app.py

import gradio as gr
import spacy
from transformers import pipeline

# Load the Spanish language model
nlp = spacy.load('es_core_news_sm')

# Load the text generation pipeline
text_generator = pipeline('text-generation', model='gpt2')

def generate_sentence():
    # Generate a sentence
    try:
        result = text_generator('', max_length=50)[0]
        sentence = result['generated_text']
        return sentence
    except Exception as e:
        return str(e)

def analyze_sentence(sentence):
    # Analyze the sentence
    doc = nlp(sentence)
    tagged_words = [(token.text, token.pos_) for token in doc]
    return tagged_words

def check_answer(sentence, answer):
    # Check the user's 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)

# Define the Gradio interface
iface = gr.Interface(
    fn={
        "generate_sentence": generate_sentence,
        "check_answer": check_answer
    }, 
    inputs={
        "sentence": "text", 
        "answer": "list"
    }, 
    outputs="text"
)

# Launch the interface
iface.launch()