| import json | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| import torch | |
| # Define the schema for the database | |
| db_schema = { | |
| "products": ["product_id", "name", "price", "description", "type"], | |
| "orders": ["order_id", "product_id", "quantity", "order_date"], | |
| "customers": ["customer_id", "name", "email", "phone_number"] | |
| } | |
| def dummy_function(schema_description, user_question): | |
| print(user_question) | |
| # Schema as a context for the model | |
| schema_description = json.dumps(db_schema, indent=4) | |
| # Example interactive questions | |
| print("Ask a question about the database schema.") | |
| while True: | |
| user_question = input("Question: ") | |
| if user_question.lower() in ["exit", "quit"]: | |
| print("Exiting...") | |
| break | |
| # Generate SQL query | |
| sql_query = dummy_function(schema_description, user_question) | |
| print(f"Generated SQL Query:\n{sql_query}\n") | |