## 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']

sentence_state = {'sentence': '', 'tagged_words': []}

def generate_sentence():
    result = text_generator('')[0]
    sentence = result['generated_text']
    tagged_words = analyze_sentence(sentence)
    sentence_state['sentence'] = sentence
    sentence_state['tagged_words'] = tagged_words
    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(*args):
    correct_answer = [tag for word, tag in sentence_state['tagged_words']]
    user_answer = list(args)
    if user_answer == correct_answer:
        return 'Correcto!'
    else:
        return 'Incorrecto. La respuesta correcta es: ' + str(correct_answer)

def game_flow():
    sentence, words = generate_sentence()
    answer = check_answer(*gr.inputs)
    return sentence, words, answer

iface = gr.Interface(fn=game_flow, 
                     inputs=[gr.inputs.Button(label='Generate Sentence')] + 
                            [gr.inputs.Dropdown(choices=pos_tags, label=f'Word {i+1}') for i in range(len(sentence_state['tagged_words']))],
                     outputs=[gr.outputs.Textbox(label='Sentence'),
                              gr.outputs.Textbox(label='Words'),
                              gr.outputs.Textbox(label='Result')])
iface.launch()