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.env

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+GOOGLE_API_KEY = "AIzaSyBZhxdQ0BFLAeHAp9LOc2nqJCgddXq89rU"

requirements.txt

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+streamlit
+google-generativeai 
+python-dotenv

sql.py

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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"))
+
+# 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")
+

sqlite.py

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+import sqlite3
+
+# Establish a connection to SQLite
+connection=sqlite3.connect("student.db")
+
+# Create a cursor object to insert record, create table 
+cursor=connection.cursor()
+
+## Create the table 
+table_info = """
+CREATE TABLE STUDENT_INFO(NAME VARCHAR(25), CLASS VARCHAR(25),
+SECTION VARCHAR(25));
+"""
+
+cursor.execute(table_info)
+
+# Insert Some more records 
+cursor.execute('''Insert Into STUDENT_INFO values('Ashish', 'Data Scientist', 'A')''')
+cursor.execute('''Insert Into STUDENT_INFO values('Devesh', 'Software Developer', 'A')''')
+cursor.execute('''Insert Into STUDENT_INFO values('Tanuja', 'Cyber Security', 'A')''')
+cursor.execute('''Insert Into STUDENT_INFO values('Hardeep', 'UX Design', 'A')''')
+cursor.execute('''Insert Into STUDENT_INFO values('Rahul', 'Data Analyst', 'A')''')
+cursor.execute('''Insert Into STUDENT_INFO values('Pawan', 'Data Scientist', 'A')''')
+cursor.execute('''Insert Into STUDENT_INFO values('Neeraj', 'Backend Developer', 'A')''')
+cursor.execute('''Insert Into STUDENT_INFO values('Neeraja', 'Data Scientist', 'A')''')
+
+
+# Display All the records 
+print("The Inserted records are")
+data=cursor.execute('''Select * from STUDENT_INFO''')
+for row in data:
+  print(row)
+
+# Commit your changes in the Database 
+connection.commit()
+connection.close()