Delete sql_model.py

sql_model.py

@@ -1,111 +0,0 @@
-# from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-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)
-        self.tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b-it")
-        self.model = AutoModelForCausalLM.from_pretrained("google/gemma-7b-it",)
-
-    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