dataset_adapters/ed2b4cf199998dfb4690d6ae767d25dca1256ccd97729b257db3a37206a72969_bp.py

def transform_data(data):
    # Create the base structure for the transformed data
    transformed = {'conversations': []}

    # Check for system message type, if any, before human input and output
    system_msg = next((msg for msg in data.get('messages', []) if msg.get('role') == 'system'), None)
    input_msg = next((msg for msg in data.get('messages', []) if msg.get('role') == 'user'), None)
    output_msg = next((msg for msg in data.get('messages', []) if msg.get('role') == 'assistant'), None)

    # Include system message if present
    if system_msg:
        transformed['conversations'].append({'from': 'system', 'value': system_msg['content']})

    # Handle input and instruction
    if input_msg:
        transformed['conversations'].append({'from': 'human', 'value': input_msg['content']})

    # Include GPT message if present and after human input
    if output_msg:
        transformed['conversations'].append({'from': 'gpt', 'value': output_msg['content']})

    return transformed