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import gradio as gr |
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import spacy |
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from transformers import pipeline |
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nlp = spacy.load('es_core_news_sm') |
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text_generator = pipeline('text-generation', model='gpt2') |
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pos_tags = ['ADJ', 'ADP', 'ADV', 'AUX', 'CONJ', 'DET', 'INTJ', 'NOUN', 'NUM', 'PART', 'PRON', 'PROPN', 'PUNCT', 'SCONJ', 'SYM', 'VERB', 'X'] |
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def generate_sentence(): |
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result = text_generator('')[0] |
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sentence = result['generated_text'] |
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tagged_words = analyze_sentence(sentence) |
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return sentence, [word for word, _ in tagged_words] |
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def analyze_sentence(sentence): |
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doc = nlp(sentence) |
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return [(token.text, token.pos_) for token in doc] |
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def check_answer(sentence, *args): |
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tagged_words = analyze_sentence(sentence) |
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correct_answer = [tag for word, tag in tagged_words] |
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user_answer = list(args) |
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if user_answer == correct_answer: |
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return 'Correcto!' |
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else: |
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return 'Incorrecto. La respuesta correcta es: ' + str(correct_answer) |
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generate_iface = gr.Interface(fn=generate_sentence, |
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inputs='number', |
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outputs=['textbox', 'textbox']) |
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check_iface = gr.Interface(fn=check_answer, |
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inputs=['textbox'] + [gr.inputs.Dropdown(choices=pos_tags) for _ in range(10)], |
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outputs='textbox') |
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generate_iface.launch() |
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check_iface.launch() |
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