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from dotenv import load_dotenv
load_dotenv() # Load all the environment variables
import streamlit as st
import os
import sqlite3
import google.generativeai as genai
# Configure Genai Key
genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
# Function to load Google Gemini Model and provide queries as response
def get_gemini_response(question, prompt):
model = genai.GenerativeModel('gemini-pro')
response = model.generate_content([prompt[0], question])
return response.text
# Function to retrieve query from the database
def read_sql_query(sql, db):
conn = sqlite3.connect(db)
cur = conn.cursor()
cur.execute(sql)
rows = cur.fetchall()
conn.commit()
conn.close()
return rows
# Define Your Prompt
prompt = [
"""
You are an expert in converting English questions to SQL query!
The SQL database has the name COMPANY and has the following columns - FIRST_NAME, LAST_NAME, DEPT,
SALARY, AGE \n\nFor example,\nExample 1 - How many entries of records are present?,
the SQL command will be something like this SELECT COUNT(*) FROM COMPANY ;
\nExample 2 - Tell me all the students studying in Data Science class?,
the SQL command will be something like this SELECT * FROM COMPANY
where DEPT="Data Science";
also the sql code should not have ``` in beginning or end and sql word in output
"""
]
# Streamlit App
st.set_page_config(page_title="Retrieve Any SQL query")
st.header("App To Generate SQL & Data")
question = st.text_input("Input: ", key="input")
submit = st.button("Ask the question")
# if submit is clicked
if submit:
response = get_gemini_response(question, prompt)
st.subheader("Generated SQL Query")
st.code(response, language='sql')
try:
data = read_sql_query(response, "COMPANY.db")
st.subheader("The Response is")
for row in data:
st.write(row)
except sqlite3.Error as e:
st.error(f"Error: {e}")
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