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ec4da21 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 | from __future__ import annotations
from typing import Any, Dict, List, Tuple
from catalog_text_templates import (
RECOMMENDATION_TEMPLATES,
STRENGTH_TEMPLATES,
VERDICT_LEVELS,
WEAKNESS_TEMPLATES,
)
def clamp(v: float, lo: float, hi: float) -> float:
return max(lo, min(hi, v))
def pick_top_unique(items: List[str], limit: int = 3) -> List[str]:
out: List[str] = []
seen = set()
for item in items:
if item and item not in seen:
out.append(item)
seen.add(item)
if len(out) >= limit:
break
return out
def build_strength_keys(
official_scores: Dict[str, float],
diagnostic_metrics: Dict[str, int],
) -> List[str]:
keys: List[str] = []
if official_scores.get("composition", 0.0) >= 4.2:
keys.append("strong_composition")
if diagnostic_metrics.get("hierarchy_strength", 0) >= 4:
keys.append("clear_hierarchy")
if diagnostic_metrics.get("focal_clarity", 0) >= 4:
keys.append("clear_focal_point")
if diagnostic_metrics.get("palette_control", 0) >= 4:
keys.append("controlled_palette")
if diagnostic_metrics.get("visual_impact", 0) >= 4:
keys.append("strong_impact")
if diagnostic_metrics.get("technical_quality", 0) >= 4:
keys.append("good_technical_quality")
if diagnostic_metrics.get("visual_clutter", 0) <= 2:
keys.append("clean_presentation")
if official_scores.get("typography", 0.0) >= 4.0:
keys.append("readable_typography")
if official_scores.get("message_clarity", 0.0) >= 4.0:
keys.append("good_message_clarity")
return pick_top_unique(keys, 3)
def build_weakness_keys(
official_scores: Dict[str, float],
diagnostic_metrics: Dict[str, int],
) -> List[str]:
keys: List[str] = []
if official_scores.get("composition", 0.0) <= 2.8:
keys.append("weak_composition")
if diagnostic_metrics.get("hierarchy_strength", 0) <= 2:
keys.append("weak_hierarchy")
if diagnostic_metrics.get("focal_clarity", 0) <= 2:
keys.append("weak_focal_point")
if diagnostic_metrics.get("palette_control", 0) <= 2:
keys.append("weak_palette_control")
if diagnostic_metrics.get("visual_impact", 0) <= 2:
keys.append("low_visual_impact")
if diagnostic_metrics.get("technical_quality", 0) <= 2:
keys.append("low_technical_quality")
if diagnostic_metrics.get("visual_clutter", 0) >= 4:
keys.append("high_clutter")
if official_scores.get("typography", 0.0) <= 3.0:
keys.append("weak_typography")
if official_scores.get("message_clarity", 0.0) <= 3.0:
keys.append("weak_message_clarity")
return pick_top_unique(keys, 3)
def build_recommendation_keys(
official_scores: Dict[str, float],
diagnostic_metrics: Dict[str, int],
pins: List[str],
) -> List[str]:
keys: List[str] = []
if diagnostic_metrics.get("hierarchy_strength", 0) <= 3:
keys.append("improve_hierarchy")
if diagnostic_metrics.get("visual_clutter", 0) >= 4:
keys.append("reduce_clutter")
if diagnostic_metrics.get("focal_clarity", 0) <= 3:
keys.append("improve_focal_point")
if official_scores.get("typography", 0.0) <= 3.3:
keys.append("improve_typography")
if official_scores.get("color", 0.0) <= 3.2:
keys.append("improve_palette")
if official_scores.get("message_clarity", 0.0) <= 3.5:
keys.append("improve_message_clarity")
if official_scores.get("quality", 0.0) <= 3.3:
keys.append("improve_technical_quality")
if "credits_box" in pins or "small_footer_info" in pins:
keys.append("improve_footer_block")
return pick_top_unique(keys, 3)
def render_texts_from_keys(
keys: List[str],
template_map: Dict[str, str],
) -> List[str]:
return [template_map[k] for k in keys if k in template_map]
def compute_verdict_level(
primary_label: str,
primary_confidence: str,
diagnostic_score: float,
) -> str:
if primary_label == "good" and diagnostic_score >= 4.3:
return "strong"
if primary_label == "good" and diagnostic_score >= 3.5:
return "good"
if primary_label == "uncertain":
return "uncertain"
if primary_label == "bad" and diagnostic_score <= 2.7:
return "weak"
return "mixed"
def build_verdict_summary(
*,
verdict_level: str,
primary_label: str,
diagnostic_score: float,
tags: List[str],
strengths: List[str],
weaknesses: List[str],
) -> str:
level_text = VERDICT_LEVELS.get(verdict_level, "Смешанный")
tone_bits: List[str] = []
if "minimal" in tags:
tone_bits.append("минималистичной подачей")
if "clean" in tags:
tone_bits.append("чистой подачей")
if "cinematic" in tags:
tone_bits.append("атмосферной подачей")
if "editorial" in tags:
tone_bits.append("редакционной структурой")
tone_text = ""
if tone_bits:
tone_text = " с " + ", ".join(tone_bits[:2])
if primary_label == "good":
return f"{level_text} постер{tone_text}, который в целом производит положительное визуальное впечатление."
if primary_label == "bad":
return f"{level_text} постер, которому пока не хватает ясности и собранности восприятия."
return f"{level_text} постер: в нем есть сильные стороны, но общий сигнал пока неоднозначен."
def build_verdict_takeaway(
strengths: List[str],
weaknesses: List[str],
recommendations: List[str],
) -> str:
if recommendations:
first = recommendations[0]
if first.endswith("."):
return first
return first + "."
if weaknesses:
return "Основной резерв роста связан с улучшением читаемости и структуры."
if strengths:
return "Текущую визуальную логику стоит сохранить и доработать точечно."
return "Нужна дополнительная проверка композиции, ясности сообщения и визуальной иерархии."
def build_structured_report(
*,
official_scores: Dict[str, float],
diagnostic_metrics: Dict[str, int],
tags: List[str],
pins: List[str],
primary_label: str,
primary_confidence: str,
diagnostic_score: float,
) -> Dict[str, Any]:
strength_keys = build_strength_keys(official_scores, diagnostic_metrics)
weakness_keys = build_weakness_keys(official_scores, diagnostic_metrics)
recommendation_keys = build_recommendation_keys(official_scores, diagnostic_metrics, pins)
strengths = render_texts_from_keys(strength_keys, STRENGTH_TEMPLATES)
weaknesses = render_texts_from_keys(weakness_keys, WEAKNESS_TEMPLATES)
recommendations = render_texts_from_keys(recommendation_keys, RECOMMENDATION_TEMPLATES)
verdict_level = compute_verdict_level(
primary_label=primary_label,
primary_confidence=primary_confidence,
diagnostic_score=diagnostic_score,
)
verdict_summary = build_verdict_summary(
verdict_level=verdict_level,
primary_label=primary_label,
diagnostic_score=diagnostic_score,
tags=tags,
strengths=strengths,
weaknesses=weaknesses,
)
verdict_takeaway = build_verdict_takeaway(
strengths=strengths,
weaknesses=weaknesses,
recommendations=recommendations,
)
return {
"tags": tags,
"pins": pins,
"strengths": strengths,
"weaknesses": weaknesses,
"recommendations": recommendations,
"verdict": {
"level": verdict_level,
"summary": verdict_summary,
"takeaway": verdict_takeaway,
},
"debug_keys": {
"strength_keys": strength_keys,
"weakness_keys": weakness_keys,
"recommendation_keys": recommendation_keys,
},
} |