app.py

import pandas as pd
from transformers import pipeline

# Example data
data = {
    'term': [
        'Atmospheric Chemistry',
        'Organic Chemistry',
        'Business Ethics',
        'Corporate Social Responsibility'
    ]
}
df = pd.DataFrame(data)

# Load the zero-shot classification pipeline
classifier = pipeline('zero-shot-classification', model='facebook/bart-large-mnli')

# Define your candidate labels
candidate_labels = ['Discipline', 'Subdiscipline']

# Function to classify term and recommend discipline
def classify_term(term):
    result = classifier(term, candidate_labels)
    label = result['labels'][0]  # Get the highest scoring label
    return label

# Classify all terms
df['classification'] = df['term'].apply(classify_term)

# Example mapping of subdisciplines to disciplines
subdiscipline_to_discipline = {
    'Atmospheric Chemistry': 'Atmospheric Science',
    'Organic Chemistry': 'Chemistry',
    'Corporate Social Responsibility': 'Business Ethics'
    # Add your mappings here
}

def recommend_discipline(term, classification):
    if classification == 'Subdiscipline':
        return subdiscipline_to_discipline.get(term, 'Unknown Discipline')
    else:
        return term

df['recommended_discipline'] = df.apply(lambda x: recommend_discipline(x['term'], x['classification']), axis=1)

# Display the results
print(df[['term', 'classification', 'recommended_discipline']])