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']
    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)

def process_form(input_dict):
    answer = [input_dict[word] for word in sorted(input_dict.keys())]
    return check_answer(sentence, answer)

sentence = generate_sentence()
tagged_words = analyze_sentence(sentence)
inputs = {word: gr.inputs.Dropdown(choices=pos_tags) for word, tag in tagged_words}
inputs['submit'] = gr.inputs.Button(label='Submit')
outputs = gr.outputs.Textbox()

iface = gr.Interface(fn=process_form, inputs=inputs, outputs=outputs)
iface.launch()