Samir Boulahtit
50d50fcbd0
- Restructure score_breakdown from flat dict to grouped by category:
{technical_health: {flag: pts}, modernity: {...}, ...}
- Each category row shows score/max with progress bar + per-flag detail
(e.g. Technical Health 15/40 → "very slow: 15 pts")
- Color-coded: green for positive flags, orange for issues
- "No issues detected" shown for clean categories
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
43 lines
1.0 KiB
Python
43 lines
1.0 KiB
Python
# app/modules/prospecting/schemas/score.py
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"""Pydantic schemas for opportunity scoring."""
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import json
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from datetime import datetime
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from pydantic import BaseModel, field_validator
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class ProspectScoreResponse(BaseModel):
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"""Schema for prospect score response."""
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id: int
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prospect_id: int
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score: int
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technical_health_score: int
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modernity_score: int
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business_value_score: int
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engagement_score: int
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reason_flags: list[str] = []
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score_breakdown: dict[str, dict[str, int]] | None = None
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lead_tier: str | None = None
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notes: str | None = None
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created_at: datetime
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updated_at: datetime
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@field_validator("reason_flags", mode="before")
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@classmethod
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def parse_reason_flags(cls, v):
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if isinstance(v, str):
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return json.loads(v)
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return v
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@field_validator("score_breakdown", mode="before")
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@classmethod
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def parse_score_breakdown(cls, v):
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if isinstance(v, str):
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return json.loads(v)
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return v
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class Config:
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from_attributes = True
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