Update app.py

app.py

@@ -1,9 +1,9 @@
 import gradio as gr
-from PIL import Image
+from PIL import Image # to open / display image
 import os
-import sqlite3
+import sqlite3 # DB for SQL operation, analytical db
 import google.generativeai as genai
-import time
+import time # 
 
 # Initialize Gemini
 gemini_key = os.getenv("gemini_key")
@@ -14,20 +14,28 @@ class SQLPromptModel:
     def __init__(self, database):
         # Initialize with database file path and create connection
         self.database = database
-        self.conn = sqlite3.connect(self.database)
+        self.conn = sqlite3.connect(self.database) # create connection to DB  
         
     def fetch_table_schema(self, table_name):
         # Get database table structure
+        # In order to execute SQL statements and fetch results from SQL queries, 
+        #we will need to use a database cursor. Call con.cursor() to create the Cursor:
         cursor = self.conn.cursor()
         # PRAGMA table_info returns: 
         # (id, name, type, notnull, default_value, primary_key)
         cursor.execute(f"PRAGMA table_info({table_name})")
         schema = cursor.fetchall()
-        return schema if schema else None
+        if schema:
+            return schema 
+        else
+            return None
+        #return schema if schema else None
 
     def text2sql_gemini(self, schema, user_prompt, inp_prompt=None):
         # Convert table columns to string format
         table_columns = ', '.join([f"{col[1]} {col[2]}" for col in schema])
+        #col[1] - column name/index 
+        #col[2] - column data type 
         
         # Create prompt for Gemini AI
         prompt = f"""Below are SQL table schemas paired with instructions that describe a task.