SQL_LLM_APP / app.py
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# Load all environment variables
import streamlit as st
import os
import sqlite3
import google.generativeai as genai
# Configure GenAI Key
genai.configure(api_key="AIzaSyA4jlt819TA84K9zr5EUroIQK83Rsx1A6E") # Use environment variable for API key
# Function to load Google Gemini Model and provide queries as responses
def get_gemini_response(question, prompt):
try:
model = genai.GenerativeModel('gemini-pro')
response = model.generate_content([prompt[0], question])
return response.text.strip() # Ensure no extra whitespace
except Exception as e:
st.error(f"Error generating response: {e}")
return ""
# Function to retrieve query 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 sqlite3.Error as e:
st.error(f"SQL error: {e}")
return []
# Define your prompt
prompt = [
"""
You are an expert in converting English questions to SQL query!
The SQL database has the name STUDENT and has the following columns - NAME, CLASS,
SECTION. \n\nFor example,\nExample 1 - How many entries of records are present?,
the SQL command will be something like this: SELECT COUNT(*) FROM STUDENT;
\nExample 2 - Tell me all the students studying in Data Science class?,
the SQL command will be something like this: SELECT * FROM STUDENT
WHERE CLASS="Data Science";
Note: The SQL code should not have ``` in beginning or end, and should not include the word "sql".
"""
]
# Streamlit App
st.set_page_config(page_title="SQL Query Retriever")
st.header("Gemini App To Retrieve SQL Data")
question = st.text_input("Input your question:", key="input")
if st.button("Ask the question"):
response = get_gemini_response(question, prompt)
if response:
st.write(f"Generated SQL Query: `{response}`")
result = read_sql_query(response, "students.db")
if result:
st.subheader("Query Results")
for row in result:
st.write(row)
else:
st.write("No results returned or an error occurred.")