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efdcedc | 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 | def final_score(
intent_score: float = 0,
emotion_score: float = 0,
cta_score: float = 0,
text_quality_score: float = 0,
similarity_score: float = None
) -> int:
"""
Compute final Ad Performance Score (0-100)
Scores are expected between 0 and 1 (or 0-100 if already scaled)
similarity_score is optional (0-100)
"""
# Scale 0-1 inputs to 0-100
intent_score = intent_score * 100 if intent_score <= 1 else intent_score
emotion_score = emotion_score * 100 if emotion_score <= 1 else emotion_score
cta_score = cta_score * 100 if cta_score <= 1 else cta_score
text_quality_score = text_quality_score * 100 if text_quality_score <= 1 else text_quality_score
# Weighted scoring (adjustable)
weights = {
"intent": 0.3,
"emotion": 0.25,
"cta": 0.2,
"text_quality": 0.15,
"similarity": 0.1
}
total_score = (
intent_score * weights["intent"] +
emotion_score * weights["emotion"] +
cta_score * weights["cta"] +
text_quality_score * weights["text_quality"]
)
# Include similarity if available
if similarity_score is not None:
total_score += similarity_score * weights["similarity"]
# Clamp between 0-100
total_score = max(min(total_score, 100), 0)
return round(total_score)
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