_markdown = generate_section(
        project_id=project_id,
        section_type=data.section_type,
        context=data.prompt_context
        or "Generuj treść ogólną do urzędowego wniosku unijnego.",
        external_context=ext_ctx,
        program_name=project.program_name,
        user_id=clerk_id,
    )

    from core.subscription.models import User
    from datetime import datetime

    user_record = db.query(User).filter(User.clerk_id == clerk_id).first()
    disclaimer_enabled = getattr(user_record, "ai_disclaimer_enabled", True)

    if disclaimer_enabled:
        generated_markdown += "\n\n---\n*Treść wygenerowana przez AI na podstawie bazy wiedzy. Zalecana ostateczna weryfikacja przez doradcę/prawnika.*"

    project.last_generated_at = datetime.now(timezone.utc)
    project.updated_at = datetime.now(timezone.utc)

    if section:
        # Zapisz starą wersję
        version = ProjectSectionVersion(
            section_id=section.id,
            old_content=section.content,
            author="Asystent AI",
            summary="Wygenerowanie z AI na podstawie baz RAG",
        )
        db.add(version)
        section.content = generated_markdown
        section.generated_by_ai = True
        section.is_approved = False
    else:
        # Utwórz nową
        section = ProjectSection(
            project_id=project_id,
            section_type=data.section_type,
            content=generated_markdown,
            generated_by_ai=True,
        )
        db.add(section)

    # Rejestracja limitów
    increment_wizard_iteration(clerk_id)

    db.commit()
    db.refresh(section)
    return section


@router.post("/{project_id}/review-section")
async def review_project_section(
    project_id: str,
    data: SectionReviewRequest,
    token_data: dict = Depends(verify_token),
    db=Depends(get_db),
):
    clerk_id = token_data.get("sub")
    project = (
        db.query(Project)
        .filter(Project.id == project_id, Project.clerk_user_id == clerk_id)
        .first()
    )
    if not project:
        raise HTTPException(status_code=404, detail="Brak dostępu do projektu")

    critic_eval = review_section(project_id, data.section_type, data.content)

    # Save status if approved
    if critic_eval.is_approved:
        section = (
            db.query(ProjectSection)
            .filter(
                ProjectSection.project_id == project_id,
                ProjectSection.section_type == data.section_type,
            )
            .first()
        )
        if section:
            section.is_approved = True
            db.commit()

    return {
        "is_approved": critic_eval.is_approved,
        "feedback": critic_eval.feedback,
        "severity": critic_eval.severity,
    }


@router.get("/{project_id}/preview")
async def preview_project(
    project_id: str, token_data: dict = Depends(verify_token), db=Depends(get_db)
):
    clerk_id = token_data.get("sub")
    project = (
        db.query(Project)
        .filter(Project.id == project_id, Project.clerk_user_id == clerk_id)
        .first()
    )
    if not project:
        raise HTTPException(status_code=404, detail="Brak dostępu")

    sections = (
        db.query(ProjectSection)
        .filter(ProjectSection.project_id == project_id)
        .order_by(ProjectSection.order.asc())
        .all()
    )

    markdown_content = f"# WNIOSEK O DOFINANSOWANIE: {project.title}\n\n"
    old_markdown_content = f"# WNIOSEK O DOFINANSOWANIE: {project.title}\n\n"

    for s in sections:
        markdown_content += f"## {s.section_type.upper()}\n\n{s.content}\n\n"

        # Odszukaj ostatnią wersję historyczną
        last_version = (
            db.query(ProjectSectionVersion)
            .filter(ProjectSectionVersion.section_id == s.id)
            .order_by(ProjectSectionVersion.timestamp.desc())
            .first()
        )
        if last_version:
            old_markdown_content += (
                f"## {s.section_type.upper()}\n\n{last_version.old_content}\n\n"
            )
        else:
            old_markdown_content += f"## {s.section_type.upper()}\n\n{s.content}\n\n"

    return {"markdown": markdown_content, "old_markdown": old_markdown_content}


@router.get("/{project_id}/sections", response_model=List[ProjectSectionResponse])
async def get_project_sections(
    project_id: str, token_data: dict = Depends(verify_token), db=Depends(get_db)
):
    clerk_id = token_data.get("sub")
    project = (
        db.query(Project)
        .filter(Project.id == project_id, Project.clerk_user_id == clerk_id)
        .first()
    )
    if not project:
        raise HTTPException(status_code=404, detail="Brak dostępu")

    sections = (
        db.query(ProjectSection)
        .filter(ProjectSection.project_id == project_id)
        .order_by(ProjectSection.order.asc())
        .all()
    )
    templates = (
        db.query(ProjectSectionTemplate)
        .filter(ProjectSectionTemplate.program_type == project.program_type)
        .all()
    )
    if not templates:
        templates = (
            db.query(ProjectSectionTemplate)
            .filter(ProjectSectionTemplate.program_type == "SMART")
            .all()
        )

    template_map = (
        {t.section_type: t.title for t in templates} if templates else UNIVERSAL_FALLBACK_MAP
    )

    result = []
    for s in sections:
        sec_dict = s.__dict__.copy()
        sec_dict["title"] = template_map.get(
            s.section_type,
            UNIVERSAL_FALLBACK_MAP.get(s.section_type, s.section_type.replace("_", " ").title()),
        )
        result.append(sec_dict)

    return result


class SectionUpdateRequest(BaseModel):
    content: str


@router.put(
    "/{project_id}/sections/{section_id}", response_model=ProjectSectionResponse
)
async def update_project_section(
    project_id: str,
    section_id: str,
    data: SectionUpdateRequest,
    token_data: dict = Depends(verify_token),
    db=Depends(get_db),
):
    clerk_id = token_data.get("sub")
    # Zabezpieczenie
    section = (
        db.query(ProjectSection)
        .join(Project)
        .filter(
            ProjectSection.id == section_id,
            ProjectSection.project_id == project_id,
            Project.clerk_user_id == clerk_id,
        )
        .first()
    )

    if not section:
        raise HTTPException(status_code=404, detail="Nie znaleziono sekcji")

    # Zapisz loga historycznego
    version = ProjectSectionVersion(
        section_id=section.id,
        old_content=section.content,
        author="Edycja ręczna",
        summary="Ręczny auto-save użytkownika",
    )
    db.add(version)

    section.content = data.content
    section.is_approved = False  # Reset po edycji manualnej
    db.commit()
    db.refresh(section)
    return section


@router.post(
    "/{project_id}/sections/{section_id}/autofix", response_model=ProjectSectionResponse
)
async def autofix_project_section(
    project_id: str,
    section_id: str,
    token_data: dict = Depends(verify_token),
    db=Depends(get_db),
):
    clerk_id = token_data.get("sub")
    project = (
        db.query(Project)
        .filter(Project.id == project_id, Project.clerk_user_id == clerk_id)
        .first()
    )
    section = (
        db.query(ProjectSection)
        .filter(
            ProjectSection.id == section_id, ProjectSection.project_id == project_id
        )
        .first()
    )

    if not project or not section:
        raise HTTPException(
            status_code=404, detail="Nie znaleziono projektu lub sekcji"
        )

    # Audit wyniki są przechowywane w final_document_audit_result (z globalnego audytu)
    # Fallback na external_context["last_audit"] dla starszych audytów
    audit_data = project.final_document_audit_result
    if not audit_data:
        ext_context = project.external_context or {}
        audit_data = ext_context.get("last_audit")

    if not audit_data or "issues" not in audit_data or len(audit_data["issues"]) == 0:
        return section

    # Uzyskaj prawidłowy tytuł sekcji (Polish) aby LLM powiązał go z wynikami audytu
    from core.projects.models import ProjectSectionTemplate

    templates = (
        db.query(ProjectSectionTemplate)
        .filter(ProjectSectionTemplate.program_type == project.program_type)
        .all()
    )
    if not templates:
        templates = (
            db.query(ProjectSectionTemplate)
            .filter(ProjectSectionTemplate.program_type == "SMART")
            .all()
        )

    template_map = (
        {t.section_type: t.title for t in templates} if templates else UNIVERSAL_FALLBACK_MAP
    )
    section_t = template_map.get(
        section.section_type,
        UNIVERSAL_FALLBACK_MAP.get(
            section.section_type, section.section_type.replace("_", " ").title()
        ),
    )

    from core.utils import safe_extract_text, extract_markdown_and_sanitize
    import difflib
    import time

    import os

    def log_watchdog(msg: str):
        try:
            os.makedirs("logs", exist_ok=True)
            with open("logs/watchdog.log", "a", encoding="utf-8") as f:
                f.write(f"[{time.strftime('%Y-%m-%d %H:%M:%S')}] {msg}\n")
        except Exception:
            pass

    from core.telemetry import telemetry

    llm = get_llm(task_type="writing")
    issues_list = []

    ext_context = project.external_context or {}
    holistic_review = ext_context.get("holistic_review")
    if holistic_review and isinstance(holistic_review, dict):
        recs = holistic_review.get("key_recommendations", [])
        for r in recs:
            issues_list.append(f"- [KRYTYK GLOBALNY] Rekomendacja: {r}")

    direct_issues_count = 0
    for i in audit_data["issues"]:
        if isinstance(i, dict):
            affected = i.get("affected_section", "Ogólne")
            is_general = affected.lower() in [
                "ogólne",
                "całość",
                "ogolne",
                "calosc",
                "all",
                "general",
            ]
            similarity = difflib.SequenceMatcher(
                None, affected.lower(), section_t.lower()
            ).ratio()

            if is_general or similarity > 0.35:
                direct_issues_count += 1
                issues_list.append(
                    f"- [BEZPOŚREDNIO DOTYCZY TEJ SEKCJI] [{i.get('severity', 'UNKNOWN').upper()}] Kategoria: {i.get('category', '')}\n  Błąd: {i.get('message', '')}\n  Rekomendacja: {i.get('recommendation', '')}"
                )
            else:
                issues_list.append(
                    f"- [KONTEKST Z INNEJ SEKCJI: {affected}] Błąd: {i.get('message', '')}"
                )
        else:
            issues_list.append(f"- Błąd: {str(i)}")

    telemetry.log(
        "INFO",
        "Autofix",
        f"Rozpoczynam poprawę sekcji {section_t}",
        {"issues_count": len(issues_list), "direct_issues": direct_issues_count},
    )

    if not issues_list:
        return section

    issues_text = "\n".join(issues_list)

    # v2.3 - 2026-05-07 - Wymuszenie formatowania Markdown, łagodniejszy filtr błędów
    template = """Jesteś Głównym Inżynierem ds. Wniosków Unijnych (Grant Engineer) z wieloletnim doświadczeniem.
Poniżej znajduje się treść sekcji "{section_title}" wniosku o dofinansowanie dla projektu "{project_title}".

Wniosek przeszedł audyt i ZNALEZIONO W NIM KRYTYCZNE BŁĘDY. Poniżej znajduje się lista WSZYSTKICH zidentyfikowanych uwag z całego wniosku. 
Twoim absolutnym priorytetem są uwagi oznaczone jako [BEZPOŚREDNIO DOTYCZY TEJ SEKCJI] oraz [KRYTYK GLOBALNY]. 
{issues_text}

TWOJE ZADANIE:
Otrzymujesz ORYGINALNĄ TREŚĆ tej sekcji. Twoim celem jest JEJ CAŁKOWITA PRZEBUDOWA I ZNACZNE PODNIESIENIE JAKOŚCI. 
NIE OGRANICZAJ SIĘ DO KOSMETYKI! Jeśli uwaga wytyka brak konkretnych informacji, MUSISZ je profesjonalnie wymyślić (halucynuj w sposób ekspercki i logiczny dla projektu) i wpleść w tekst.

ZASADY:
1. ZWRÓĆ PEŁNĄ TREŚĆ SEKCJI PO POPRAWKACH. Nie ucinaj tekstu. Ma to być kompletny, wielowątkowy dokument odzwierciedlający całą sekcję.
2. ZASTOSUJ PROFESJONALNY STYL BIZNESOWY I NAUKOWY. Rozbuduj akapity, które są zbyt krótkie lub ogólnikowe.
3. BOGATE FORMATOWANIE MARKDOWN: używaj czytelnych nagłówków (###, ####), pogrubień dla kluczowych danych, list punktowanych i tabel tam, gdzie to możliwe.
4. BĄDŹ PROAKTYWNY I KREATYWNY: Jeśli audytor pisze "Brak opisu ryzyka", Ty nie piszesz "należy dodać opis", tylko FAKTYCZNIE TWORZYSZ dogłębną analizę ryzyka i wstawiasz ją do tekstu.
5. Jeśli żadna z uwag nie dotyczy tej sekcji, a obecny tekst jest perfekcyjny, zwróć "[NO_CHANGE]". Używaj tego niezwykle rzadko.
6. Zwracaj TYLKO kod Markdown. Zero wstępów.

ORYGINALNA TREŚĆ SEKCJI:
{content}
"""

    prompt = PromptTemplate.from_template(template)
    chain = prompt | llm

    from tenacity import retry, stop_after_attempt, wait_exponential, stop_after_delay

    watchdog_interventions_count = 0

    def after_retry_log(retry_state):
        nonlocal watchdog_interventions_count
        watchdog_interventions_count += 1
        exc = retry_state.outcome.exception()
        log_watchdog(
            f"[RETRY] Watchdog interweniował! Próba {retry_state.attempt_number}/5. Błąd: {str(exc)}"
        )

    @retry(
        stop=(stop_after_attempt(3) | stop_after_delay(60)),
        wait=wait_exponential(multiplier=1, min=2, max=10),
        after=after_retry_log,
        reraise=True,
    )
    def invoke_with_watchdog():
        response = chain.invoke(
            {
                "section_title": section_t,
                "project_title": project.title,
                "issues_text": issues_text,
                "content": section.content,
            }
        )

        raw_content = safe_extract_text(response.content)
        extracted = extract_markdown_and_sanitize(raw_content, min_length=50)

        # GSD Rule: Jeśli wynik Autofix ma mniej niż 40% zmienionego tekstu przy uwagach >= medium -> odrzucenie.
        # Agent Autofix nie może zwracać [NO_CHANGE], jeśli istnieją uwagi o severity medium lub high.
        has_critical = any(
            isinstance(i, dict) and i.get("severity", "low").lower() in ["medium", "high", "critical"]
            for i in audit_data.get("issues", [])
        )
        
        if has_critical:
            if extracted.strip() == "[NO_CHANGE]":
                raise ValueError("Watchdog GSD: Zwrócono [NO_CHANGE] przy obecności błędów medium/high. Odrzucam wynik.")
            
            if section.content and extracted.strip() != "[NO_CHANGE]":
                similarity = difflib.SequenceMatcher(None, section.content, extracted).ratio()
                if similarity > 0.60:
                    changed_pct = 100 - (similarity * 100)
                    raise ValueError(f"Watchdog GSD: Zbyt mała zmiana tekstu ({changed_pct:.1f}%). Wymagane min. 40% zmiany.")

        return extracted

    try:
        new_content = invoke_with_watchdog()

        if new_content != "[NO_CHANGE]":
            original_len = len(section.content) if section.content else 0
            min_expected_length = max(300, int(original_len * 0.70))

            if len(new_content) < min_expected_length:
                telemetry.log(
                    "WARN",
                    "Autofix",
                    f"LLM zwrócił odpowiedź krótszą niż oczekiwano ({len(new_content)} znaków vs {original_len} oryginału). Wymuszam ponowną próbę.",
                    {"length": len(new_content), "expected_min": min_expected_length},
                )
                # Ponawiamy zapytanie jednorazowo ze wzmocnionym wymogiem
                chain_fallback = (
                    PromptTemplate.from_template(
                        template
                        + "\n\nOSTRZEŻENIE: Twoja poprzednia odpowiedź była stanowczo za krótka w stosunku do oryginalnej sekcji! MUSISZ ZWRÓCIĆ CAŁĄ TREŚĆ SEKCJI, ZACHOWUJĄC JEJ SZCZEGÓŁOWOŚĆ. Nie ucinaj i nie streszczaj tekstu!"
                    )
                    | llm
                )
                res = chain_fallback.invoke(
                    {
                        "section_title": section_t,
                        "project_title": project.title,
                        "issues_text": issues_text,
                        "content": section.content,
                    }
                )
                new_content = extract_markdown_and_sanitize(
                    safe_extract_text(res.content), min_length=50
                )

        telemetry.log(
            "INFO",
            "Autofix",
            f"Sukces dla sekcji {section_id}. Zwrócono {len(new_content)} znaków.",
            {
                "is_no_change": new_content == "[NO_CHANGE]",
                "watchdog_interventions": watchdog_interventions_count,
            },
        )
        log_watchdog(
            f"[METRIC] Sukces dla sekcji {section_id}. Łączna liczba interwencji Watchdoga: {watchdog_interventions_count}"
        )
    except Exception as e:
        error_msg = str(e)
        if (
            "429" in error_msg
            or "ResourceExhausted" in error_msg
            or "Quota" in error_msg
        ):
            status_code = 429
            detail = "Przekroczono limit zapytań do AI (Rate Limit). Spróbuj ponownie później."
        else:
            status_code = 500
            detail = f"Sztuczna inteligencja nie mogła poprawnie wygenerować tej sekcji po 5 próbach. Błąd: {error_msg}"

        telemetry.log(
            "ERROR",
            "Autofix",
            f"Błąd w autofix: {error_msg}",
            {"section_id": section_id},
        )
        log_watchdog(
            f"[ABORT] Sekcja {section_id} - Przekroczono limit prób. Ostatni błąd: {error_msg}"
        )
        raise HTTPException(
            status_code=status_code,
            detail=detail,
        )

    if new_content and new_content != section.content and new_content != "[NO_CHANGE]":
        # Zapisz starą wersję
        version = ProjectSectionVersion(
            section_id=section.id,
            old_content=section.content,
            author="Autofix",
            summary="Automatyczna korekta audytu",
        )
        db.add(version)
        section.content = new_content
        section.is_approved = False
        db.commit()
        db.refresh(section)

    return section


@router.get("/{project_id}/sections/{section_id}/versions")
async def list_section_versions(
    project_id: str,
    section_id: str,
    token_data: dict = Depends(verify_token),
    db=Depends(get_db),
):
    clerk_id = token_data.get("sub")
    section = (
        db.query(ProjectSection)
        .join(Project)
        .filter(
            ProjectSection.id == section_id,
            ProjectSection.project_id == project_id,
            Project.clerk_user_id == clerk_id,
        )
        .first()
    )

    if not section:
        raise HTTPException(status_code=404, detail="Nie znaleziono sekcji")

    versions = (
        db.query(ProjectSectionVersion)
        .filter(ProjectSectionVersion.section_id == section_id)
        .order_by(ProjectSectionVersion.timestamp.desc())
        .limit(20)
        .all()
    )

    return [
        {
            "id": v.id,
            "old_content": v.old_content,
            "author": v.author,
            "summary": v.summary,
            "timestamp": v.timestamp.isoformat() + "Z",
        }
        for v in versions
    ]


@router.post(
    "/{project_id}/sections/{section_id}/versions/{version_id}/restore",
    response_model=ProjectSectionResponse,
)
async def restore_section_version(
    project_id: str,
    section_id: str,
    version_id: str,
    token_data: dict = Depends(verify_token),
    db=Depends(get_db),
):
    clerk_id = token_data.get("sub")
    section = (
        db.query(ProjectSection)
        .join(Project)
        .filter(
            ProjectSection.id == section_id,
            ProjectSection.project_id == project_id,
            Project.clerk_user_id == clerk_id,
        )
        .first()
    )

    if not section:
        raise HTTPException(status_code=404, detail="Nie znaleziono sekcji")

    version = (
        db.query(ProjectSectionVersion)
        .filter(
            ProjectSectionVersion.id == version_id,
            ProjectSectionVersion.section_id == section_id,
        )
        .first()
    )

    if not version:
        raise HTTPException(
            status_code=404, detail="Nie znaleziono wersji historycznej"
        )

    # Tworzymy nową wersję obecnego tekstu przed nadpisaniem
    new_version = ProjectSectionVersion(
        section_id=section.id,
        old_content=section.content,
        author="Ręczna",
        summary="Cofnięcie do historii ujęte jako checkpoint",
    )
    db.add(new_version)

    section.content = version.old_content
    section.is_approved = False

    db.commit()
    db.refresh(section)

    return section


@router.post("/{project_id}/ask", response_model=ProjectAskResponse)
async def ask_project_question(
    project_id: str,
    data: ProjectAskRequest,
    token_data: dict = Depends(verify_token),
    db=Depends(get_db),
):
    clerk_id = token_data.get("sub")
    project = (
        db.query(Project)
        .filter(Project.id == project_id, Project.clerk_user_id == clerk_id)
        .first()
    )
    if not project:
        raise HTTPException(status_code=404, detail="Brak dostępu")

    sections = (
        db.query(ProjectSection).filter(ProjectSection.project_id == project_id).all()
    )

    # Budowanie zagregowanego kontekstu z obecnego etapu pracy
    project_context_lines = [
        f"Informacje Ogólne. Tytuł: {project.title}, Wartość: {project.estimated_value or 'Brak'}\n"
    ]
    for s in sections:
        if s.content and len(s.content) > 10:
            project_context_lines.append(f"--- Sekcja: {s.section_type.upper()} ---")
            project_context_lines.append(s.content)

    project_context = "\n".join(project_context_lines)

    result_dict = project_qa_agent(
        project_id=project_id,
        question=data.question,
        program_name=project.program_name or "Ogólne dotacje unijne UE",
        context=project_context,
        external_context=project.external_context,
    )

    answer_val = result_dict.get("answer", "Brak odpowiedzi")
    sources_val = result_dict.get("sources", [])
    conf_val = float(result_dict.get("confidence", 0.0))
    rec_val = result_dict.get("recommendation", "")

    new_q = ProjectQuestion(
        project_id=project_id,
        question=data.question,
        answer=answer_val,
        sources=json.dumps(sources_val),
        confidence=conf_val,
        recommendation=rec_val,
    )
    db.add(new_q)
    db.commit()
    db.refresh(new_q)

    return ProjectAskResponse(
        id=new_q.id,
        question=new_q.question,
        answer=answer_val,
        sources=sources_val,
        confidence=conf_val,
        recommendation=rec_val,
        created_at=new_q.created_at,
    )


@router.get("/{project_id}/ask/history", response_model=List[ProjectAskResponse])
async def get_project_questions_history(
    project_id: str, token_data: dict = Depends(verify_token), db=Depends(get_db)
):
    clerk_id = token_data.get("sub")
    project = (
        db.query(Project)
        .filter(Project.id == project_id, Project.clerk_user_id == clerk_id)
        .first()
    )
    if not project:
        raise HTTPException(status_code=404, detail="Brak dostępu")

    history_items = (
        db.query(ProjectQuestion)
        .filter(ProjectQuestion.project_id == project_id)
        .order_by(ProjectQuestion.created_at.asc())
        .all()
    )

    response = []
    for h in history_items:
        sources_list = []
        if h.sources:
            try:
                sources_list = json.loads(h.sources)
            except Exception:
                pass
        response.append(
            ProjectAskResponse(
                id=h.id,
                question=h.question,
                answer=h.answer,
                sources=sources_list,
                confidence=h.confidence or 0.0,
                recommendation=h.recommendation or "",
                created_at=h.created_at,
            )
        )

    return response


@router.delete("/{project_id}/ask/history")
async def clear_project_questions_history(
    project_id: str, token_data: dict = Depends(verify_token), db=Depends(get_db)
):
    clerk_id = token_data.get("sub")
    project = (
        db.query(Project)
        .filter(Project.id == project_id, Project.clerk_user_id == clerk_id)
        .first()
    )
    if not project:
        raise HTTPException(status_code=404, detail="Brak dostępu")

    db.query(ProjectQuestion).filter(ProjectQuestion.project_id == project_id).delete()
    db.commit()
    return {
        "status": "success",
        "message": "Historia weryfikatora została wyczyszczona.",
    }


# --- ZASOBY I DOKUMENTY (EXTERNAL CONTEXT & RAG) ---
import io


class ProjectResourceResponse(BaseModel):
    id: str
    filename: str
    mime_type: str
    uploaded_at: datetime
    size_bytes: int
    extracted_text: Optional[str] = None


def parse_docx(file_bytes: bytes) -> str:
    import docx

    doc = docx.Document(io.BytesIO(file_bytes))
    return "\n".join([para.text for para in doc.paragraphs])


def parse_pdf(file_bytes: bytes) -> str:
    from pypdf import PdfReader

    reader = PdfReader(io.BytesIO(file_bytes))
    text = ""
    for page in reader.pages:
        extracted = page.extract_text()
        if extracted:
            text += extracted + "\n"
    return text


@router.post("/{project_id}/documents", response_model=ProjectResourceResponse)
async def upload_project_document(
    project_id: str,
    file: UploadFile = File(...),
    token_data: dict = Depends(verify_token),
    db=Depends(get_db),
):
    clerk_id = token_data.get("sub")
    project = (
        db.query(Project)
        .filter(Project.id == project_id, Project.clerk_user_id == clerk_id)
        .first()
    )
    if not project:
        raise HTTPException(
            status_code=404, detail="Nie znaleziono projektu z autoryzacją"
        )

    ext_context = project.external_context or {}

    # Przeniesienie starych documents do resources dla komptybilności
    if "documents" in ext_context and "resources" not in ext_context:
        ext_context["resources"] = ext_context.pop("documents")

    resources = ext_context.get("resources", [])

    from core.subscription.checker import SubscriptionChecker

    checker = SubscriptionChecker(clerk_id)
    user_tier = checker.get_tier().value.capitalize()
    limits = checker.get_current_limits()
    max_resources = limits.get("max_documents_per_project", 5)

    if len(resources) >= max_resources:
        if user_tier == "Free":
            raise HTTPException(
                status_code=400,
                detail=f"Osiągnąłeś limit {max_resources} dokumentów w planie Free.\nPrzejdź na plan Pro, aby dodawać więcej umów, ofert i materiałów do projektu.",
            )
        else:
            raise HTTPException(
                status_code=400,
                detail=f"Przekroczono limit wgranych zasobów ({max_resources}) dla Twojego planu ({user_tier}).",
            )

    MAX_FILE_SIZE = 10 * 1024 * 1024  # 10 MB
    file_bytes = await file.read()

    if len(file_bytes) > MAX_FILE_SIZE:
        raise HTTPException(
            status_code=400, detail="Plik przekracza dozwolony limit 10 MB."
        )

    ext = file.filename.split(".")[-1].lower() if file.filename else ""
    if ext not in ["pdf", "docx", "doc", "txt", "md"]:
        raise HTTPException(
            status_code=400,
            detail="Niedozwolony format pliku. Obsługiwane to PDF, DOCX, DOC, TXT, MD.",
        )

    # Ekstrakcja teksu
    text_content = ""
    try:
        if ext == "pdf":
            text_content = parse_pdf(file_bytes)
        elif ext in ["docx", "doc"]:
            text_content = parse_docx(file_bytes)
        else:
            text_content = file_bytes.decode("utf-8")
    except Exception as e:
        raise HTTPException(
            status_code=500, detail=f"Bład poczas ekstrakcji tekstu z pliku: {str(e)}"
        )

    import uuid

    doc_id = str(uuid.uuid4())

    doc_meta = {
        "id": doc_id,
        "filename": file.filename,
        "mime_type": file.content_type or "application/octet-stream",
        "uploaded_at": datetime.now(timezone.utc).isoformat(),
        "size_bytes": len(file_bytes),
        "extracted_text": text_content,
        "type": "user_upload",
    }

    # Wektoryzacja tekstu za pomocą Pinecone
    try:
        from rag_pipeline.vector_store import ingest_documents
        from langchain_text_splitters import RecursiveCharacterTextSplitter

        splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=150)
        chunks = splitter.create_documents(
            texts=[text_content],
            metadatas=[
                {
                    "source": file.filename,
                    "project_id": project_id,
                    "type": "user_upload",
                }
            ],
        )
        if chunks:
            # Użycie parametru namespace dla pełnej partycjonowanej izolacji tenantów
            tenant_namespace = f"tenant_{clerk_id}_{project_id}"
            ingest_documents(chunks, namespace=tenant_namespace)
    except Exception as e:
        # Fallback jeśli RAG jest offnięty - plik dodany tylko do DB
        print(f"Warning: RAG upload failed: {e}")

    # Aktualizacja bazy
    # Usunięcie starych jeśli plik ma tą samą nazwę
    ext_context["resources"] = [
        d for d in resources if d.get("filename") != file.filename
    ]
    ext_context["resources"].append(doc_meta)

    # Ponownie przypisz (SQLAlchemy JSON type behavior)
    project.external_context = dict(ext_context)
    from sqlalchemy.orm.attributes import flag_modified

    flag_modified(project, "external_context")
    db.commit()

    return ProjectResourceResponse(
        id=doc_meta["id"],
        filename=doc_meta["filename"],
        mime_type=doc_meta["mime_type"],
        uploaded_at=datetime.fromisoformat(doc_meta["uploaded_at"]),
        size_bytes=doc_meta["size_bytes"],
        extracted_text=doc_meta["extracted_text"],
    )


@router.get("/{project_id}/documents", response_model=List[ProjectResourceResponse])
async def get_project_documents(
    project_id: str, token_data: dict = Depends(verify_token), db=Depends(get_db)
):
    clerk_id = token_data.get("sub")
    project = (
        db.query(Project)
        .filter(Project.id == project_id, Project.clerk_user_id == clerk_id)
        .first()
    )
    if not project:
        raise HTTPException(status_code=404, detail="Brak dostępu do projektu")

    ext_context = project.external_context or {}
    # Legacy fallback
    if "documents" in ext_context and "resources" not in ext_context:
        ext_context["resources"] = ext_context.pop("documents")

    docs = ext_context.get("resources", [])

    return [
        ProjectResourceResponse(
            id=d.get("id", "legacy-id"),
            filename=d.get("filename"),
            mime_type=d.get("mime_type", "application/octet-stream"),
            uploaded_at=datetime.fromisoformat(d.get("uploaded_at"))
            if d.get("uploaded_at")
            else datetime.now(timezone.utc),
            size_bytes=d.get("size_bytes", d.get("size", 0)),
            extracted_text=d.get("extracted_text", d.get("text", "")),
        )
        for d in docs
    ]


@router.delete("/{project_id}/documents/{filename}")
async def delete_project_document(
    project_id: str,
    filename: str,
    token_data: dict = Depends(verify_token),
    db=Depends(get_db),
):
    clerk_id = token_data.get("sub")
    project = (
        db.query(Project)
        .filter(Project.id == project_id, Project.clerk_user_id == clerk_id)
        .first()
    )
    if not project:
        raise HTTPException(status_code=404, detail="Brak dostępu do projektu")

    ext_context = project.external_context or {}
    if "documents" in ext_context and "resources" not in ext_context:
        ext_context["resources"] = ext_context.pop("documents")

    docs = ext_context.get("resources", [])

    ext_context["resources"] = [d for d in docs if d.get("filename") != filename]
    project.external_context = dict(ext_context)
    from sqlalchemy.orm.attributes import flag_modified

    flag_modified(project, "external_context")
    db.commit()

    # Próba usunięcia z wektorów RAG
    try:
        from rag_pipeline.vector_store import get_vector_store

        tenant_namespace = f"tenant_{clerk_id}_{project_id}"
        store = get_vector_store(namespace=tenant_namespace)
        if store and store._index:
            store._index.delete(
                filter={"source": filename, "project_id": project_id},
                namespace=tenant_namespace,
            )
    except Exception as e:
        print(f"Warning: RAG deletion failed: {e}")

    return {"status": "deleted"}


@router.get("/{project_id}/audit")
async def get_project_audit(
    project_id: str, token_data: dict = Depends(verify_token), db=Depends(get_db)
):
    clerk_id = token_data.get("sub")
    project = (
        db.query(Project)
        .filter(Project.id == project_id, Project.clerk_user_id == clerk_id)
        .first()
    )
    if not project:
        raise HTTPException(status_code=404, detail="Brak dostępu do projektu")

    ext_context = project.external_context or {}
    last_audit = ext_context.get("last_audit")
    if not last_audit:
        return {"status": "no_audit"}
    return last_audit


@router.post("/{project_id}/audit")
async def run_project_audit(
    project_id: str, token_data: dict = Depends(verify_token), db=Depends(get_db)
):
    clerk_id = token_data.get("sub")
    project = (
        db.query(Project)
        .filter(Project.id == project_id, Project.clerk_user_id == clerk_id)
        .first()
    )
    if not project:
        raise HTTPException(status_code=404, detail="Brak dostępu do projektu")

    # Pobieranie kontentu ze wszystkich sekcji
    sections = (
        db.query(ProjectSection)
        .filter(ProjectSection.project_id == project_id)
        .order_by(ProjectSection.order)
        .all()
    )
    if not sections:
        raise HTTPException(
            status_code=400, detail="Brak sekcji w projekcie. Nie można wykonać audytu."
        )

    full_content = ""
    for sec in sections:
        if sec.content:
            sec_title_name = getattr(sec, "title", sec.section_type)
            full_content += f"\n\n### {sec_title_name} ###\n"
            full_content += sec.content

    # Uruchomienie agenta audytu
    audit_output = audit_final_document(
        project.id, project.program_name or "Ogólny", full_content
    )

    # Zapis do bazy jako dict uodporniony na surowe słowniki zwracane przez Langchain
    if hasattr(audit_output, "model_dump"):
        audit_dict = audit_output.model_dump()
    elif hasattr(audit_output, "dict"):
        audit_dict = audit_output.dict()
    elif isinstance(audit_output, dict):
        audit_dict = audit_output
    elif audit_output is None:
        audit_dict = {
            "is_approved": False,
            "export_status": "blocked",
            "overall_score": 0,
            "issues": [
                {
                    "category": "Błąd",
                    "severity": "critical",
                    "message": "Pusta odpowiedź z modelu",
                }
            ],
        }
    else:
        audit_dict = dict(audit_output)

    ext_context = project.external_context or {}
    ext_context["last_audit"] = audit_dict
    project.external_context = dict(ext_context)
    db.commit()

    return ext_context["last_audit"]


@router.delete("/{project_id}/audit")
async def clear_project_audit(
    project_id: str, token_data: dict = Depends(verify_token), db=Depends(get_db)
):
    clerk_id = token_data.get("sub")
    project = (
        db.query(Project)
        .filter(Project.id == project_id, Project.clerk_user_id == clerk_id)
        .first()
    )
    if not project:
        raise HTTPException(status_code=404, detail="Brak dostępu do projektu")

    ext_context = project.external_context or {}
    ext_context.pop("last_audit", None)
    project.external_context = dict(ext_context)
    db.commit()
    return {"status": "cleared"}



# --- CHATBOT PROJECT ---


class ChatMessageResponse(BaseModel):
    id: str
    role: str
    content: str
    created_at: datetime


class ChatMessageRequest(BaseModel):
    content: str
    active_section: Optional[str] = None
    active_section_title: Optional[str] = None


@router.get("/{project_id}/chat", response_model=List[ChatMessageResponse])
async def get_project_chat(
    project_id: str, token_data: dict = Depends(verify_token), db=Depends(get_db)
):
    clerk_id = token_data.get("sub")
    project = (
        db.query(Project)
        .filter(Project.id == project_id, Project.clerk_user_id == clerk_id)
        .first()
    )
    if not project:
        raise HTTPException(status_code=404, detail="Brak dostępu do projektu")

    messages = (
        db.query(ProjectChatMessage)
        .filter(ProjectChatMessage.project_id == project_id)
        .order_by(ProjectChatMessage.created_at.asc())
        .all()
    )

    return [
        ChatMessageResponse(
            id=m.id, role=m.role, content=m.content, created_at=m.created_at
        )
        for m in messages
    ]


@router.delete("/{project_id}/chat")
async def clear_project_chat(
    project_id: str, token_data: dict = Depends(verify_token), db=Depends(get_db)
):
    clerk_id = token_data.get("sub")
    project = (
        db.query(Project)
        .filter(Project.id == project_id, Project.clerk_user_id == clerk_id)
        .first()
    )
    if not project:
        raise HTTPException(status_code=404, detail="Brak dostępu do projektu")

    db.query(ProjectChatMessage).filter(
        ProjectChatMessage.project_id == project_id
    ).delete()
    db.commit()
    return {"status": "success", "message": "Historia czatu została wyczyszczona."}


@router.post("/{project_id}/chat", response_model=ChatMessageResponse)
async def post_project_chat(
    project_id: str,
    data: ChatMessageRequest,
    token_data: dict = Depends(verify_token),
    db=Depends(get_db),
):
    clerk_id = token_data.get("sub")
    project = (
        db.query(Project)
        .filter(Project.id == project_id, Project.clerk_user_id == clerk_id)
        .first()
    )
    if not project:
        raise HTTPException(status_code=404, detail="Brak dostępu do projektu")

    # Zapisz wiadomość użytkownika
    user_msg = ProjectChatMessage(
        project_id=project_id, role="user", content=data.content
    )
    db.add(user_msg)

    # Pobierz historię
    messages = (
        db.query(ProjectChatMessage)
        .filter(ProjectChatMessage.project_id == project_id)
        .order_by(ProjectChatMessage.created_at.asc())
        .all()
    )

    # Zbuduj kontekst projektu
    sections = (
        db.query(ProjectSection).filter(ProjectSection.project_id == project_id).all()
    )
    project_context = f"Tytuł: {project.title}\nStatus: {project.status}\n"
    for s in sections:
        if s.content and len(s.content) > 10:
            project_context += f"--- {s.section_type.upper()} ---\n{s.content[:3000]}\n"  # limit per section to avoid context overflow

    # Kontekst od RAG / External
    ext_context = project.external_context or {}
    last_audit = ext_context.get("last_audit")
    resources = ext_context.get("resources", [])

    extra_info = ""
    if ext_context:
        extra_info += f"\nZewnętrzny kontekst (np. GUS): {json.dumps({k:v for k,v in ext_context.items() if k not in ['last_audit', 'resources']}, ensure_ascii=False)}\n"

    if last_audit:
        extra_info += f"\nWynik ostatniego audytu: Ocena ogólna {last_audit.get('overall_score')}/100. Problemy: {len(last_audit.get('issues', []))}\n"

    if resources:
        extra_info += "\nZAWARTOSĆ WGRANYCH PLIKÓW (używaj ich jako głównego źródła wiedzy na prośbę użytkownika):\n"
        for r in resources:
            text = r.get('extracted_text', r.get('text', ''))
            # Ograniczenie wielkości tekstu by nie wyczerpać kontekstu
            truncated_text = text[:4000] + ("..." if len(text) > 4000 else "")
            extra_info += f"\n--- Plik: {r.get('filename')} ({r.get('size_bytes', 0)} bajtów) ---\n{truncated_text}\n"

    from langchain_core.messages import SystemMessage, HumanMessage, AIMessage

    llm = get_llm(task_type="chat")

    active_section_info = ""
    if hasattr(data, "active_section") and data.active_section:
        active_section_info = f"\nUżytkownik obecnie edytuje sekcję: {data.active_section_title or data.active_section}. Skup się na doradzaniu w jej kontekście."

    system_prompt = f"""Jesteś wirtualnym asystentem pomagającym użytkownikowi napisać i dopracować wniosek o dofinansowanie unijne.
Masz pełen dostęp do obecnego tekstu projektu użytkownika. Twoim celem jest odpowiadać na pytania, sugerować poprawki i wspierać pisanie fragmentów tekstu na podstawie tego co już wiadomo.

Zawsze pisz bardzo profesjonalnie, precyzyjnie i pomocnie w języku polskim.
{active_section_info}

Jeśli użytkownik prosi o sugestię tekstu (lub chcesz mu pomóc zastąpić/wstawić fragment), zawsze odpowiadaj normalną wiadomością, a propozycję fragmentu umieść wyłącznie wewnątrz tagu XML o formacie <SUGGESTION section="nazwa_sekcji">...treść...</SUGGESTION>.
BARDZO WAŻNE: Wewnątrz tagu <SUGGESTION> musi znaleźć się kompletny tekst do podmiany, zawsze rozpoczynający się od odpowiedniego nagłówka Markdown (np. "### Tytuł Sekcji"), tak aby po wklejeniu do edytora tekst miał od razu nadany tytuł. Zwracaj dokładną uwagę, do jakiej sekcji sugerujesz tekst.
Dzięki temu system automatycznie pozwoli użytkownikowi na wstrzyknięcie tekstu pod kursorem z poziomu czatu. Nie dodawaj niczego poza tagiem wewnątrz proponowanego wycinka!

KONTEKST PROJEKTU:
{project_context} {extra_info}
"""

    langchain_msgs = [SystemMessage(content=system_prompt)]

    for m in messages:
        if m.role == "user":
            langchain_msgs.append(HumanMessage(content=m.content))
        else:
            langchain_msgs.append(AIMessage(content=m.content))

    # Zabezpieczenie przed zbyt długim contextem (zostawiamy system + np 10 ostatnich)
    if len(langchain_msgs) > 11:
        langchain_msgs = [langchain_msgs[0]] + langchain_msgs[-10:]

    try:
        response = llm.invoke(langchain_msgs)
        from core.utils import safe_extract_text

        ai_content = safe_extract_text(response.content)
    except Exception as e:
        ai_content = f"Przepraszam, wystąpił techniczny błąd poczas generowania odpowiedzi: {str(e)}"

    ai_msg = ProjectChatMessage(
        project_id=project_id, role="assistant", content=ai_content
    )
    db.add(ai_msg)
    db.commit()
    db.refresh(ai_msg)

    return ChatMessageResponse(
        id=ai_msg.id,
        role=ai_msg.role,
        content=ai_msg.content,
        created_at=ai_msg.created_at,
    )


from fastapi import BackgroundTasks
from fastapi.responses import FileResponse
from utils.export_documents import export_to_pdf, export_to_docx
import tempfile
import os
import uuid
import logging
from datetime import datetime

logger = logging.getLogger(__name__)

EXPORT_TASKS = {}

def background_export_task(task_id, format_ext, content, filepath, request_template, project_title, company_name, version, date_str, extra_ctx):
    try:
        if format_ext == "pdf":
            success = export_to_pdf(content, filepath, template=request_template, project_title=project_title, company_name=company_name, version=version, date_str=date_str)
        else:
            success = export_to_docx(content, filepath, template=request_template, project_title=project_title, company_name=company_name, version=version, date_str=date_str, extra_context=extra_ctx)
        
        if success:
            EXPORT_TASKS[task_id]["status"] = "completed"
            logger.info(f"[ExportTask] Sukces dla zadania {task_id}")
        else:
            EXPORT_TASKS[task_id]["status"] = "error"
            EXPORT_TASKS[task_id]["error"] = "Błąd generowania wewnętrznego"
            logger.error(f"[ExportTask] Błąd generowania zadania {task_id}")
    except Exception as e:
        EXPORT_TASKS[task_id]["status"] = "error"
        EXPORT_TASKS[task_id]["error"] = str(e)
        logger.error(f"[ExportTask] Wyjątek dla zadania {task_id}: {e}")

@router.post("/{project_id}/export")
def export_project_document(
    project_id: str, request: ExportRequest, background_tasks: BackgroundTasks, token_data: dict = Depends(verify_token)
):
    db = SessionLocal()
    try:
        clerk_id = token_data.get("sub")
        project = (
            db.query(Project)
            .filter(Project.id == project_id, Project.clerk_user_id == clerk_id)
            .first()
        )
        if not project:
            raise HTTPException(status_code=404, detail="Projekt nie znaleziony")

        content = project.final_document_markdown

        if request.version_id:
            version = (
                db.query(ProjectExportVersion)
                .filter(ProjectExportVersion.id == request.version_id, ProjectExportVersion.project_id == project_id)
                .first()
            )
            if not version:
                raise HTTPException(status_code=404, detail="Wersja projektu nie znaleziona")
            content = version.content_markdown

        if not content:
            sections = (
                db.query(ProjectSection)
                .filter(ProjectSection.project_id == project_id)
                .order_by(ProjectSection.order)
                .all()
            )
            if not sections:
                raise HTTPException(
                    status_code=400,
                    detail="Brak sekcji we wniosku. Uzupełnij treść najpierw.",
                )

            content = f"# {project.title}\n\n"
            for s in sections:
                sec_title = UNIVERSAL_FALLBACK_MAP.get(
                    s.section_type, getattr(s, "title", s.section_type.replace("_", " ").title())
                )
                content += f"## {sec_title}\n\n"
                if s.content:
                    content += f"{s.content}\n\n"

        format_ext = request.format.lower()
        if format_ext not in ["pdf", "docx"]:
            raise HTTPException(
                status_code=400, detail="Format musi być 'pdf' lub 'docx'"
            )

        temp_dir = tempfile.gettempdir()
        safe_title = (
            "".join(c for c in project.title if c.isalnum() or c in " _-")
            .strip()
            .replace(" ", "_")
        )
        if not safe_title:
            safe_title = "Wniosek"

        filename = f"{safe_title}.{format_ext}"
        filepath = os.path.join(temp_dir, f"export_{uuid.uuid4().hex}.{format_ext}")

        company_name = "Brak nazwy firmy"
        if project.external_context and "company_data" in project.external_context:
            company_data = project.external_context["company_data"]
            company_name = company_data.get("name", "Brak nazwy firmy")
            if "nip" in company_data:
                company_name += f" (NIP: {company_data['nip']})"
        elif project.external_context and "name" in project.external_context:
            company_name = project.external_context["name"]
            if "nip" in project.external_context:
                company_name += f" (NIP: {project.external_context['nip']})"
        elif project.external_context and "company_name" in project.external_context:
            company_name = project.external_context["company_name"]
            if "nip" in project.external_context:
                company_name += f" (NIP: {project.external_context['nip']})"

        version = "1.0"
        date_str = datetime.now().strftime("%d.%m.%Y")

        extra_ctx = {}
        if project.external_context:
            extra_ctx = dict(project.external_context)
            if "company_data" in project.external_context:
                cd = project.external_context["company_data"]
                extra_ctx["nip"] = cd.get("nip", extra_ctx.get("nip"))
                extra_ctx["krs"] = cd.get("krs", extra_ctx.get("krs"))
                extra_ctx["regon"] = cd.get("regon", extra_ctx.get("regon"))

        extra_ctx["beneficjent"] = {
            "nazwa": company_name,
            "nip": extra_ctx.get("nip", "Brak danych"),
            "krs": extra_ctx.get("krs", "Brak danych"),
            "regon": extra_ctx.get("regon", "Brak danych"),
        }

        extra_ctx["projekt"] = {
            "tytul": project.title,
            "akronim": "".join(
                [word[0] for word in project.title.split() if word]
            ).upper()
            if project.title
            else "",
            "program": project.program_name or project.program_type,
            "budzet_calkowity": project.estimated_value or "Brak danych",
            "dofinansowanie": "Zgodnie z wnioskiem",
            "koszty_posrednie": "Zgodnie z limitem dla programu ryczałtowego",
        }

        task_id = uuid.uuid4().hex
        EXPORT_TASKS[task_id] = {
            "status": "processing",
            "filepath": filepath,
            "filename": filename,
            "format": format_ext
        }

        background_tasks.add_task(
            background_export_task,
            task_id,
            format_ext,
            content,
            filepath,
            request.template,
            project.title,
            company_name,
            version,
            date_str,
            extra_ctx
        )

        return {
            "task_id": task_id,
            "status": "processing",
            "message": "Dokument generuje się w tle. Odpytuj endpoint /export/status/{task_id}."
        }

    except HTTPException:
        raise
    except Exception as e:
        logger.error(f"Błąd eksportu: {e}", exc_info=True)
        raise HTTPException(status_code=500, detail=str(e))
    finally:
        db.close()

@router.get("/{project_id}/export/status/{task_id}")
def get_export_status(project_id: str, task_id: str, token_data: dict = Depends(verify_token)):
    if task_id not in EXPORT_TASKS:
        raise HTTPException(404, "Zadanie nie istnieje")
    
    task = EXPORT_TASKS[task_id]
    if task["status"] == "completed":
        return {
            "status": "completed",
            "download_url": f"/api/projects/{project_id}/export/download/{task_id}"
        }
    return {"status": task["status"], "error": task.get("error")}

@router.get("/{project_id}/export/download/{task_id}")
def download_export(project_id: str, task_id: str, token_data: dict = Depends(verify_token)):
    if task_id not in EXPORT_TASKS:
        raise HTTPException(404, "Zadanie nie istnieje")
        
    task = EXPORT_TASKS[task_id]
    if task["status"] != "completed":
        raise HTTPException(400, "Dokument nie jest gotowy")
        
    media_type = "application/pdf" if task["format"] == "pdf" else "application/vnd.openxmlformats-officedocument.wordprocessingml.document"
    return FileResponse(
        task["filepath"],
        media_type=media_type,
        filename=task["filename"]
    )


@router.get("/{project_id}/versions")
async def get_project_export_versions(
    project_id: str, token_data: dict = Depends(verify_token), db=Depends(get_db)
):
    clerk_id = token_data.get("sub")
    project = (
        db.query(Project)
        .filter(Project.id == project_id, Project.clerk_user_id == clerk_id)
        .first()
    )
    if not project:
        raise HTTPException(status_code=404, detail="Projekt nie znaleziony")

    versions = (
        db.query(ProjectExportVersion)
        .filter(ProjectExportVersion.project_id == project_id)
        .order_by(ProjectExportVersion.version_number.desc())
        .all()
    )

    result = []
    for v in versions:
        result.append(
            {
                "id": v.id,
                "version_number": v.version_number,
                "title": v.title,
                "created_at": v.created_at.isoformat() if v.created_at else None,
            }
        )
    return result


class CreateVersionRequest(BaseModel):
    title: Optional[str] = None


@router.post("/{project_id}/versions")
async def create_project_export_version(
    project_id: str,
    data: CreateVersionRequest,
    token_data: dict = Depends(verify_token),
    db=Depends(get_db),
):
    clerk_id = token_data.get("sub")
    project = (
        db.query(Project)
        .filter(Project.id == project_id, Project.clerk_user_id == clerk_id)
        .first()
    )
    if not project:
        raise HTTPException(status_code=404, detail="Projekt nie znaleziony")

    if not project.final_document_markdown:
        # Auto-compile current doc if empty
        sections = (
            db.query(ProjectSection)
            .filter(ProjectSection.project_id == project_id)
            .order_by(ProjectSection.order)
            .all()
        )
        content = f"# {project.title}\n\n"
        for s in sections:
            sec_title = UNIVERSAL_FALLBACK_MAP.get(s.section_type, getattr(s, "title", s.section_type.upper()))
            content += f"## {sec_title}\n\n"
            if s.content:
                content += f"{s.content}\n\n"
        project.final_document_markdown = content
        db.commit()

    last_v = (
        db.query(ProjectExportVersion)
        .filter(ProjectExportVersion.project_id == project_id)
        .order_by(ProjectExportVersion.version_number.desc())
        .first()
    )
    new_v_number = (last_v.version_number + 1) if last_v else 1

    new_version = ProjectExportVersion(
        id=str(uuid.uuid4()),
        project_id=project_id,
        version_number=new_v_number,
        title=data.title or f"Wersja {new_v_number}",
        content_markdown=project.final_document_markdown,
        export_type="archived",
    )
    db.add(new_version)
    db.commit()
    db.refresh(new_version)

    return {
        "id": new_version.id,
        "version_number": new_version.version_number,
        "title": new_version.title,
        "created_at": new_version.created_at.isoformat()
        if new_version.created_at
        else None,
    }


class ExpenseEvaluateRequest(BaseModel):
    expense_description: str = Field(..., title="Opis wydatku")
    expense_amount: float = Field(..., title="Kwota wydatku")


@router.post("/{project_id}/evaluate-expense")
async def evaluate_project_expense_endpoint(
    project_id: str,
    request: ExpenseEvaluateRequest,
    token_data: dict = Depends(verify_token),
    db=Depends(get_db),
):
    clerk_id = token_data.get("sub")
    project = (
        db.query(Project)
        .filter(Project.id == project_id, Project.clerk_user_id == clerk_id)
        .first()
    )
    if not project:
        raise HTTPException(status_code=404, detail="Projekt nie znaleziony")

    # Call evaluator agent with strict Pydantic parsing (Faza 4)
    from agents.evaluator import evaluate_project_expense

    try:
        company_size = "MŚP"
        if project.external_context and "size" in project.external_context:
            company_size = project.external_context["size"]

        tenant_id = token_data.get("sub")

        evaluation_result = evaluate_project_expense(
            expense_description=request.expense_description,
            expense_amount=request.expense_amount,
            project_title=project.title,
            program_name=project.program_name,
            company_size=company_size,
            tenant_id=tenant_id,
        )

        return {
            "status": "ok",
            "project_id": project_id,
            "evaluation": evaluation_result.dict(),
        }
    except Exception as e:
        logger.error(f"Evaluating expense failed: {str(e)}", exc_info=True)
        raise HTTPException(
            status_code=500, detail=f"Błąd silnika AI podczas oceny wydatku: {str(e)}"
        )


class MSMERequest(BaseModel):
    krs_number: str = Field(..., title="Numer KRS firmy do weryfikacji powiązań")


@router.post("/{project_id}/analyze-msme")
async def analyze_project_msme_endpoint(
    project_id: str,
    request: MSMERequest,
    token_data: dict = Depends(verify_token),
    db=Depends(get_db),
):
    """
    Endpoint pozwalający na badanie wielkości przedsiębiorstwa
    przy pomocy analizy wielowymiarowej z bazy Neo4j i KRS_Graph_RAG.
    """
    clerk_id = token_data.get("sub")
    project = (
        db.query(Project)
        .filter(Project.id == project_id, Project.clerk_user_id == clerk_id)
        .first()
    )
    if not project:
        raise HTTPException(status_code=404, detail="Projekt nie znaleziony")

    from agents.tools.krs_graph_tool import analyze_company_network

    try:
        # Analiza po stronie agencji analitycznej
        analysis_result = analyze_company_network.invoke(request.krs_number)

        # Pamiętajmy, by wpisać wstępną analizę do bazy aby Finansista i Prawnik nie musieli robić tego samemu
        if (
            "size" not in project.external_context
            or project.external_context.get("krs_analysis") is None
        ):
            context = project.external_context or {}
            context["krs_analysis"] = analysis_result
            project.external_context = context
            db.commit()

        return {
            "status": "ok",
            "project_id": project_id,
            "krs_network_analysis": analysis_result,
        }
    except Exception as e:
        logger.error(f"Evaluating MSME failed: {str(e)}", exc_info=True)
        raise HTTPException(
            status_code=500, detail=f"Błąd narzędzi Neo4j/KRS podczas oceny: {str(e)}"
        )