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import gradio as gr | |
from transformers import T5Tokenizer, T5ForConditionalGeneration | |
from deep_translator import GoogleTranslator | |
import torch | |
# Optimizaci贸n para entornos limitados | |
torch.set_num_threads(1) | |
torch.set_num_interop_threads(1) | |
# Modelo p煤blico sin token | |
tokenizer = T5Tokenizer.from_pretrained("mrm8488/t5-base-finetuned-wikiSQL") | |
model = T5ForConditionalGeneration.from_pretrained("mrm8488/t5-base-finetuned-wikiSQL") | |
# Funci贸n para generar SQL | |
def generar_sql(pregunta_espanol): | |
try: | |
pregunta_ingles = GoogleTranslator(source="es", target="en").translate(pregunta_espanol) | |
prompt = f"translate English to SQL: {pregunta_ingles}" | |
input_ids = tokenizer(prompt, return_tensors="pt").input_ids | |
output = model.generate(input_ids, max_length=128) | |
sql = tokenizer.decode(output[0], skip_special_tokens=True) | |
return sql | |
except Exception as e: | |
return f"Error: {str(e)}" | |
# Interfaz Gradio | |
iface = gr.Interface( | |
fn=generar_sql, | |
inputs=gr.Textbox(lines=3, label="Pregunta en espa帽ol"), | |
outputs=gr.Textbox(label="Consulta SQL generada"), | |
title="Texto a SQL (con T5 de Hugging Face)", | |
description="Convierte preguntas en espa帽ol a SQL con un modelo entrenado en WikiSQL." | |
) | |
iface.launch() | |