| import gradio as gr | |
| import spacy | |
| from transformers import pipeline | |
| nlp = spacy.load('es_core_news_sm') | |
| text_generator = pipeline('text-generation', model='gpt2') | |
| def generate_sentence(): | |
| result = text_generator('')[0] | |
| sentence = result['generated_text'] | |
| return sentence | |
| def analyze_sentence(sentence): | |
| doc = nlp(sentence) | |
| tagged_words = [(token.text, token.pos_) for token in doc] | |
| return tagged_words | |
| def check_answer(sentence, answer): | |
| tagged_words = analyze_sentence(sentence) | |
| correct_answer = [tag for word, tag in tagged_words] | |
| if answer == correct_answer: | |
| return 'Correcto!' | |
| else: | |
| return 'Incorrecto. La respuesta correcta es: ' + str(correct_answer) | |
| sentence = generate_sentence() | |
| iface = gr.Interface(fn=check_answer, inputs=['text', 'list'], outputs='text') | |
| iface.launch() | |