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import gradio as gr | |
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM | |
from datasets import load_dataset | |
# Load the Spider dataset | |
spider_dataset = load_dataset("spider", split='train') # Load a subset of the dataset | |
# Load tokenizer and model | |
tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-wikiSQL") | |
model = AutoModelForSeq2SeqLM.from_pretrained("mrm8488/t5-base-finetuned-wikiSQL") | |
def generate_sql_from_user_input(query): | |
# Generate SQL for the user's query | |
input_text = "translate English to SQL: " + query | |
inputs = tokenizer(input_text, return_tensors="pt", padding=True) | |
outputs = model.generate(**inputs, max_length=512) | |
sql_query = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
return sql_query | |
def find_matching_sql(nl_query): | |
# Find the matching SQL query from the Spider dataset | |
for item in spider_dataset: | |
if item['question'].lower() == nl_query.lower(): | |
return item['query'] | |
return "No matching SQL query found in the Spider dataset." | |
# Create a Gradio interface | |
interface = gr.Interface( | |
fn=lambda query: { | |
"Generated SQL Query": generate_sql_from_user_input(query), | |
"Matching SQL Query from Spider Dataset": find_matching_sql(query) | |
}, | |
inputs=gr.Textbox(label="Enter your natural language query"), | |
outputs=[gr.Textbox(label="Generated SQL Query"), gr.Textbox(label="Matching SQL Query from Spider Dataset")], | |
title="NL to SQL with T5 using Spider Dataset", | |
description="This model generates an SQL query for your natural language input and finds a matching SQL query from the Spider dataset." | |
) | |
# Launch the app | |
if __name__ == "__main__": | |
interface.launch() | |