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Update app.py
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app.py
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import gradio as gr
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from transformers import T5Tokenizer, T5ForConditionalGeneration
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from deep_translator import GoogleTranslator
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import torch
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# Limitar el uso de CPU para servidores lentos
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torch.set_num_threads(1)
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torch.set_num_interop_threads(1)
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# Cargar modelo
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tokenizer = T5Tokenizer.from_pretrained("cssupport/t5-small-awesome-text-to-sql")
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model = T5ForConditionalGeneration.from_pretrained("cssupport/t5-small-awesome-text-to-sql")
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Database schema:
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Table bodegas(Id,
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Table
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"""
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# Función principal
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def generar_sql(pregunta_espanol):
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# Interfaz Gradio
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iface = gr.Interface(
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import gradio as gr
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from transformers import T5Tokenizer, T5ForConditionalGeneration
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import torch
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# Limitar el uso de CPU para servidores lentos
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torch.set_num_threads(1)
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torch.set_num_interop_threads(1)
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# Cargar modelo
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tokenizer = T5Tokenizer.from_pretrained("cssupport/t5-small-awesome-text-to-sql")
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model = T5ForConditionalGeneration.from_pretrained("cssupport/t5-small-awesome-text-to-sql")
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# Esquema de base de datos simplificado
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SCHEMA = """
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Database schema:
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Table bodegas(Id, Nombre, Encargado, Telefono, Email, Direccion, Horario, Regional, Latitud, Longitud)
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Table maestra(CodigoSap, Descripcion, Grupo, Agrupador, Marca, Parte, Operacion, Componente)
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"""
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# Función principal
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def generar_sql(pregunta_espanol):
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try:
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# Crear prompt claro y directo
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prompt = f"Esquema de base de datos:\n{SCHEMA}\nPregunta en español: {pregunta_espanol}\nGenera la consulta SQL correspondiente."
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# Generar la consulta SQL
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input_ids = tokenizer(prompt, return_tensors="pt").input_ids
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output = model.generate(input_ids, max_length=128)
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sql = tokenizer.decode(output[0], skip_special_tokens=True)
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return sql
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except Exception as e:
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return f"Error: {str(e)}"
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# Interfaz Gradio
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iface = gr.Interface(
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fn=generar_sql,
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inputs=gr.Textbox(lines=3, label="Pregunta en español"),
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outputs=gr.Textbox(label="Consulta SQL generada"),
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title="Texto a SQL (entrada en español)",
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description="Escribe una pregunta en español sobre la base de datos y obtén la consulta SQL."
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)
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iface.launch()
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