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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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sentence_state = {'sentence': '', 'tagged_words': []} |
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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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sentence_state['sentence'] = sentence |
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sentence_state['tagged_words'] = tagged_words |
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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(*args): |
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correct_answer = [tag for word, tag in sentence_state['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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def game_flow(): |
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sentence, words = generate_sentence() |
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answer = check_answer(*gr.inputs) |
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return sentence, words, answer |
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iface = gr.Interface(fn=game_flow, |
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inputs=[gr.inputs.Button(label='Generate Sentence')] + |
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[gr.inputs.Dropdown(choices=pos_tags, label=f'Word {i+1}') for i in range(len(sentence_state['tagged_words']))], |
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outputs=[gr.outputs.Textbox(label='Sentence'), |
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gr.outputs.Textbox(label='Words'), |
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gr.outputs.Textbox(label='Result')]) |
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iface.launch() |
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