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import torch
from transformers import T5Tokenizer, T5ForConditionalGeneration
import gradio as gr
tokenizer = T5Tokenizer.from_pretrained('t5-small')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = T5ForConditionalGeneration.from_pretrained('cssupport/t5-small-awesome-text-to-sql')
model = model.to(device)
model.eval()
def generate_sql(input_prompt):
inputs = tokenizer(input_prompt, padding=True, truncation=True, return_tensors="pt").to(device)
with torch.no_grad():
outputs = model.generate(**inputs, max_length=512)
generated_sql = tokenizer.decode(outputs[0], skip_special_tokens=True)
return generated_sql
def gradio_interface(tables, query):
input_prompt = f"tables:\n{tables}\nquery for:{query}"
return generate_sql(input_prompt)
iface = gr.Interface(
fn=gradio_interface,
inputs=[
gr.Textbox(lines=5, label="Context Tables", placeholder="Enter table definitions here..."),
gr.Textbox(lines=2, label="Query Description", placeholder="Enter your SQL query here...")
],
outputs=gr.Textbox(label="Generated SQL Query", placeholder=""),
title="Text to SQL Generator",
examples=[
["CREATE TABLE student_course_attendance (student_id VARCHAR); CREATE TABLE students (student_id VARCHAR);", "List the id of students who never attends courses?"]
]
)
iface.launch()
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