import gradio as gr
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM



# Model in Hugging Hub
tokenizer = AutoTokenizer.from_pretrained("Andresmfs/st5s-es-inclusivo")
model = AutoModelForSeq2SeqLM.from_pretrained("Andresmfs/st5s-es-inclusivo")

def make_neutral(phrase):
    # Define prompt for converting gendered text to neutral
    input_ids = tokenizer(phrase, return_tensors="pt").input_ids

    # Call the LLM to generate neutral text
    outputs = model.generate(input_ids)

    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Ejemplos de preguntas
mis_ejemplos = [
    ["La cocina de los gallegos es fabulosa."],
    ["Los niños juegan a la pelota."],
    ["Los científicos son muy listos"],
    ["Las enfermeras se esforzaron mucho durante la pandemia."],
    
]

    
iface = gr.Interface(
    fn=make_neutral,
    inputs="text",
    outputs="text",
    title="ES Inclusive Language",
    description="Enter a Spanish phrase and get it converted into neutral/inclusive form.",
    examples = mis_ejemplos
)
iface.launch()