from transformers import pipeline


def classifyA(text):
    """
    Extracts labels and scores from the input data,
    maps the labels using the provided mapping dictionary,
    and returns a list of formatted label-score strings.
    """
    from transformers import pipeline
    classification = pipeline(task="text-classification", model="Hashuz/AS_MentalQAU", return_all_scores=True)
    result = []

    mapping = {
        'info': 'تقديم معلومة',
        'guid': 'توجيه أو ارشاد',
        'support': 'دعم نفسي'
    }
    output = classification(text)
    for item in output[0]:  
        label = item['label']
        label = mapping.get(label)
        score = item['score']
        if score > 0.5:
            result.append(label)
    return ', '.join(result)


def classifyQ(text):
    """
    Extracts labels and scores from the input data,
    maps the labels using the provided mapping dictionary,
    and returns a list of formatted label-score strings.
    """
    from transformers import pipeline
    classification = pipeline(task="text-classification", model="Hashuz/QT_MentalQA", return_all_scores=True)
    result = []
    
    mapping = {
        'diagnosis': 'فحص',
        'treatment': 'علاج',
        'anatomy': 'التشريح',
        'epidemiology': 'الأوبئة',
        'lifestyle': 'نمط الحياة',
        'provider': 'مقدم الخدمة',
        'other': 'غير محدد'
    }
    output = classification(text)
    for item in output[0]: 
        label = item['label']
        label = mapping.get(label)
        score = item['score']
        if score > 0.5:
            result.append(label)
    return ', '.join(result)