| 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() | |