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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 post_process_sql_query(sql_query, db_schema): | |
| # Modify the SQL query to match the dataset's schema | |
| # This is just an example and might need to be adapted based on the dataset and model output | |
| for table_name in db_schema['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 db_schema['column_names']: | |
| if "COLUMN" in sql_query: | |
| sql_query = sql_query.replace("COLUMN", column_name[1], 1) | |
| return sql_query | |
| def generate_sql_from_user_input(query, db_id): | |
| # Find the corresponding database schema | |
| db_schema = None | |
| for item in spider_dataset: | |
| if item['db_id'] == db_id: | |
| db_schema = item | |
| break | |
| if db_schema is None: | |
| return "Database schema not found for the given DB ID." | |
| # 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 | |
| sql_query = post_process_sql_query(sql_query, db_schema) | |
| 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"), gr.Textbox(label="Enter DB ID")], | |
| outputs=gr.Textbox(label="Generated SQL Query"), | |
| title="NL to SQL with T5 using Spider Dataset", | |
| description="This model generates an SQL query for your natural language input based on the Spider dataset." | |
| ) | |
| # Launch the app | |
| if __name__ == "__main__": | |
| interface.launch() | |