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import gradio as gr
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
from datasets import load_dataset
# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-wikiSQL")
model = AutoModelForSeq2SeqLM.from_pretrained("mrm8488/t5-base-finetuned-wikiSQL")
# Initialize the pipeline
nl2sql_pipeline = pipeline("text2text-generation", model=model, tokenizer=tokenizer)
# Load a part of the Spider dataset
spider_dataset = load_dataset("spider", split='train[:5]')
def generate_sql(query):
# Format the input for the model
input_text = f"translate English to SQL: {query}"
# Run the pipeline
results = nl2sql_pipeline(input_text)
# Extract the SQL query
sql_query = results[0]['generated_text']
return sql_query
# Use examples from the Spider dataset
example_questions = [(question['question'],) for question in spider_dataset]
# Create a Gradio interface
interface = gr.Interface(
fn=generate_sql,
inputs=gr.Textbox(lines=2, placeholder="Enter your natural language query here..."),
outputs="text",
examples=example_questions,
title="NL to SQL with T5",
description="This model converts natural language queries into SQL. Try one of the example questions or enter your own!"
)
# Launch the app
if __name__ == "__main__":
interface.launch()
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