"""Frontend analysis-result contract (camelCase) for the SSE events endpoint.

Mirrors the web client's Zod schema (`analysisResultSchema` / `analysisIssueSchema`):

    analysisResultSchema = z.object({
      id, fileName, fileType, fileSize,
      status: 'pending' | 'processing' | 'completed' | 'failed',
      issues: analysisIssue[],
      summary, riskScore (0-10), createdAt, completedAt: string | null,
    })

Backend document statuses are normalized to that enum; report findings
(`core.analysis.report_schema.Finding`) are mapped to `analysisIssueSchema`.
`riskScore` and `summary` are not stored — they are derived from the issue
list on completion (severity-weighted score, counts-based summary line).
"""

from __future__ import annotations

import datetime as dt
from typing import Any, Literal

from pydantic import BaseModel, ConfigDict, Field

from src.contract_check.core.analysis.analyzer import checklist_title
from src.contract_check.core.logging import get_logger

log = get_logger(__name__)

AnalysisStatus = Literal["pending", "processing", "completed", "failed"]
IssueSeverity = Literal["critical", "warning", "info"]

# Backend documents.status -> analysisResultSchema.status.
_STATUS_MAP: dict[str, AnalysisStatus] = {
    "queued": "pending",
    "extracting": "processing",
    "prescreening": "processing",
    "ocr": "processing",
    "analyzing": "processing",
    "done": "completed",
    # Terminal without a report; surfaced to the client as a completed,
    # empty-issues result (prescreen routed it away from deep analysis).
    "manual_review": "completed",
    "failed": "failed",
}

# Finding.severity -> analysisIssueSchema.severity.
_SEVERITY_MAP: dict[str, IssueSeverity] = {
    "high": "critical",
    "medium": "warning",
    "low": "info",
}

# riskScore weights (capped at 10) per issue severity.
_RISK_WEIGHTS: dict[IssueSeverity, int] = {"critical": 4, "warning": 2, "info": 1}


class AnalysisIssue(BaseModel):
    """One contract risk finding in the frontend contract shape."""

    model_config = ConfigDict(populate_by_name=True)

    id: str
    severity: IssueSeverity
    category: str
    title: str
    description: str
    fragment: str | None = None
    line_number: int | None = Field(default=None, serialization_alias="lineNumber")


class AnalysisResult(BaseModel):
    """analysisResultSchema mirror — the SSE event payload for /reports/{id}/events."""

    model_config = ConfigDict(populate_by_name=True)

    id: str
    file_name: str = Field(serialization_alias="fileName")
    file_type: str = Field(default="", serialization_alias="fileType")
    file_size: int = Field(default=0, serialization_alias="fileSize")
    status: AnalysisStatus
    issues: list[AnalysisIssue] = Field(default_factory=list)
    summary: str = ""
    risk_score: int = Field(default=0, ge=0, le=10, serialization_alias="riskScore")
    created_at: str = Field(serialization_alias="createdAt")
    completed_at: str | None = Field(default=None, serialization_alias="completedAt")

    def dump_json(self) -> str:
        return self.model_dump_json(by_alias=True)


def _iso(value: Any) -> str:
    if isinstance(value, dt.datetime):
        return value.isoformat()
    return str(value)


def _map_finding(finding: dict[str, Any], index: int) -> AnalysisIssue:
    checklist_id = str(finding.get("checklist_id") or "unknown")
    severity = _SEVERITY_MAP.get(str(finding.get("severity") or ""), "info")
    risk = str(finding.get("risk") or "").strip()
    recommendation = str(finding.get("recommendation") or "").strip()
    description = risk or checklist_title(checklist_id)
    if recommendation:
        description = f"{description}\n\nРекомендация: {recommendation}"
    quote = str(finding.get("quote") or "").strip()
    section_ref = str(finding.get("section_ref") or "").strip()
    fragment = f"{quote} (п. {section_ref})" if quote and section_ref else (quote or None)
    return AnalysisIssue(
        id=f"{checklist_id}-{index}",
        severity=severity,
        category=checklist_id,
        title=checklist_title(checklist_id),
        description=description,
        fragment=fragment,
    )


def to_analysis_result(row: dict[str, Any]) -> AnalysisResult:
    """Build an AnalysisResult from a `fetch_document_status_for_user` row."""
    doc_status = str(row["status"])
    status = _STATUS_MAP.get(doc_status)
    if status is None:
        log.warning("unknown_document_status_mapped", status=doc_status)
        status = "processing" if doc_status not in ("done",) else "completed"

    issues: list[AnalysisIssue] = []
    risk_score = 0
    summary = ""

    if status == "completed" and doc_status == "done":
        content = row.get("content_json") or {}
        findings = content.get("findings", []) if isinstance(content, dict) else []
        issues = [_map_finding(f, i) for i, f in enumerate(findings)]
        risk_score = min(10, sum(_RISK_WEIGHTS[i.severity] for i in issues))
        counts = {"critical": 0, "warning": 0, "info": 0}
        for issue in issues:
            counts[issue.severity] += 1
        summary = (
            "Риски не найдены"
            if not issues
            else (
                f"Критичных: {counts['critical']}, "
                f"предупреждений: {counts['warning']}, "
                f"замечаний: {counts['info']}"
            )
        )

    return AnalysisResult(
        id=str(row["id"]),
        file_name=str(row.get("filename") or ""),
        file_type=str(row.get("mime") or ""),
        file_size=int(row.get("bytes") or 0),
        status=status,
        issues=issues,
        summary=summary,
        risk_score=risk_score,
        created_at=_iso(row["created_at"]),
        completed_at=_iso(row["report_created_at"]) if row.get("report_created_at") else None,
    )