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()