Update app.py

app.py

@@ -1,70 +1,72 @@
-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")
-
+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")
+