jPdZddlmZmZmZdeeeefdefdZdededefdZy ) u Confidence scorer for AI categorization predictions. Computes a confidence_score (0.00–100.00) from a set of weighted evidence signals and derives a confidence_level relative to the configured threshold. )ListDictAnyevidence_signalsreturnc |sytd|D}|dkrytd|D}||z dz}tdtdt|d}|S)u Compute a confidence score from a list of evidence signals. Each signal must have a 'weight' (0.0–1.0) and a 'signal_strength' (0.0–1.0) that indicates how strongly the signal supports the prediction. Returns a score in the range [10.00, 100.00]. The floor of 10 is applied when no usable evidence exists so predictions are never scored at zero. Args: evidence_signals: List of dicts, each with keys: - weight (float): relative importance of this signal (0.0–1.0) - signal_strength (float): how strongly it supports the category (0.0–1.0) Returns: float: confidence score in [10.00, 100.00] g$@c3@K|]}|jddyw)weightNget.0ss :/var/www/html/ai_cats/python/services/confidence_scorer.py z compute_score.. sFquuXs+Fsrc3fK|])}|jdd|jddz+yw)r r signal_strengthNr rs rrz compute_score..$s4  hquu%6<<s/1gY@)summaxminround)r total_weight weighted_sum raw_scorescores r compute_scorer sm$ F5EFFLq!L  ,5I c%y!!45 6E Lr thresholdc||k\ry|dk\ryy)aM Derive a confidence level label from a numeric score. Args: score: Confidence score in [0, 100]. threshold: The configured auto-approval threshold (e.g., 90.0). Returns: 'high' if score >= threshold 'medium' if score >= 70 and score < threshold 'low' if score < 70 highgQ@mediumlow)rr s r derive_levelr&.s  } rN) __doc__typingrrrstrfloatrr&r%rrr+sM#" Dc3h$8 U F%Cr