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import gradio as gr
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from datasets import load_dataset
from difflib import get_close_matches
# Load the WikiSQL dataset
wikisql_dataset = load_dataset("wikisql", split='train[:100]') # 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 find_closest_match(query, dataset):
questions = [item['question'] for item in dataset]
matches = get_close_matches(query, questions, n=1)
return matches[0] if matches else None
def generate_sql_from_user_input(query):
# Find the closest match in the dataset
matched_query = find_closest_match(query, wikisql_dataset)
if not matched_query:
return "No close match found in the dataset.", ""
# Find the corresponding SQL query in the dataset
for item in wikisql_dataset:
if item['question'] == matched_query:
return matched_query, item['sql']['human_readable']
return "Match found, but corresponding SQL query not found in dataset.", ""
# Create a Gradio interface
interface = gr.Interface(
fn=generate_sql_from_user_input,
inputs=gr.Textbox(label="Enter your natural language query"),
outputs=[gr.Textbox(label="Matched Query from Dataset"), gr.Textbox(label="Corresponding SQL Query from Dataset")],
title="NL to SQL with T5 using WikiSQL Dataset",
description="This model finds the closest match in the WikiSQL dataset for your query and returns the corresponding SQL query from the dataset."
)
# Launch the app
if __name__ == "__main__":
interface.launch()
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