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import gradio as gr
from transformers import T5Tokenizer, T5ForConditionalGeneration
from datasets import load_dataset
# Load the tokenizer and model
tokenizer = T5Tokenizer.from_pretrained('t5-small')
model = T5ForConditionalGeneration.from_pretrained('t5-small')
def generate_sql(question):
# Format the question for the model if needed. For example:
# input_text = f"translate English to SQL: {question}"
input_text = f"{question}" # Directly use the question if the model is fine-tuned for SQL generation
# Tokenize the input text
input_ids = tokenizer.encode(input_text, return_tensors="pt")
# Generate the output sequence
output_ids = model.generate(input_ids, max_length=512, num_beams=5)[0]
# Decode the generated ids to get the SQL query
sql_query = tokenizer.decode(output_ids, skip_special_tokens=True)
return sql_query
# Define the Gradio interface
iface = gr.Interface(
fn=generate_sql,
inputs=gr.Textbox(lines=2, placeholder="Enter your question here..."),
outputs=gr.Textbox(),
title="Natural Language to SQL",
description="This app uses a Seq2Seq model to generate SQL queries from natural language questions."
)
# Launch the app
if __name__ == "__main__":
iface.launch()
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