impactflow_discovery/app/schemas.py

"""Pydantic request/response models for the discovery API."""
from datetime import datetime
from typing import Optional

from pydantic import BaseModel


class StartResponse(BaseModel):
    conversation_id: str


class RespondRequest(BaseModel):
    prompt_alive: str = ""
    prompt_friction: str = ""
    prompt_pull: str = ""
    prompt_recognition: str = ""
    prompt_future: str = ""
    prompt_goals_short: str = ""
    prompt_goals_long: str = ""


class RespondResponse(BaseModel):
    conversation_id: str
    status: str


class Confidence(BaseModel):
    triad: Optional[str] = None
    type: Optional[str] = None
    variant: Optional[str] = None
    ikigai: Optional[str] = None


class ProfileResponse(BaseModel):
    id: str
    user_id: str
    conversation_id: str
    generated_at: datetime
    triad: Optional[str] = None
    probable_type: Optional[int] = None
    wing: Optional[int] = None
    instinctual_variant: Optional[str] = None
    instinctual_stack: Optional[str] = None
    love_summary: Optional[str] = None
    strength_summary: Optional[str] = None
    mission_summary: Optional[str] = None
    vocation_summary: Optional[str] = None
    overlap_narrative: Optional[str] = None
    short_term_goals: Optional[str] = None
    long_term_goals: Optional[str] = None
    confidence: Optional[Confidence] = None
    locked: bool = False
    extraction_notes: Optional[str] = None


class ProfileUpdate(BaseModel):
    """Partial edit of a profile's prose. Only fields explicitly provided are
    updated (see exclude_unset in the router). The AI's structural inference
    (triad/type/wing/variant) and confidence are not editable here."""

    love_summary: Optional[str] = None
    strength_summary: Optional[str] = None
    mission_summary: Optional[str] = None
    vocation_summary: Optional[str] = None
    overlap_narrative: Optional[str] = None
    short_term_goals: Optional[str] = None
    long_term_goals: Optional[str] = None


class ConfirmResponse(BaseModel):
    status: str


# -- Phase 2: AI coach reflection loop ---------------------------------------


class ReflectRequest(BaseModel):
    # Empty/omitted starts the loop (the coach's opening reflection).
    message: str = ""
    # Phase 5: optional refinement focus, e.g. "goals" to steer the coach
    # toward sharpening the near/long-term goals.
    focus: str = ""


class ReflectionMessageOut(BaseModel):
    role: str  # "coach" or "person"
    content: str
    sequence: int
    created_at: datetime


class ReflectTurnResponse(BaseModel):
    """One coach turn, plus the (possibly revised) profile."""

    message: ReflectionMessageOut
    profile: ProfileResponse
    revised: bool = False
    revision_note: Optional[str] = None


class ReflectionThreadResponse(BaseModel):
    messages: list[ReflectionMessageOut]
    profile: ProfileResponse


class ProfileRevisionOut(BaseModel):
    id: str
    source: str  # "extraction" | "reflection" | "manual_edit"
    fields: dict
    note: Optional[str] = None
    created_at: datetime


# -- Phase 3: coaching preferences + check-ins -------------------------------


class CoachingPreferencesOut(BaseModel):
    coaching_frequency: str
    coaching_style: str
    misalignment_threshold: str
    friction_tolerance: str
    prefer_questions_over_directives: bool
    time_of_day_preference: str
    auto_generated: bool
    updated_at: datetime


class CoachingPreferencesUpdate(BaseModel):
    coaching_frequency: Optional[str] = None
    coaching_style: Optional[str] = None
    misalignment_threshold: Optional[str] = None
    friction_tolerance: Optional[str] = None
    prefer_questions_over_directives: Optional[bool] = None
    time_of_day_preference: Optional[str] = None


class CheckinOut(BaseModel):
    id: str
    body: str
    created_at: datetime
    still_valid: Optional[bool] = None
    response_note: Optional[str] = None
    acknowledged_at: Optional[datetime] = None


class CheckinRespondRequest(BaseModel):
    still_valid: bool
    note: str = ""


class RunCheckinsResponse(BaseModel):
    considered: int
    generated: int


# -- Phase 4: task-to-goal integration ---------------------------------------


class FoundationOut(BaseModel):
    key: str
    label: str
    text: Optional[str] = None


class TaskMappingCreate(BaseModel):
    external_task_id: str
    foundation: str
    minutes: int = 0
    task_label: str = ""
    # When the work happened; defaults to now if omitted.
    occurred_at: Optional[datetime] = None


class TaskMappingOut(BaseModel):
    id: str
    external_task_id: str
    task_label: Optional[str] = None
    foundation: str
    minutes: int
    occurred_at: datetime
    created_at: datetime


class FoundationPattern(BaseModel):
    foundation: str
    label: str
    minutes: int
    task_count: int
    last_at: Optional[datetime] = None
    share: float


class WorkPatternsOut(BaseModel):
    window_days: int
    total_minutes: int
    by_foundation: list[FoundationPattern]
    neglected: list[str]


# -- Phase 5: iteration & polish ---------------------------------------------


class GoalHistoryEntry(BaseModel):
    value: Optional[str] = None
    source: Optional[str] = None
    at: Optional[datetime] = None


class GoalHistoryOut(BaseModel):
    short_term_goals: list[GoalHistoryEntry]
    long_term_goals: list[GoalHistoryEntry]


class SuggestFoundationRequest(BaseModel):
    task_label: str


class SuggestFoundationOut(BaseModel):
    foundation: str
    label: str
    rationale: str = ""
    confidence: str = "low"


class ConversationResponse(BaseModel):
    id: str
    user_id: str
    started_at: datetime
    completed_at: Optional[datetime] = None
    prompt_alive: Optional[str] = None
    prompt_friction: Optional[str] = None
    prompt_pull: Optional[str] = None
    prompt_recognition: Optional[str] = None
    prompt_future: Optional[str] = None
    prompt_goals_short: Optional[str] = None
    prompt_goals_long: Optional[str] = None

    model_config = {"from_attributes": True}