"""AI use-case metadata and validation.

Defines the structured metadata that describes *why* and *how* an AI agent
may process data. This is the governance contract for an agent definition:
which data categories it may touch, which providers/models/actions are
allowed, and whether human oversight is required.

Used by:
- ``app/ai/data_policy.py`` — runtime enforcement of allowed data categories
- ``app/ai/oversight.py`` — human-review policy (``oversight_policy``)
- ``app/plugins/builtins/automation/agent_routes.py`` — PATCH/GET endpoints
"""

from __future__ import annotations

from typing import Any

from pydantic import BaseModel, Field

# ──────────────────────────────────────────────────────────────────────────
# Constants
# ──────────────────────────────────────────────────────────────────────────

# Known data categories an agent may declare it processes.
KNOWN_DATA_CATEGORIES = (
    "contact_data",
    "email_content",
    "calendar",
    "tasks",
    "dms",
    "communication",
    "financial",
    "public",
)

# Valid oversight policies.
OVERSIGHT_POLICIES = ("always_required", "on_high_risk", "never")

# Valid risk classes.
RISK_CLASSES = ("low", "medium", "high")

# Valid allowed actions.
KNOWN_ACTIONS = ("read", "summarize", "draft", "send", "create", "update", "delete")


class AIUseCaseMetadata(BaseModel):
    """Structured metadata describing an AI agent's intended use case.

    Attributes:
        intended_purpose: Human-readable description of the use case.
        owner: User ID or email responsible for the use case.
        data_categories: Data categories the agent may process.
        allowed_providers: Provider IDs the agent may use (empty = any).
        allowed_models: Model names the agent may use (empty = any).
        allowed_actions: Actions the agent may perform (empty = any).
        oversight_policy: When human review is required.
        risk_class: Risk classification of the use case.
        human_review_required: Whether a human must review outputs.
    """

    intended_purpose: str = Field(default="", max_length=1000)
    owner: str = Field(default="", max_length=255)
    data_categories: list[str] = Field(default_factory=list)
    allowed_providers: list[str] = Field(default_factory=list)
    allowed_models: list[str] = Field(default_factory=list)
    allowed_actions: list[str] = Field(default_factory=list)
    oversight_policy: str = Field(default="never")
    risk_class: str = Field(default="low")
    human_review_required: bool = False

    @classmethod
    def from_dict(cls, data: dict[str, Any] | None) -> "AIUseCaseMetadata":
        """Build metadata from a raw dict (e.g. the agent's JSONB column)."""
        if not data:
            return cls()
        # Only pass known fields so unknown keys don't break validation.
        known = {k: v for k, v in data.items() if k in cls.model_fields}
        return cls(**known)

    def to_dict(self) -> dict[str, Any]:
        """Serialize to a plain dict for JSONB storage."""
        return self.model_dump()


# ──────────────────────────────────────────────────────────────────────────
# Validation
# ──────────────────────────────────────────────────────────────────────────


def validate_ai_use_case(metadata: AIUseCaseMetadata, agent_definition: Any) -> list[str]:
    """Validate metadata against an agent configuration.

    Returns a list of human-readable warnings. An empty list means the
    metadata is consistent with the agent definition.

    Checks performed:
    - ``intended_purpose`` and ``owner`` are set.
    - ``data_categories`` are known values.
    - ``oversight_policy`` and ``risk_class`` are valid.
    - ``allowed_models`` (if non-empty) include the agent's configured model.
    - ``allowed_providers`` (if non-empty) include the agent's provider.
    - ``human_review_required`` is consistent with ``oversight_policy``.
    """
    warnings: list[str] = []

    if not metadata.intended_purpose.strip():
        warnings.append("intended_purpose is empty — describe the AI use case")

    if not metadata.owner.strip():
        warnings.append("owner is empty — set a responsible user or email")

    for cat in metadata.data_categories:
        if cat not in KNOWN_DATA_CATEGORIES:
            warnings.append(f"data_category '{cat}' is not a known category")

    if metadata.oversight_policy not in OVERSIGHT_POLICIES:
        warnings.append(
            f"oversight_policy '{metadata.oversight_policy}' is invalid "
            f"(expected one of {OVERSIGHT_POLICIES})"
        )

    if metadata.risk_class not in RISK_CLASSES:
        warnings.append(
            f"risk_class '{metadata.risk_class}' is invalid "
            f"(expected one of {RISK_CLASSES})"
        )

    # Model / provider consistency (only if the agent pins allowed values).
    agent_model = getattr(agent_definition, "llm_model", None)
    if metadata.allowed_models and agent_model:
        # Strip provider prefix for comparison (e.g. "openai/gpt-4o" -> "gpt-4o").
        bare_model = agent_model.split("/", 1)[-1]
        if agent_model not in metadata.allowed_models and bare_model not in metadata.allowed_models:
            warnings.append(
                f"agent model '{agent_model}' is not in allowed_models {metadata.allowed_models}"
            )

    agent_provider = getattr(agent_definition, "provider", None)
    if metadata.allowed_providers and agent_provider:
        if agent_provider not in metadata.allowed_providers:
            warnings.append(
                f"agent provider '{agent_provider}' is not in allowed_providers "
                f"{metadata.allowed_providers}"
            )

    # Oversight consistency.
    if metadata.oversight_policy == "always_required" and not metadata.human_review_required:
        warnings.append(
            "oversight_policy is 'always_required' but human_review_required is False"
        )
    if metadata.oversight_policy == "never" and metadata.human_review_required:
        warnings.append(
            "oversight_policy is 'never' but human_review_required is True"
        )

    return warnings