nlp/sql_model.py

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
# import os
# from transformers import AutoTokenizer, AutoModelForCausalLM

class SQLModel:
    def __init__(self, model_name="google/flan-t5-base"):
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        self.model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
        # hf_token = os.environ.get("HF_HUB_TOKEN")
        # self.tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it",use_auth_token=hf_token)
        # self.model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-it",use_auth_token=hf_token)

    def generate_sql(self, natural_language_query):
        input_text = f"""You are a highly skilled SQL translator. Your task is to convert natural language descriptions of data queries into correct and optimized SQL statements.
        Here is the schema information for our database :

        Table: Employees
        - id (INT)
        - NAME (VARCHAR)
        - Department (VARCHAR)
        - Salary (INT)
        - Hire_Date (DATE)

        Table: Departments
        - ID (INT)
        - Name (VARCHAR)
        - Manager (VARCHAR)

        Here are a few examples:

        1. **Input**: "Show me all employees in the Sales department."
        **Output**:

                SELECT *
                FROM Employees
                WHERE Department = 'Sales';

        2. **Input**: "Who is the manager of the Engineering department?"
        **Output**:

                SELECT Manager
                FROM Departments
                WHERE Name = 'Engineering';


        3. **Input**: "List all employees hired after 2021-01-01."
        **Output**:

                SELECT *
                FROM Employees
                WHERE Hire_Date > '2021-01-01';


        4. **Input**: "What is the total salary expense for the Marketing department?"
        **Output**:

                SELECT SUM(Salary)
                FROM Employees
                WHERE Department = 'Marketing';


        5. **Input**: "Find the average salary of employees in each department."
        **Output**:

                SELECT Department, AVG(Salary) AS average_salary
                FROM Employees
                GROUP BY Department;

        Please do not return additional text besides query.

        Please only answer queries which makes sense for the given schema. Else just return - "No information found"

        Now, translate the following natural language query into an syntactically correct SQL query:
        **Input**: {natural_language_query}
        **Output**:

"""
#         input_text = f"""
# Translate the following natural language query into a syntactically correct SQL query using the provided database schema. Output only the SQL query with no additional text or explanation.

# Database Schema:

# Table: Employees
# - id (INT)
# - NAME (VARCHAR)
# - Department (VARCHAR)
# - Salary (INT)
# - Hire_Date (DATE)

# Table: Departments
# - ID (INT)
# - Name (VARCHAR)
# - Manager (VARCHAR)

# Examples:
# 1. Natural Language Query: "List all employees who were hired after '2020-01-01'."
#    Output: SELECT * FROM Employees WHERE Hire_Date > '2020-01-01';

# 2. Natural Language Query: "Retrieve the names and salaries of employees in the 'Sales' department."
#    Output: SELECT NAME, Salary FROM Employees WHERE Department = 'Sales';

# Now, translate the following query:
# {natural_language_query}
# """
        # input_text = f"translate English to SQL: {natural_language_query}"
        # inputs = self.tokenizer(input_text, return_tensors="pt").input_ids
        # outputs = self.model.generate(inputs, max_new_tokens=100, do_sample=False)
        # sql_query = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
        inputs = self.tokenizer(input_text, return_tensors="pt")
        outputs = self.model.generate(**inputs)
        sql_query = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
        print(sql_query)
        return sql_query