feat(routines): enrich single AI suggestions with concise context
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5 changed files with 113 additions and 8 deletions
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@ -5,7 +5,7 @@ from uuid import UUID, uuid4
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from fastapi import APIRouter, Depends, HTTPException
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from pydantic import BaseModel as PydanticBase
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from sqlmodel import Session, SQLModel, col, select
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from sqlmodel import Field, Session, SQLModel, col, select
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from db import get_session
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from innercontext.api.utils import get_or_404
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@ -76,6 +76,8 @@ class SuggestedStep(SQLModel):
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action_notes: Optional[str] = None
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dose: Optional[str] = None
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region: Optional[str] = None
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why_this_step: Optional[str] = None
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optional: Optional[bool] = None
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class SuggestRoutineRequest(SQLModel):
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@ -86,9 +88,16 @@ class SuggestRoutineRequest(SQLModel):
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leaving_home: Optional[bool] = None
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class RoutineSuggestionSummary(SQLModel):
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primary_goal: str = ""
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constraints_applied: list[str] = Field(default_factory=list)
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confidence: float = 0.0
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class RoutineSuggestion(SQLModel):
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steps: list[SuggestedStep]
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reasoning: str
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summary: Optional[RoutineSuggestionSummary] = None
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class SuggestBatchRequest(SQLModel):
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@ -116,7 +125,17 @@ class BatchSuggestion(SQLModel):
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# ---------------------------------------------------------------------------
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class _StepOut(PydanticBase):
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class _SingleStepOut(PydanticBase):
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product_id: Optional[str] = None
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action_type: Optional[GroomingAction] = None
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dose: Optional[str] = None
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region: Optional[str] = None
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action_notes: Optional[str] = None
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why_this_step: Optional[str] = None
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optional: Optional[bool] = None
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class _BatchStepOut(PydanticBase):
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product_id: Optional[str] = None
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action_type: Optional[GroomingAction] = None
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dose: Optional[str] = None
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@ -124,15 +143,22 @@ class _StepOut(PydanticBase):
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action_notes: Optional[str] = None
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class _SummaryOut(PydanticBase):
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primary_goal: str
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constraints_applied: list[str]
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confidence: float
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class _SuggestionOut(PydanticBase):
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steps: list[_StepOut]
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steps: list[_SingleStepOut]
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reasoning: str
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summary: _SummaryOut
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class _DayPlanOut(PydanticBase):
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date: str
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am_steps: list[_StepOut]
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pm_steps: list[_StepOut]
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am_steps: list[_BatchStepOut]
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pm_steps: list[_BatchStepOut]
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reasoning: str
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@ -424,6 +450,8 @@ ZASADY PLANOWANIA:
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- Jeśli krok to produkt: podaj poprawny UUID z listy.
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- Jeśli krok to czynność pielęgnacyjna: product_id = null. Dozwolone akcje są ściśle określone w schemacie (action_type).
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- Nie zwracaj "pustych" kroków: każdy krok musi mieć product_id albo action_type.
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- Pole region uzupełniaj tylko gdy ma znaczenie kliniczne/praktyczne (np. broda, wąsy, okolica oczu, szyja).
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Dla standardowych kroków pielęgnacji całej twarzy pozostaw region puste.
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JAK ROZWIĄZYWAĆ KONFLIKTY:
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- Gdy cel użytkownika koliduje z bezpieczeństwem, wybierz bezpieczeństwo.
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@ -432,6 +460,17 @@ JAK ROZWIĄZYWAĆ KONFLIKTY:
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"""
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_ROUTINES_SINGLE_EXTRA = """\
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DODATKOWE WYMAGANIA DLA TRYBU JEDNEJ RUTYNY:
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- Każdy krok powinien mieć zwięzłe why_this_step (maks. jedno zdanie).
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- Pole optional ustawiaj na true tylko dla kroków niekrytycznych.
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- Uzupełnij summary:
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- primary_goal: główny cel tej rutyny,
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- constraints_applied: lista kluczowych ograniczeń zastosowanych przy planowaniu,
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- confidence: liczba 0-1.
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"""
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# ---------------------------------------------------------------------------
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# Helper
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# ---------------------------------------------------------------------------
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@ -515,6 +554,7 @@ def suggest_routine(
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"INPUT DATA:\n"
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f"{skin_ctx}\n{grooming_ctx}\n{history_ctx}\n{day_ctx}\n{products_ctx}\n{objectives_ctx}"
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f"{notes_line}\n"
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f"{_ROUTINES_SINGLE_EXTRA}\n"
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"Zwróć JSON zgodny ze schematem."
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)
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@ -545,10 +585,34 @@ def suggest_routine(
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action_notes=s.get("action_notes"),
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dose=s.get("dose"),
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region=s.get("region"),
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why_this_step=s.get("why_this_step"),
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optional=s.get("optional"),
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)
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for s in parsed.get("steps", [])
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]
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return RoutineSuggestion(steps=steps, reasoning=parsed.get("reasoning", ""))
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summary_raw = parsed.get("summary") or {}
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confidence_raw = summary_raw.get("confidence", 0)
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try:
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confidence = float(confidence_raw)
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except (TypeError, ValueError):
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confidence = 0.0
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confidence = max(0.0, min(1.0, confidence))
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constraints_applied = summary_raw.get("constraints_applied") or []
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if not isinstance(constraints_applied, list):
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constraints_applied = []
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summary = RoutineSuggestionSummary(
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primary_goal=str(summary_raw.get("primary_goal") or ""),
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constraints_applied=[str(x) for x in constraints_applied],
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confidence=confidence,
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)
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return RoutineSuggestion(
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steps=steps,
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reasoning=parsed.get("reasoning", ""),
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summary=summary,
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)
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@router.post("/suggest-batch", response_model=BatchSuggestion)
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@ -624,6 +688,8 @@ def suggest_batch(
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action_notes=s.get("action_notes"),
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dose=s.get("dose"),
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region=s.get("region"),
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why_this_step=s.get("why_this_step"),
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optional=s.get("optional"),
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)
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)
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return result
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@ -171,6 +171,10 @@
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"suggest_errorSave": "Error saving.",
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"suggest_amMorning": "AM (morning)",
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"suggest_pmEvening": "PM (evening)",
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"suggest_summaryPrimaryGoal": "Primary goal",
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"suggest_summaryConfidence": "Confidence",
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"suggest_summaryConstraints": "Constraints",
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"suggest_stepOptionalBadge": "optional",
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"medications_title": "Medications",
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"medications_count": "{count} entries",
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@ -171,6 +171,10 @@
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"suggest_errorSave": "Błąd podczas zapisywania.",
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"suggest_amMorning": "AM (rano)",
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"suggest_pmEvening": "PM (wieczór)",
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"suggest_summaryPrimaryGoal": "Główny cel",
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"suggest_summaryConfidence": "Pewność",
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"suggest_summaryConstraints": "Ograniczenia",
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"suggest_stepOptionalBadge": "opcjonalny",
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"medications_title": "Leki",
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"medications_count": "{count} wpisów",
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@ -224,11 +224,20 @@ export interface SuggestedStep {
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action_notes?: string;
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dose?: string;
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region?: string;
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why_this_step?: string;
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optional?: boolean;
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}
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export interface RoutineSuggestionSummary {
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primary_goal: string;
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constraints_applied: string[];
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confidence: number;
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}
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export interface RoutineSuggestion {
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steps: SuggestedStep[];
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reasoning: string;
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summary?: RoutineSuggestionSummary;
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}
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export interface DayPlan {
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@ -45,7 +45,7 @@
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function stepMeta(step: SuggestedStep): string {
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const parts: string[] = [];
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if (step.dose) parts.push(step.dose);
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if (step.region) parts.push(step.region);
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if (step.region && step.region.toLowerCase() !== 'face') parts.push(step.region);
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if (step.action_notes && !step.action_type) parts.push(step.action_notes);
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return parts.join(' · ');
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}
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@ -209,6 +209,20 @@
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</CardContent>
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</Card>
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{#if suggestion.summary}
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<Card class="border-muted bg-muted/30">
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<CardContent class="space-y-2 pt-4">
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<p class="text-sm"><span class="font-medium">{m["suggest_summaryPrimaryGoal"]()}:</span> {suggestion.summary.primary_goal || '—'}</p>
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<p class="text-sm"><span class="font-medium">{m["suggest_summaryConfidence"]()}:</span> {Math.round((suggestion.summary.confidence ?? 0) * 100)}%</p>
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{#if suggestion.summary.constraints_applied?.length}
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<p class="text-xs text-muted-foreground">
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{m["suggest_summaryConstraints"]()}: {suggestion.summary.constraints_applied.join(' · ')}
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</p>
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{/if}
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</CardContent>
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</Card>
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{/if}
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<!-- Steps -->
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<div class="space-y-2">
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{#each suggestion.steps as step, i (i)}
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@ -217,10 +231,18 @@
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{i + 1}
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</span>
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<div class="min-w-0 flex-1">
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<div class="flex items-center gap-2">
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<p class="text-sm font-medium">{stepLabel(step)}</p>
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{#if step.optional}
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<Badge variant="secondary">{m["suggest_stepOptionalBadge"]()}</Badge>
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{/if}
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</div>
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{#if stepMeta(step)}
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<p class="text-xs text-muted-foreground">{stepMeta(step)}</p>
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{/if}
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{#if step.why_this_step}
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<p class="text-xs text-muted-foreground italic">{step.why_this_step}</p>
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{/if}
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</div>
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</div>
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{/each}
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