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| import gradio as gr | |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM | |
| from datasets import load_dataset | |
| # Load the WikiSQL dataset | |
| wikisql_dataset = load_dataset("wikisql", split='train') # Load a subset of the dataset | |
| # Extract schema information from the WikiSQL dataset | |
| table_names = set() | |
| column_names = set() | |
| for item in wikisql_dataset: | |
| table_names.add(item['table']['name']) | |
| for column in item['table']['header']: | |
| column_names.add(column) | |
| # 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) | |
| # Post-process the SQL query to match the dataset's schema | |
| for table_name in table_names: | |
| if "TABLE" in sql_query: | |
| sql_query = sql_query.replace("TABLE", table_name) | |
| break # Assuming only one table is referenced in the query | |
| for column_name in column_names: | |
| if "COLUMN" in sql_query: | |
| sql_query = sql_query.replace("COLUMN", column_name, 1) | |
| return sql_query | |
| # 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="Generated SQL Query"), | |
| title="NL to SQL with T5 using WikiSQL Dataset", | |
| description="This model generates an SQL query for your natural language input based on the WikiSQL dataset." | |
| ) | |
| # Launch the app | |
| if __name__ == "__main__": | |
| interface.launch() | |