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