import nlu

examples = [
    {
        "text": "icecream is cold",
        "parsed": ("exists-property-with-value", 'icecream', 'cold'),
    },
    {
        "text": "lava is dangerous",
        "parsed": ("exists-property-with-value", 'lava', 'dangerous')
    },
    {
        "text": "earth is a planet",
        "parsed": ("pertenence-to-group", 'earth', 'planet'),
    },
    {
        "text": "Green is a color",
        "parsed": ("pertenence-to-group", 'green', 'color'),
    },
    {
        "text": "a plane can fly",
        "parsed": ("has-capacity", 'plane', 'fly')
    },
    {
        "text": "a wale can swim",
        "parsed": ("has-capacity", 'wale', 'swim')
    }
]

base_knowledge = {
    'icecream': {
        "groups": ['noun', 'object', 'comestible', 'sweet'],
    },
    'lava': {
        "groups": ['noun', 'object'],
    },
    'earth': {
        "groups": ['noun', 'object', 'planet'],
    },
    'green': {
        "groups": ['noun', 'color', 'concept'],
    },
    'plane': {
        "groups": ['noun', 'object', 'vehicle', 'fast'],
    },
    'car': {
        "groups": ['noun', 'object', 'vehicle', 'slow-ish'],
    },
    'wale': {
        "groups": ['noun', 'object', 'living-being']
    },
    'cold': {
        "groups": ['property', 'temperature'],
    },
    'dangerous': {
        "groups": ['property'],
    },
    'planet': {
        "groups": ['noun', 'group'],
    },
    'color': {
        "groups": ['property', 'group'],
    },
    'fly': {
        "groups": ['verb'],
    },
    'swim': {
        "groups": ['verb'],
    },
}


def main():
    knowledge = nlu.KnowledgeBase(
        examples=[],
        trained=[],
        knowledge=base_knowledge
    )

    knowledge = nlu.train(knowledge, examples)
    for test in [{'text': 'a bus can run'}, {'text': 'io is a moon'}]:
        row = test['text'].lower().split()
        fit = nlu.get_fit(knowledge, row)
        print(test['text'], fit)


if __name__ == '__main__':
    main()