scoring.py

def compute_quality_score(issues):
    """
    Compute the weighted quality score based on detected issues.
    Logic is identical to your original implementation.
    """

    # Count severities
    critical = sum(1 for _, sev, _ in issues if sev == "CRITICAL")
    high = sum(1 for _, sev, _ in issues if sev == "HIGH")
    medium = sum(1 for _, sev, _ in issues if sev == "MEDIUM")
    low = sum(1 for _, sev, _ in issues if sev == "LOW")

    # ============================================
    # Weighted scoring 
    # ============================================

    score = 100
    score -= critical * 35
    score -= high * 20
    score -= medium * 8
    score -= low * 3

    # Heavy processing penalties
    if len(issues) >= 6:
        score -= 10

    if (critical + high) >= 3:
        score -= 10

    # Clean bonus
    if len(issues) == 0:
        score += 5

    score = max(0, min(score, 100))

    # ============================================
    # Grade Mapping
    # ============================================

    if score >= 90:
        grade = "A"
        quality = "EXCELLENT"
        color = "#00C853"
        recommendation = "Excellent for TTS dataset"

    elif score >= 75:
        grade = "B"
        quality = "GOOD"
        color = "#64DD17"
        recommendation = "Very good quality; suitable for TTS"

    elif score >= 60:
        grade = "C"
        quality = "FAIR"
        color = "#FFD600"
        recommendation = "Usable but may contain processing artifacts"

    elif score >= 40:
        grade = "D"
        quality = "POOR"
        color = "#FF6D00"
        recommendation = "Not recommended for TTS (heavy processing)"

    else:
        grade = "F"
        quality = "CRITICAL"
        color = "#D50000"
        recommendation = "Severely degraded or processed; avoid for TTS"

    # ============================================
    # Cleanliness + Processing Index
    # ============================================

    cleanliness_score = max(0, 100 - (medium * 5 + low * 3))
    processing_severity = (critical * 3) + (high * 2) + medium

    return {
        "score": score,
        "grade": grade,
        "quality": quality,
        "color": color,
        "recommendation": recommendation,
        "cleanliness_score": cleanliness_score,
        "processing_severity": processing_severity,
        "critical": critical,
        "high": high,
        "medium": medium,
        "low": low
    }