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| """Before/after image comparison module.""" | |
| from typing import Dict, Any, List, Tuple, Optional | |
| from scoring.engine import ScoreResponse | |
| def compare_scores(original: ScoreResponse, revised: ScoreResponse) -> Dict[str, Any]: | |
| """ | |
| Compare two scored images and produce delta analysis. | |
| Returns dict with: | |
| original_overall, revised_overall, delta_overall, | |
| improvements, regressions, next_recommendations, all_deltas | |
| """ | |
| orig_scores = original.sub_scores | |
| rev_scores = revised.sub_scores | |
| # Compute deltas | |
| all_deltas = {} | |
| for key in orig_scores: | |
| if key in rev_scores: | |
| all_deltas[key] = round(rev_scores[key] - orig_scores[key], 1) | |
| # Identify improvements (>5) and regressions (<-5) | |
| MEANINGFUL = 5.0 | |
| improvements = [(k, v) for k, v in all_deltas.items() if v > MEANINGFUL] | |
| regressions = [(k, v) for k, v in all_deltas.items() if v < -MEANINGFUL] | |
| # Sort by magnitude | |
| improvements.sort(key=lambda x: -x[1]) | |
| regressions.sort(key=lambda x: x[1]) | |
| # Next recommendations: weakest sub-scores in revised version | |
| # Exclude improvement_potential | |
| filtered_rev = {k: v for k, v in rev_scores.items() if k != "improvement_potential"} | |
| next_targets = sorted(filtered_rev.items(), key=lambda x: x[1])[:3] | |
| # Build next recommendations | |
| readable_names = { | |
| "concept_match": "Concept Match", | |
| "visual_focus": "Visual Focus", | |
| "readability": "Readability", | |
| "complexity_balance": "Complexity Balance", | |
| "communication_clarity": "Communication Clarity", | |
| "neural_richness": "Neural Richness", | |
| "memorability_proxy": "Memorability", | |
| } | |
| next_recommendations = [] | |
| for name, score in next_targets: | |
| readable = readable_names.get(name, name) | |
| next_recommendations.append({ | |
| "sub_score": name, | |
| "current_score": score, | |
| "message": f"Further improve {readable.lower()} (currently {score:.0f}/100) — this is your weakest remaining dimension." | |
| }) | |
| return { | |
| "original_overall": round(original.overall_score, 1), | |
| "revised_overall": round(revised.overall_score, 1), | |
| "delta_overall": round(revised.overall_score - original.overall_score, 1), | |
| "improvements": improvements[:5], | |
| "regressions": regressions[:5], | |
| "next_recommendations": next_recommendations, | |
| "all_deltas": all_deltas, | |
| } | |
| def format_comparison_result(result: Dict[str, Any]) -> str: | |
| """Format comparison result as human-readable text.""" | |
| lines = [] | |
| orig = result["original_overall"] | |
| rev = result["revised_overall"] | |
| delta = result["delta_overall"] | |
| if delta > 0: | |
| lines.append(f"📈 Overall improved: {orig:.0f} → {rev:.0f} (+{delta:.0f} points)") | |
| elif delta < 0: | |
| lines.append(f"📉 Overall decreased: {orig:.0f} → {rev:.0f} ({delta:.0f} points)") | |
| else: | |
| lines.append(f"➡️ Overall unchanged: {orig:.0f} → {rev:.0f}") | |
| if result["improvements"]: | |
| lines.append("\n✅ Improvements:") | |
| for name, val in result["improvements"]: | |
| lines.append(f" • {name}: +{val:.0f} points") | |
| if result["regressions"]: | |
| lines.append("\n⚠️ Regressions:") | |
| for name, val in result["regressions"]: | |
| lines.append(f" • {name}: {val:.0f} points") | |
| if result["next_recommendations"]: | |
| lines.append("\n📋 Next recommended edits:") | |
| for rec in result["next_recommendations"]: | |
| lines.append(f" • {rec['message']}") | |
| return "\n".join(lines) | |