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from dotenv import load_dotenv
import streamlit as st
import os
import sqlite3
import google.generativeai as genai
# Load environment variables
load_dotenv()
# Configure Gemini API
# genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
API_KEY = st.secrets["GOOGLE_API_KEY"]
# Function to load Gemini model and generate SQL query
def get_gemini_response(question, prompt):
model = genai.GenerativeModel('gemini-pro')
full_prompt = prompt + "\n\nUser Query: " + question # Better structuring
response = model.generate_content(full_prompt)
sql_query = response.text.strip() # Clean the response
return sql_query
# Function to retrieve query results from the database
def read_sql_query(sql, db):
try:
conn = sqlite3.connect(db)
cur = conn.cursor()
cur.execute(sql)
rows = cur.fetchall()
conn.close()
return rows
except Exception as e:
return [("Error:", str(e))] # Return error message if query fails
# Define prompt
prompt = """
You are an expert in SQL query generation. Your task is to convert natural language questions into valid SQL queries based on the given database schema.
Instructions:
- The SQL database schema will be provided.
- Generate a syntactically correct SQL query based on the input question.
- The SQL query should be optimized and free from unnecessary clauses.
- Do not include SQL keywords or formatting like triple backticks (```) in the response.
- If the question is ambiguous, generate the most probable SQL query.
Example:
Input: "How many students are in the database?"
Output: SELECT COUNT(*) FROM STUDENT_INFO;
Input: "List all students in CLASS 10 section A."
Output: SELECT * FROM STUDENT_INFO WHERE CLASS = '10' AND SECTION = 'A';
Input: "Show the names of students in Data Science Section."
Output: SELECT NAME FROM STUDENT_INFO WHERE SECTION = 'Data Science';
"""
# Streamlit App
st.set_page_config(page_title="SQL Query Generator")
st.header("Gemini App To Retrieve SQL Data")
question = st.text_input("Enter your question:", key="input")
submit = st.button("Generate SQL Query")
# If submit is clicked
if submit:
sql_query = get_gemini_response(question, prompt)
st.subheader("Generated SQL Query")
st.code(sql_query, language="sql") # Show SQL query
response = read_sql_query(sql_query, "student.db")
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