c1b80eb9a7
The pure Shannon entropy score penalized well-designed ads with multiple intentional visual elements (e.g. hero product + text + logo scored ~8/100). New composite score (0-100) weights four components: - Peak Dominance (30%): strength of #1 hotspot vs rest - Hierarchy Clarity (25%): monotonic intensity ordering - Gaze Coherence (25%): smooth spatial gaze path - Entropy Concentration (20%): sqrt-softened entropy The raw entropy score is preserved as entropy_score for users who want it, visible in the ScoreCard hover tooltip and PDF report. Also adds auto-create DB tables on startup for fresh Docker deploys. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
55 lines
1.1 KiB
Python
55 lines
1.1 KiB
Python
from datetime import datetime
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from pydantic import BaseModel
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class AnalysisSummary(BaseModel):
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id: str
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name: str
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model_used: str
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status: str
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original_filename: str
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image_width: int
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image_height: int
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overall_score: float | None = None
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entropy_score: float | None = None
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created_at: datetime
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model_config = {"from_attributes": True}
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class GazePoint(BaseModel):
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rank: int
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x: int
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y: int
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x_pct: float
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y_pct: float
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probability: float
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class Insight(BaseModel):
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type: str # "info" | "success" | "warning"
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title: str
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description: str
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class AnalysisDetail(AnalysisSummary):
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file_format: str
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gaze_sequence: list[GazePoint] | None = None
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hotspots: list[dict] | None = None
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insights: list[Insight] | None = None
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ai_insights: list[Insight] | None = None
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ai_score: int | None = None
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ai_score_reason: str | None = None
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ai_cost_usd: float | None = None
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aoi_count: int = 0
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class AnalysisCreate(BaseModel):
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name: str | None = None
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model: str = "deepgaze_iie"
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class AnalysisStatus(BaseModel):
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id: str
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status: str
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