Update app.py

app.py

@@ -4,6 +4,7 @@
 # - Students practice SELECT, WHERE, JOINs (INNER/LEFT), aliases, views, CTAS/SELECT INTO.
 # - Validator enforces columns only when the prompt asks; otherwise focuses on rows.
 # - ERD shows all FK edges in light gray and dynamically HIGHLIGHTS edges implied by JOINs.
+# - Domain picker now round-robins across: bookstore, retail sales, wholesaler, sales tax, oil & gas wells, marketing.
 
 import os
 import re
@@ -232,140 +233,686 @@ def init_progress_tables(con: sqlite3.Connection):
 
 init_progress_tables(CONN)
 
-# -------------------- Fallback dataset & questions --------------------
-FALLBACK_SCHEMA = {
-    "domain": "bookstore",
-    "tables": [
-        {
-            "name": "authors",
-            "pk": ["author_id"],
-            "columns": [
-                {"name":"author_id","type":"INTEGER"},
-                {"name":"name","type":"TEXT"},
-                {"name":"country","type":"TEXT"},
-                {"name":"birth_year","type":"INTEGER"},
-            ],
-            "fks": [],
-            "rows": [
-                {"author_id":1,"name":"Isaac Asimov","country":"USA","birth_year":1920},
-                {"author_id":2,"name":"Ursula K. Le Guin","country":"USA","birth_year":1929},
-                {"author_id":3,"name":"Haruki Murakami","country":"Japan","birth_year":1949},
-                {"author_id":4,"name":"Chinua Achebe","country":"Nigeria","birth_year":1930},
-                {"author_id":5,"name":"Jane Austen","country":"UK","birth_year":1775},
-                {"author_id":6,"name":"J.K. Rowling","country":"UK","birth_year":1965},
-                {"author_id":7,"name":"Yuval Noah Harari","country":"Israel","birth_year":1976},
-                {"author_id":8,"name":"New Author","country":"Nowhere","birth_year":1990},
-            ],
-        },
-        {
-            "name": "bookstores",
-            "pk": ["store_id"],
-            "columns": [
-                {"name":"store_id","type":"INTEGER"},
-                {"name":"name","type":"TEXT"},
-                {"name":"city","type":"TEXT"},
-                {"name":"state","type":"TEXT"},
-            ],
-            "fks": [],
-            "rows": [
-                {"store_id":1,"name":"Downtown Books","city":"Oklahoma City","state":"OK"},
-                {"store_id":2,"name":"Harbor Books","city":"Seattle","state":"WA"},
-                {"store_id":3,"name":"Desert Pages","city":"Phoenix","state":"AZ"},
-            ],
-        },
-        {
-            "name": "books",
-            "pk": ["book_id"],
-            "columns": [
-                {"name":"book_id","type":"INTEGER"},
-                {"name":"title","type":"TEXT"},
-                {"name":"author_id","type":"INTEGER"},
-                {"name":"store_id","type":"INTEGER"},
-                {"name":"category","type":"TEXT"},
-                {"name":"price","type":"REAL"},
-                {"name":"published_year","type":"INTEGER"},
-            ],
-            "fks": [
-                {"columns":["author_id"],"ref_table":"authors","ref_columns":["author_id"]},
-                {"columns":["store_id"],"ref_table":"bookstores","ref_columns":["store_id"]},
-            ],
-            "rows": [
-                {"book_id":101,"title":"Foundation","author_id":1,"store_id":1,"category":"Sci-Fi","price":14.99,"published_year":1951},
-                {"book_id":102,"title":"I, Robot","author_id":1,"store_id":1,"category":"Sci-Fi","price":12.50,"published_year":1950},
-                {"book_id":103,"title":"The Left Hand of Darkness","author_id":2,"store_id":2,"category":"Sci-Fi","price":16.00,"published_year":1969},
-                {"book_id":104,"title":"A Wizard of Earthsea","author_id":2,"store_id":2,"category":"Fantasy","price":11.50,"published_year":1968},
-                {"book_id":105,"title":"Norwegian Wood","author_id":3,"store_id":3,"category":"Fiction","price":18.00,"published_year":1987},
-                {"book_id":106,"title":"Kafka on the Shore","author_id":3,"store_id":1,"category":"Fiction","price":21.00,"published_year":2002},
-                {"book_id":107,"title":"Things Fall Apart","author_id":4,"store_id":1,"category":"Fiction","price":10.00,"published_year":1958},
-                {"book_id":108,"title":"Pride and Prejudice","author_id":5,"store_id":2,"category":"Fiction","price":9.00,"published_year":1813},
-                {"book_id":109,"title":"Harry Potter and the Sorcerer's Stone","author_id":6,"store_id":3,"category":"Children","price":22.00,"published_year":1997},
-                {"book_id":110,"title":"Harry Potter and the Chamber of Secrets","author_id":6,"store_id":3,"category":"Children","price":23.00,"published_year":1998},
-                {"book_id":111,"title":"Sapiens","author_id":7,"store_id":1,"category":"History","price":26.00,"published_year":2011},
-                {"book_id":112,"title":"Homo Deus","author_id":7,"store_id":2,"category":"History","price":28.00,"published_year":2015},
-            ],
-        },
+# -------------------- Built-in fallback domain packs --------------------
+# Each pack: {"schema": {...}, "questions": [...]}
+FALLBACK_PACKS: Dict[str, Dict[str, Any]] = {}
+
+# --- Bookstore (existing) ---
+FALLBACK_PACKS["bookstore"] = {
+    "schema": {
+        "domain": "bookstore",
+        "tables": [
+            {
+                "name": "authors",
+                "pk": ["author_id"],
+                "columns": [
+                    {"name":"author_id","type":"INTEGER"},
+                    {"name":"name","type":"TEXT"},
+                    {"name":"country","type":"TEXT"},
+                    {"name":"birth_year","type":"INTEGER"},
+                ],
+                "fks": [],
+                "rows": [
+                    {"author_id":1,"name":"Isaac Asimov","country":"USA","birth_year":1920},
+                    {"author_id":2,"name":"Ursula K. Le Guin","country":"USA","birth_year":1929},
+                    {"author_id":3,"name":"Haruki Murakami","country":"Japan","birth_year":1949},
+                    {"author_id":4,"name":"Chinua Achebe","country":"Nigeria","birth_year":1930},
+                    {"author_id":5,"name":"Jane Austen","country":"UK","birth_year":1775},
+                    {"author_id":6,"name":"J.K. Rowling","country":"UK","birth_year":1965},
+                    {"author_id":7,"name":"Yuval Noah Harari","country":"Israel","birth_year":1976},
+                    {"author_id":8,"name":"New Author","country":"Nowhere","birth_year":1990},
+                ],
+            },
+            {
+                "name": "bookstores",
+                "pk": ["store_id"],
+                "columns": [
+                    {"name":"store_id","type":"INTEGER"},
+                    {"name":"name","type":"TEXT"},
+                    {"name":"city","type":"TEXT"},
+                    {"name":"state","type":"TEXT"},
+                ],
+                "fks": [],
+                "rows": [
+                    {"store_id":1,"name":"Downtown Books","city":"Oklahoma City","state":"OK"},
+                    {"store_id":2,"name":"Harbor Books","city":"Seattle","state":"WA"},
+                    {"store_id":3,"name":"Desert Pages","city":"Phoenix","state":"AZ"},
+                ],
+            },
+            {
+                "name": "books",
+                "pk": ["book_id"],
+                "columns": [
+                    {"name":"book_id","type":"INTEGER"},
+                    {"name":"title","type":"TEXT"},
+                    {"name":"author_id","type":"INTEGER"},
+                    {"name":"store_id","type":"INTEGER"},
+                    {"name":"category","type":"TEXT"},
+                    {"name":"price","type":"REAL"},
+                    {"name":"published_year","type":"INTEGER"},
+                ],
+                "fks": [
+                    {"columns":["author_id"],"ref_table":"authors","ref_columns":["author_id"]},
+                    {"columns":["store_id"],"ref_table":"bookstores","ref_columns":["store_id"]},
+                ],
+                "rows": [
+                    {"book_id":101,"title":"Foundation","author_id":1,"store_id":1,"category":"Sci-Fi","price":14.99,"published_year":1951},
+                    {"book_id":102,"title":"I, Robot","author_id":1,"store_id":1,"category":"Sci-Fi","price":12.50,"published_year":1950},
+                    {"book_id":103,"title":"The Left Hand of Darkness","author_id":2,"store_id":2,"category":"Sci-Fi","price":16.00,"published_year":1969},
+                    {"book_id":104,"title":"A Wizard of Earthsea","author_id":2,"store_id":2,"category":"Fantasy","price":11.50,"published_year":1968},
+                    {"book_id":105,"title":"Norwegian Wood","author_id":3,"store_id":3,"category":"Fiction","price":18.00,"published_year":1987},
+                    {"book_id":106,"title":"Kafka on the Shore","author_id":3,"store_id":1,"category":"Fiction","price":21.00,"published_year":2002},
+                    {"book_id":107,"title":"Things Fall Apart","author_id":4,"store_id":1,"category":"Fiction","price":10.00,"published_year":1958},
+                    {"book_id":108,"title":"Pride and Prejudice","author_id":5,"store_id":2,"category":"Fiction","price":9.00,"published_year":1813},
+                    {"book_id":109,"title":"Harry Potter and the Sorcerer's Stone","author_id":6,"store_id":3,"category":"Children","price":22.00,"published_year":1997},
+                    {"book_id":110,"title":"Harry Potter and the Chamber of Secrets","author_id":6,"store_id":3,"category":"Children","price":23.00,"published_year":1998},
+                    {"book_id":111,"title":"Sapiens","author_id":7,"store_id":1,"category":"History","price":26.00,"published_year":2011},
+                    {"book_id":112,"title":"Homo Deus","author_id":7,"store_id":2,"category":"History","price":28.00,"published_year":2015},
+                ],
+            },
+        ]
+    },
+    "questions": [
+        {"id":"B_Q01","category":"SELECT *","difficulty":1,
+         "prompt_md":"Select all rows and columns from `authors`.",
+         "answer_sql":["SELECT * FROM authors;"]},
+        {"id":"B_Q02","category":"SELECT columns","difficulty":1,
+         "prompt_md":"Show `title` and `price` from `books`.",
+         "answer_sql":["SELECT title, price FROM books;"]},
+        {"id":"B_Q03","category":"WHERE","difficulty":1,
+         "prompt_md":"List Sci‑Fi books under $15 (show title, price).",
+         "answer_sql":["SELECT title, price FROM books WHERE category='Sci-Fi' AND price < 15;"]},
+        {"id":"B_Q04","category":"Aliases","difficulty":1,
+         "prompt_md":"Using aliases `b` and `a`, join `books` to `authors` and show `b.title` and `a.name` as `author_name`.",
+         "answer_sql":["SELECT b.title, a.name AS author_name FROM books b JOIN authors a ON b.author_id=a.author_id;"],
+         "requires_aliases":True,"required_aliases":["a","b"]},
+        {"id":"B_Q05","category":"JOIN (INNER)","difficulty":2,
+         "prompt_md":"Inner join `books` and `bookstores`. Return `title`, `name` as `store`.",
+         "answer_sql":["SELECT b.title, s.name AS store FROM books b INNER JOIN bookstores s ON b.store_id=s.store_id;"]},
+        {"id":"B_Q06","category":"JOIN (LEFT)","difficulty":2,
+         "prompt_md":"List each author and their number of books (include authors with zero): columns `name`, `book_count`.",
+         "answer_sql":["SELECT a.name, COUNT(b.book_id) AS book_count FROM authors a LEFT JOIN books b ON a.author_id=b.author_id GROUP BY a.name;"]},
+        {"id":"B_Q07","category":"VIEW","difficulty":2,
+         "prompt_md":"Create a view `vw_pricy` with `title`, `price` for books priced > 25.",
+         "answer_sql":["CREATE VIEW vw_pricy AS SELECT title, price FROM books WHERE price > 25;"]},
+        {"id":"B_Q08","category":"CTAS / SELECT INTO","difficulty":2,
+         "prompt_md":"Create a table `cheap_books` containing books priced < 12. Use CTAS or SELECT INTO.",
+         "answer_sql":[
+             "CREATE TABLE cheap_books AS SELECT * FROM books WHERE price < 12;",
+             "SELECT * INTO cheap_books FROM books WHERE price < 12;"
+         ]},
     ]
 }
 
-FALLBACK_QUESTIONS = [
-    {"id":"Q01","category":"SELECT *","difficulty":1,
-     "prompt_md":"Select all rows and columns from `authors`.",
-     "answer_sql":["SELECT * FROM authors;"],
-     "requires_aliases":False,"required_aliases":[]},
-    {"id":"Q02","category":"SELECT columns","difficulty":1,
-     "prompt_md":"Show `title` and `price` from `books`.",
-     "answer_sql":["SELECT title, price FROM books;"],
-     "requires_aliases":False,"required_aliases":[]},
-    {"id":"Q03","category":"WHERE","difficulty":1,
-     "prompt_md":"List Sci‑Fi books under $15 (show title, price).",
-     "answer_sql":["SELECT title, price FROM books WHERE category='Sci-Fi' AND price < 15;"],
-     "requires_aliases":False,"required_aliases":[]},
-    {"id":"Q04","category":"Aliases","difficulty":1,
-     "prompt_md":"Using aliases `b` and `a`, join `books` to `authors` and show `b.title` and `a.name` as `author_name`.",
-     "answer_sql":["SELECT b.title, a.name AS author_name FROM books b JOIN authors a ON b.author_id=a.author_id;"],
-     "requires_aliases":True,"required_aliases":["a","b"]},
-    {"id":"Q05","category":"JOIN (INNER)","difficulty":2,
-     "prompt_md":"Inner join `books` and `bookstores`. Return `title`, `name` as `store`.",
-     "answer_sql":["SELECT b.title, s.name AS store FROM books b INNER JOIN bookstores s ON b.store_id=s.store_id;"],
-     "requires_aliases":False,"required_aliases":[]},
-    {"id":"Q06","category":"JOIN (LEFT)","difficulty":2,
-     "prompt_md":"List each author and their number of books (include authors with zero): columns `name`, `book_count`.",
-     "answer_sql":["SELECT a.name, COUNT(b.book_id) AS book_count FROM authors a LEFT JOIN books b ON a.author_id=b.author_id GROUP BY a.name;"],
-     "requires_aliases":False,"required_aliases":[]},
-    {"id":"Q07","category":"VIEW","difficulty":2,
-     "prompt_md":"Create a view `vw_pricy` with `title`, `price` for books priced > 25.",
-     "answer_sql":["CREATE VIEW vw_pricy AS SELECT title, price FROM books WHERE price > 25;"],
-     "requires_aliases":False,"required_aliases":[]},
-    {"id":"Q08","category":"CTAS / SELECT INTO","difficulty":2,
-     "prompt_md":"Create a table `cheap_books` containing books priced < 12. Use CTAS or SELECT INTO.",
-     "answer_sql":[
-         "CREATE TABLE cheap_books AS SELECT * FROM books WHERE price < 12;",
-         "SELECT * INTO cheap_books FROM books WHERE price < 12;"
-     ],
-     "requires_aliases":False,"required_aliases":[]},
-]
+# --- Retail sales ---
+FALLBACK_PACKS["retail sales"] = {
+    "schema": {
+        "domain": "retail sales",
+        "tables": [
+            {
+                "name":"customers","pk":["customer_id"],
+                "columns":[
+                    {"name":"customer_id","type":"INTEGER"},
+                    {"name":"name","type":"TEXT"},
+                    {"name":"city","type":"TEXT"},
+                    {"name":"state","type":"TEXT"}
+                ],
+                "fks":[],
+                "rows":[
+                    {"customer_id":1,"name":"Ava Reed","city":"Seattle","state":"WA"},
+                    {"customer_id":2,"name":"Mason Ortiz","city":"Portland","state":"OR"},
+                    {"customer_id":3,"name":"Noah Patel","city":"Phoenix","state":"AZ"},
+                    {"customer_id":4,"name":"Emma Kim","city":"San Diego","state":"CA"},
+                    {"customer_id":5,"name":"Olivia Park","city":"Dallas","state":"TX"},
+                    {"customer_id":6,"name":"Liam Gray","city":"Denver","state":"CO"},
+                    {"customer_id":7,"name":"Sophia Lee","city":"Boston","state":"MA"},
+                    {"customer_id":8,"name":"Elijah Hall","city":"Miami","state":"FL"}
+                ]
+            },
+            {
+                "name":"products","pk":["product_id"],
+                "columns":[
+                    {"name":"product_id","type":"INTEGER"},
+                    {"name":"product_name","type":"TEXT"},
+                    {"name":"category","type":"TEXT"},
+                    {"name":"price","type":"REAL"}
+                ],
+                "fks":[],
+                "rows":[
+                    {"product_id":101,"product_name":"Coffee Maker","category":"Home","price":49.99},
+                    {"product_id":102,"product_name":"Electric Kettle","category":"Home","price":29.99},
+                    {"product_id":103,"product_name":"Headphones","category":"Electronics","price":79.00},
+                    {"product_id":104,"product_name":"USB-C Cable","category":"Electronics","price":9.99},
+                    {"product_id":105,"product_name":"Notebook","category":"Stationery","price":3.49},
+                    {"product_id":106,"product_name":"Desk Lamp","category":"Home","price":19.99},
+                    {"product_id":107,"product_name":"T-Shirt","category":"Clothing","price":15.00},
+                    {"product_id":108,"product_name":"Sneakers","category":"Clothing","price":65.00}
+                ]
+            },
+            {
+                "name":"orders","pk":["order_id"],
+                "columns":[
+                    {"name":"order_id","type":"INTEGER"},
+                    {"name":"customer_id","type":"INTEGER"},
+                    {"name":"order_date","type":"TEXT"}
+                ],
+                "fks":[{"columns":["customer_id"],"ref_table":"customers","ref_columns":["customer_id"]}],
+                "rows":[
+                    {"order_id":201,"customer_id":1,"order_date":"2024-01-05"},
+                    {"order_id":202,"customer_id":2,"order_date":"2024-01-07"},
+                    {"order_id":203,"customer_id":1,"order_date":"2024-01-12"},
+                    {"order_id":204,"customer_id":3,"order_date":"2024-02-01"},
+                    {"order_id":205,"customer_id":4,"order_date":"2024-02-10"},
+                    {"order_id":206,"customer_id":5,"order_date":"2024-03-02"},
+                    {"order_id":207,"customer_id":6,"order_date":"2024-03-03"},
+                    {"order_id":208,"customer_id":7,"order_date":"2024-03-09"},
+                    {"order_id":209,"customer_id":8,"order_date":"2024-03-15"},
+                    {"order_id":210,"customer_id":3,"order_date":"2024-03-20"}
+                ]
+            },
+            {
+                "name":"order_items","pk":["order_id","product_id"],
+                "columns":[
+                    {"name":"order_id","type":"INTEGER"},
+                    {"name":"product_id","type":"INTEGER"},
+                    {"name":"qty","type":"INTEGER"},
+                    {"name":"unit_price","type":"REAL"}
+                ],
+                "fks":[
+                    {"columns":["order_id"],"ref_table":"orders","ref_columns":["order_id"]},
+                    {"columns":["product_id"],"ref_table":"products","ref_columns":["product_id"]}
+                ],
+                "rows":[
+                    {"order_id":201,"product_id":101,"qty":1,"unit_price":49.99},
+                    {"order_id":201,"product_id":104,"qty":2,"unit_price":9.99},
+                    {"order_id":202,"product_id":107,"qty":3,"unit_price":15.00},
+                    {"order_id":203,"product_id":103,"qty":1,"unit_price":79.00},
+                    {"order_id":203,"product_id":105,"qty":5,"unit_price":3.49},
+                    {"order_id":204,"product_id":102,"qty":2,"unit_price":29.99},
+                    {"order_id":205,"product_id":108,"qty":1,"unit_price":65.00},
+                    {"order_id":206,"product_id":106,"qty":2,"unit_price":19.99},
+                    {"order_id":207,"product_id":104,"qty":4,"unit_price":9.99},
+                    {"order_id":208,"product_id":101,"qty":1,"unit_price":49.99},
+                    {"order_id":209,"product_id":107,"qty":2,"unit_price":15.00},
+                    {"order_id":210,"product_id":103,"qty":1,"unit_price":79.00}
+                ]
+            }
+        ]
+    },
+    "questions":[
+        {"id":"RS_Q01","category":"SELECT *","difficulty":1,
+         "prompt_md":"Show everything from `customers`.",
+         "answer_sql":["SELECT * FROM customers;"]},
+        {"id":"RS_Q02","category":"SELECT columns","difficulty":1,
+         "prompt_md":"List product name and price from `products`.",
+         "answer_sql":["SELECT product_name, price FROM products;"]},
+        {"id":"RS_Q03","category":"WHERE","difficulty":1,
+         "prompt_md":"Orders placed in March 2024 (return `order_id`, `order_date`).",
+         "answer_sql":["SELECT order_id, order_date FROM orders WHERE order_date BETWEEN '2024-03-01' AND '2024-03-31';"]},
+        {"id":"RS_Q04","category":"Aliases","difficulty":1,
+         "prompt_md":"Join `orders` (alias `o`) with `customers` (alias `c`) and show `o.order_id`, `c.name`.",
+         "answer_sql":["SELECT o.order_id, c.name FROM orders o JOIN customers c ON o.customer_id=c.customer_id;"],
+         "requires_aliases":True,"required_aliases":["o","c"]},
+        {"id":"RS_Q05","category":"JOIN (INNER)","difficulty":2,
+         "prompt_md":"Inner join `order_items` with `products` to show items where qty ≥ 3. Return `product_name`, `qty`.",
+         "answer_sql":["SELECT p.product_name, oi.qty FROM order_items oi INNER JOIN products p ON oi.product_id=p.product_id WHERE oi.qty >= 3;"]},
+        {"id":"RS_Q06","category":"JOIN (LEFT)","difficulty":2,
+         "prompt_md":"Customers and their number of orders (include zero). Columns: `name`, `order_count`.",
+         "answer_sql":["SELECT c.name, COUNT(o.order_id) AS order_count FROM customers c LEFT JOIN orders o ON c.customer_id=o.customer_id GROUP BY c.name;"]},
+        {"id":"RS_Q07","category":"VIEW","difficulty":2,
+         "prompt_md":"Create view `vw_top_qty` with total quantity by product: `product_id`, `total_qty`.",
+         "answer_sql":["CREATE VIEW vw_top_qty AS SELECT product_id, SUM(qty) AS total_qty FROM order_items GROUP BY product_id;"]},
+        {"id":"RS_Q08","category":"CTAS / SELECT INTO","difficulty":2,
+         "prompt_md":"Create table `cheap_products` with products priced < 10.",
+         "answer_sql":[
+             "CREATE TABLE cheap_products AS SELECT * FROM products WHERE price < 10;",
+             "SELECT * INTO cheap_products FROM products WHERE price < 10;"
+         ]}
+    ]
+}
+
+# --- Wholesaler ---
+FALLBACK_PACKS["wholesaler"] = {
+    "schema":{
+        "domain":"wholesaler",
+        "tables":[
+            {"name":"suppliers","pk":["supplier_id"],
+             "columns":[
+                 {"name":"supplier_id","type":"INTEGER"},
+                 {"name":"supplier_name","type":"TEXT"},
+                 {"name":"country","type":"TEXT"}
+             ],
+             "fks":[],
+             "rows":[
+                 {"supplier_id":1,"supplier_name":"Nordic Foods","country":"SE"},
+                 {"supplier_id":2,"supplier_name":"Metro Trade","country":"DE"},
+                 {"supplier_id":3,"supplier_name":"Pacific Imports","country":"US"},
+                 {"supplier_id":4,"supplier_name":"Andes Supply","country":"CL"},
+                 {"supplier_id":5,"supplier_name":"Sahara Wholesale","country":"MA"}
+             ]},
+            {"name":"items","pk":["item_id"],
+             "columns":[
+                 {"name":"item_id","type":"INTEGER"},
+                 {"name":"item_name","type":"TEXT"},
+                 {"name":"unit_cost","type":"REAL"}
+             ],
+             "fks":[],
+             "rows":[
+                 {"item_id":101,"item_name":"Olive Oil 1L","unit_cost":4.20},
+                 {"item_id":102,"item_name":"Canned Tuna","unit_cost":1.10},
+                 {"item_id":103,"item_name":"Basmati Rice 5kg","unit_cost":6.30},
+                 {"item_id":104,"item_name":"Black Tea 200g","unit_cost":2.70},
+                 {"item_id":105,"item_name":"Peanut Butter","unit_cost":3.00},
+                 {"item_id":106,"item_name":"Tomato Paste","unit_cost":0.95},
+                 {"item_id":107,"item_name":"Chickpeas 1kg","unit_cost":1.60},
+                 {"item_id":108,"item_name":"Soy Sauce 500ml","unit_cost":2.10}
+             ]},
+            {"name":"purchase_orders","pk":["po_id"],
+             "columns":[
+                 {"name":"po_id","type":"INTEGER"},
+                 {"name":"supplier_id","type":"INTEGER"},
+                 {"name":"po_date","type":"TEXT"}
+             ],
+             "fks":[{"columns":["supplier_id"],"ref_table":"suppliers","ref_columns":["supplier_id"]}],
+             "rows":[
+                 {"po_id":201,"supplier_id":1,"po_date":"2024-01-10"},
+                 {"po_id":202,"supplier_id":2,"po_date":"2024-01-18"},
+                 {"po_id":203,"supplier_id":3,"po_date":"2024-02-05"},
+                 {"po_id":204,"supplier_id":1,"po_date":"2024-02-22"},
+                 {"po_id":205,"supplier_id":5,"po_date":"2024-03-01"},
+                 {"po_id":206,"supplier_id":4,"po_date":"2024-03-07"}
+             ]},
+            {"name":"po_lines","pk":["po_id","item_id"],
+             "columns":[
+                 {"name":"po_id","type":"INTEGER"},
+                 {"name":"item_id","type":"INTEGER"},
+                 {"name":"qty","type":"INTEGER"},
+                 {"name":"line_cost","type":"REAL"}
+             ],
+             "fks":[
+                 {"columns":["po_id"],"ref_table":"purchase_orders","ref_columns":["po_id"]},
+                 {"columns":["item_id"],"ref_table":"items","ref_columns":["item_id"]}
+             ],
+             "rows":[
+                 {"po_id":201,"item_id":101,"qty":200,"line_cost":840.0},
+                 {"po_id":201,"item_id":106,"qty":500,"line_cost":475.0},
+                 {"po_id":202,"item_id":103,"qty":120,"line_cost":756.0},
+                 {"po_id":203,"item_id":102,"qty":600,"line_cost":660.0},
+                 {"po_id":203,"item_id":104,"qty":150,"line_cost":405.0},
+                 {"po_id":204,"item_id":105,"qty":180,"line_cost":540.0},
+                 {"po_id":205,"item_id":107,"qty":300,"line_cost":480.0},
+                 {"po_id":206,"item_id":108,"qty":250,"line_cost":525.0}
+             ]}
+        ]
+    },
+    "questions":[
+        {"id":"W_Q01","category":"SELECT *","difficulty":1,
+         "prompt_md":"Show all suppliers.",
+         "answer_sql":["SELECT * FROM suppliers;"]},
+        {"id":"W_Q02","category":"SELECT columns","difficulty":1,
+         "prompt_md":"Return `item_name` and `unit_cost` from `items`.",
+         "answer_sql":["SELECT item_name, unit_cost FROM items;"]},
+        {"id":"W_Q03","category":"WHERE","difficulty":1,
+         "prompt_md":"Items costing more than 3.00 (show name, cost).",
+         "answer_sql":["SELECT item_name, unit_cost FROM items WHERE unit_cost > 3.00;"]},
+        {"id":"W_Q04","category":"Aliases","difficulty":1,
+         "prompt_md":"Using aliases `p` and `s`, show each `po_id` with `supplier_name`.",
+         "answer_sql":["SELECT p.po_id, s.supplier_name FROM purchase_orders p JOIN suppliers s ON p.supplier_id=s.supplier_id;"],
+         "requires_aliases":True,"required_aliases":["p","s"]},
+        {"id":"W_Q05","category":"JOIN (INNER)","difficulty":2,
+         "prompt_md":"Inner join `po_lines` and `items`; list `item_name`, total qty per item.",
+         "answer_sql":["SELECT i.item_name, SUM(l.qty) AS total_qty FROM po_lines l INNER JOIN items i ON l.item_id=i.item_id GROUP BY i.item_name;"]},
+        {"id":"W_Q06","category":"JOIN (LEFT)","difficulty":2,
+         "prompt_md":"Suppliers and count of POs (include those with zero). Columns `supplier_name`, `po_count`.",
+         "answer_sql":["SELECT s.supplier_name, COUNT(p.po_id) AS po_count FROM suppliers s LEFT JOIN purchase_orders p ON s.supplier_id=p.supplier_id GROUP BY s.supplier_name;"]},
+        {"id":"W_Q07","category":"VIEW","difficulty":2,
+         "prompt_md":"Create view `vw_po_value` with `po_id` and total `line_cost` per PO.",
+         "answer_sql":["CREATE VIEW vw_po_value AS SELECT po_id, SUM(line_cost) AS po_value FROM po_lines GROUP BY po_id;"]},
+        {"id":"W_Q08","category":"CTAS / SELECT INTO","difficulty":2,
+         "prompt_md":"Create table `budget_items` where unit_cost < 2.00.",
+         "answer_sql":[
+             "CREATE TABLE budget_items AS SELECT * FROM items WHERE unit_cost < 2.00;",
+             "SELECT * INTO budget_items FROM items WHERE unit_cost < 2.00;"
+         ]}
+    ]
+}
 
-# --------------- OpenAI prompts + parsing helpers ---------------
-DOMAIN_AND_QUESTIONS_SCHEMA = {"required": ["domain", "tables", "questions"]}
+# --- Sales tax ---
+FALLBACK_PACKS["sales tax"] = {
+    "schema":{
+        "domain":"sales tax",
+        "tables":[
+            {"name":"jurisdictions","pk":["jurisdiction_id"],
+             "columns":[
+                 {"name":"jurisdiction_id","type":"INTEGER"},
+                 {"name":"name","type":"TEXT"},
+                 {"name":"state","type":"TEXT"}
+             ],
+             "fks":[],
+             "rows":[
+                 {"jurisdiction_id":1,"name":"King County","state":"WA"},
+                 {"jurisdiction_id":2,"name":"Multnomah","state":"OR"},
+                 {"jurisdiction_id":3,"name":"Maricopa","state":"AZ"},
+                 {"jurisdiction_id":4,"name":"Travis","state":"TX"},
+                 {"jurisdiction_id":5,"name":"Denver","state":"CO"},
+                 {"jurisdiction_id":6,"name":"Miami-Dade","state":"FL"}
+             ]},
+            {"name":"tax_rates","pk":["rate_id"],
+             "columns":[
+                 {"name":"rate_id","type":"INTEGER"},
+                 {"name":"jurisdiction_id","type":"INTEGER"},
+                 {"name":"category","type":"TEXT"},
+                 {"name":"rate","type":"REAL"}
+             ],
+             "fks":[{"columns":["jurisdiction_id"],"ref_table":"jurisdictions","ref_columns":["jurisdiction_id"]}],
+             "rows":[
+                 {"rate_id":101,"jurisdiction_id":1,"category":"general","rate":0.102},
+                 {"rate_id":102,"jurisdiction_id":2,"category":"general","rate":0.000},  # OR no sales tax
+                 {"rate_id":103,"jurisdiction_id":3,"category":"general","rate":0.056},
+                 {"rate_id":104,"jurisdiction_id":4,"category":"general","rate":0.0825},
+                 {"rate_id":105,"jurisdiction_id":5,"category":"general","rate":0.081},
+                 {"rate_id":106,"jurisdiction_id":6,"category":"general","rate":0.070}
+             ]},
+            {"name":"transactions","pk":["txn_id"],
+             "columns":[
+                 {"name":"txn_id","type":"INTEGER"},
+                 {"name":"txn_date","type":"TEXT"},
+                 {"name":"amount","type":"REAL"},
+                 {"name":"category","type":"TEXT"},
+                 {"name":"jurisdiction_id","type":"INTEGER"}
+             ],
+             "fks":[{"columns":["jurisdiction_id"],"ref_table":"jurisdictions","ref_columns":["jurisdiction_id"]}],
+             "rows":[
+                 {"txn_id":201,"txn_date":"2024-01-03","amount":120.00,"category":"general","jurisdiction_id":1},
+                 {"txn_id":202,"txn_date":"2024-01-05","amount":55.25,"category":"general","jurisdiction_id":2},
+                 {"txn_id":203,"txn_date":"2024-01-10","amount":300.00,"category":"general","jurisdiction_id":3},
+                 {"txn_id":204,"txn_date":"2024-02-01","amount":240.55,"category":"general","jurisdiction_id":4},
+                 {"txn_id":205,"txn_date":"2024-02-14","amount":89.99,"category":"general","jurisdiction_id":5},
+                 {"txn_id":206,"txn_date":"2024-03-02","amount":150.00,"category":"general","jurisdiction_id":6},
+                 {"txn_id":207,"txn_date":"2024-03-09","amount":70.00,"category":"general","jurisdiction_id":1},
+                 {"txn_id":208,"txn_date":"2024-03-15","amount":18.50,"category":"general","jurisdiction_id":2},
+                 {"txn_id":209,"txn_date":"2024-03-20","amount":99.95,"category":"general","jurisdiction_id":3},
+                 {"txn_id":210,"txn_date":"2024-03-25","amount":199.99,"category":"general","jurisdiction_id":4}
+             ]}
+        ]
+    },
+    "questions":[
+        {"id":"TX_Q01","category":"SELECT *","difficulty":1,
+         "prompt_md":"Show all records from `jurisdictions`.",
+         "answer_sql":["SELECT * FROM jurisdictions;"]},
+        {"id":"TX_Q02","category":"SELECT columns","difficulty":1,
+         "prompt_md":"Show `jurisdiction_id`, `rate` from `tax_rates`.",
+         "answer_sql":["SELECT jurisdiction_id, rate FROM tax_rates;"]},
+        {"id":"TX_Q03","category":"WHERE","difficulty":1,
+         "prompt_md":"Transactions over $150 (return `txn_id`, `amount`).",
+         "answer_sql":["SELECT txn_id, amount FROM transactions WHERE amount > 150;"]},
+        {"id":"TX_Q04","category":"Aliases","difficulty":1,
+         "prompt_md":"Join `transactions` (`t`) with `jurisdictions` (`j`), returning `t.txn_id`, `j.name`.",
+         "answer_sql":["SELECT t.txn_id, j.name FROM transactions t JOIN jurisdictions j ON t.jurisdiction_id=j.jurisdiction_id;"],
+         "requires_aliases":True,"required_aliases":["t","j"]},
+        {"id":"TX_Q05","category":"JOIN (INNER)","difficulty":2,
+         "prompt_md":"Compute tax for each transaction: return `txn_id`, `amount*rate` as `tax`.",
+         "answer_sql":["SELECT t.txn_id, t.amount * r.rate AS tax FROM transactions t INNER JOIN tax_rates r ON t.jurisdiction_id=r.jurisdiction_id AND t.category=r.category;"]},
+        {"id":"TX_Q06","category":"JOIN (LEFT)","difficulty":2,
+         "prompt_md":"Jurisdictions and count of transactions (include zero). Columns `name`, `txn_count`.",
+         "answer_sql":["SELECT j.name, COUNT(t.txn_id) AS txn_count FROM jurisdictions j LEFT JOIN transactions t ON j.jurisdiction_id=t.jurisdiction_id GROUP BY j.name;"]},
+        {"id":"TX_Q07","category":"VIEW","difficulty":2,
+         "prompt_md":"Create view `vw_high_txn` with transactions amount > 150.",
+         "answer_sql":["CREATE VIEW vw_high_txn AS SELECT * FROM transactions WHERE amount > 150;"]},
+        {"id":"TX_Q08","category":"CTAS / SELECT INTO","difficulty":2,
+         "prompt_md":"Create table `low_rate` with tax_rates where rate < 0.06.",
+         "answer_sql":[
+             "CREATE TABLE low_rate AS SELECT * FROM tax_rates WHERE rate < 0.06;",
+             "SELECT * INTO low_rate FROM tax_rates WHERE rate < 0.06;"
+         ]}
+    ]
+}
+
+# --- Oil & gas wells ---
+FALLBACK_PACKS["oil and gas wells"] = {
+    "schema":{
+        "domain":"oil and gas wells",
+        "tables":[
+            {"name":"wells","pk":["well_id"],
+             "columns":[
+                 {"name":"well_id","type":"INTEGER"},
+                 {"name":"well_name","type":"TEXT"},
+                 {"name":"location","type":"TEXT"},
+                 {"name":"status","type":"TEXT"},
+                 {"name":"depth","type":"INTEGER"}
+             ],
+             "fks":[],
+             "rows":[
+                 {"well_id":1,"well_name":"Alpha-1","location":"TX-TRV","status":"producing","depth":12000},
+                 {"well_id":2,"well_name":"Bravo-2","location":"TX-TRV","status":"shut-in","depth":10500},
+                 {"well_id":3,"well_name":"Cedar-7","location":"OK-CAD","status":"producing","depth":9800},
+                 {"well_id":4,"well_name":"Delta-3","location":"ND-WIL","status":"drilling","depth":7000},
+                 {"well_id":5,"well_name":"Eagle-5","location":"CO-DNV","status":"producing","depth":8500},
+                 {"well_id":6,"well_name":"Fox-9","location":"NM-LEA","status":"producing","depth":11000}
+             ]},
+            {"name":"operators","pk":["operator_id"],
+             "columns":[
+                 {"name":"operator_id","type":"INTEGER"},
+                 {"name":"name","type":"TEXT"},
+                 {"name":"contact","type":"TEXT"}
+             ],
+             "fks":[],
+             "rows":[
+                 {"operator_id":10,"name":"PetroMax","contact":"pmx@example.com"},
+                 {"operator_id":11,"name":"BlueRock Energy","contact":"blue@example.com"},
+                 {"operator_id":12,"name":"HighPlains LLC","contact":"hp@example.com"},
+                 {"operator_id":13,"name":"Mesa Oil","contact":"mesa@example.com"}
+             ]},
+            {"name":"well_operators","pk":["well_id","operator_id"],
+             "columns":[
+                 {"name":"well_id","type":"INTEGER"},
+                 {"name":"operator_id","type":"INTEGER"},
+                 {"name":"start_date","type":"TEXT"}
+             ],
+             "fks":[
+                 {"columns":["well_id"],"ref_table":"wells","ref_columns":["well_id"]},
+                 {"columns":["operator_id"],"ref_table":"operators","ref_columns":["operator_id"]}
+             ],
+             "rows":[
+                 {"well_id":1,"operator_id":10,"start_date":"2023-01-01"},
+                 {"well_id":2,"operator_id":10,"start_date":"2023-06-01"},
+                 {"well_id":3,"operator_id":11,"start_date":"2022-03-15"},
+                 {"well_id":4,"operator_id":12,"start_date":"2024-02-01"},
+                 {"well_id":5,"operator_id":13,"start_date":"2022-10-10"},
+                 {"well_id":6,"operator_id":11,"start_date":"2021-08-05"}
+             ]},
+            {"name":"production","pk":["prod_id"],
+             "columns":[
+                 {"name":"prod_id","type":"INTEGER"},
+                 {"name":"well_id","type":"INTEGER"},
+                 {"name":"month","type":"TEXT"},
+                 {"name":"oil_bbl","type":"REAL"},
+                 {"name":"gas_mcf","type":"REAL"}
+             ],
+             "fks":[{"columns":["well_id"],"ref_table":"wells","ref_columns":["well_id"]}],
+             "rows":[
+                 {"prod_id":1001,"well_id":1,"month":"2024-01","oil_bbl":1200,"gas_mcf":5000},
+                 {"prod_id":1002,"well_id":1,"month":"2024-02","oil_bbl":1180,"gas_mcf":5100},
+                 {"prod_id":1003,"well_id":3,"month":"2024-01","oil_bbl":900,"gas_mcf":3000},
+                 {"prod_id":1004,"well_id":3,"month":"2024-02","oil_bbl":950,"gas_mcf":3100},
+                 {"prod_id":1005,"well_id":5,"month":"2024-01","oil_bbl":600,"gas_mcf":2200},
+                 {"prod_id":1006,"well_id":6,"month":"2024-01","oil_bbl":750,"gas_mcf":2600},
+                 {"prod_id":1007,"well_id":2,"month":"2024-01","oil_bbl":0,"gas_mcf":0},
+                 {"prod_id":1008,"well_id":4,"month":"2024-02","oil_bbl":100,"gas_mcf":400}
+             ]}
+        ]
+    },
+    "questions":[
+        {"id":"OG_Q01","category":"SELECT *","difficulty":1,
+         "prompt_md":"List all rows from `wells`.",
+         "answer_sql":["SELECT * FROM wells;"]},
+        {"id":"OG_Q02","category":"SELECT columns","difficulty":1,
+         "prompt_md":"Return `well_name`, `status` from `wells`.",
+         "answer_sql":["SELECT well_name, status FROM wells;"]},
+        {"id":"OG_Q03","category":"WHERE","difficulty":1,
+         "prompt_md":"Wells deeper than 10,000 ft (return `well_name`, `depth`).",
+         "answer_sql":["SELECT well_name, depth FROM wells WHERE depth > 10000;"]},
+        {"id":"OG_Q04","category":"Aliases","difficulty":1,
+         "prompt_md":"Using `w` and `o`, show `w.well_name` with `o.name` (operator).",
+         "answer_sql":["SELECT w.well_name, o.name FROM wells w JOIN well_operators wo ON w.well_id=wo.well_id JOIN operators o ON wo.operator_id=o.operator_id;"],
+         "requires_aliases":True,"required_aliases":["w","o"]},
+        {"id":"OG_Q05","category":"JOIN (INNER)","difficulty":2,
+         "prompt_md":"Join `production` to `wells` and return `well_name`, total `oil_bbl` per well.",
+         "answer_sql":["SELECT w.well_name, SUM(p.oil_bbl) AS total_oil FROM production p INNER JOIN wells w ON p.well_id=w.well_id GROUP BY w.well_name;"]},
+        {"id":"OG_Q06","category":"JOIN (LEFT)","difficulty":2,
+         "prompt_md":"List operators and count of wells (include operators with zero). Columns `name`, `well_count`.",
+         "answer_sql":["SELECT o.name, COUNT(wo.well_id) AS well_count FROM operators o LEFT JOIN well_operators wo ON o.operator_id=wo.operator_id GROUP BY o.name;"]},
+        {"id":"OG_Q07","category":"VIEW","difficulty":2,
+         "prompt_md":"Create view `vw_prod_jan` for January 2024 production.",
+         "answer_sql":["CREATE VIEW vw_prod_jan AS SELECT * FROM production WHERE month='2024-01';"]},
+        {"id":"OG_Q08","category":"CTAS / SELECT INTO","difficulty":2,
+         "prompt_md":"Create table `active_wells` for wells with status='producing'.",
+         "answer_sql":[
+             "CREATE TABLE active_wells AS SELECT * FROM wells WHERE status='producing';",
+             "SELECT * INTO active_wells FROM wells WHERE status='producing';"
+         ]}
+    ]
+}
 
-def _domain_prompt(prev_domain: Optional[str]) -> str:
-    extra = f" Avoid using the previous domain '{prev_domain}' if possible." if prev_domain else ""
+# --- Marketing ---
+FALLBACK_PACKS["marketing"] = {
+    "schema":{
+        "domain":"marketing",
+        "tables":[
+            {"name":"channels","pk":["channel_id"],
+             "columns":[
+                 {"name":"channel_id","type":"INTEGER"},
+                 {"name":"channel_name","type":"TEXT"}
+             ],
+             "fks":[],
+             "rows":[
+                 {"channel_id":1,"channel_name":"Search"},
+                 {"channel_id":2,"channel_name":"Social"},
+                 {"channel_id":3,"channel_name":"Email"},
+                 {"channel_id":4,"channel_name":"Display"}
+             ]},
+            {"name":"campaigns","pk":["campaign_id"],
+             "columns":[
+                 {"name":"campaign_id","type":"INTEGER"},
+                 {"name":"campaign_name","type":"TEXT"},
+                 {"name":"channel_id","type":"INTEGER"},
+                 {"name":"start_date","type":"TEXT"},
+                 {"name":"budget","type":"REAL"}
+             ],
+             "fks":[{"columns":["channel_id"],"ref_table":"channels","ref_columns":["channel_id"]}],
+             "rows":[
+                 {"campaign_id":101,"campaign_name":"Spring Search","channel_id":1,"start_date":"2024-03-01","budget":5000},
+                 {"campaign_id":102,"campaign_name":"Brand Social","channel_id":2,"start_date":"2024-03-05","budget":3000},
+                 {"campaign_id":103,"campaign_name":"Welcome Email","channel_id":3,"start_date":"2024-03-07","budget":1000},
+                 {"campaign_id":104,"campaign_name":"Retargeting","channel_id":4,"start_date":"2024-03-10","budget":2000},
+                 {"campaign_id":105,"campaign_name":"Summer Search","channel_id":1,"start_date":"2024-06-01","budget":6000},
+                 {"campaign_id":106,"campaign_name":"Promo Social","channel_id":2,"start_date":"2024-06-05","budget":3500}
+             ]},
+            {"name":"ad_stats","pk":["campaign_id","day"],
+             "columns":[
+                 {"name":"campaign_id","type":"INTEGER"},
+                 {"name":"day","type":"TEXT"},
+                 {"name":"impressions","type":"INTEGER"},
+                 {"name":"clicks","type":"INTEGER"},
+                 {"name":"spend","type":"REAL"}
+             ],
+             "fks":[{"columns":["campaign_id"],"ref_table":"campaigns","ref_columns":["campaign_id"]}],
+             "rows":[
+                 {"campaign_id":101,"day":"2024-03-12","impressions":10000,"clicks":500,"spend":200.0},
+                 {"campaign_id":101,"day":"2024-03-13","impressions":12000,"clicks":600,"spend":230.0},
+                 {"campaign_id":102,"day":"2024-03-12","impressions":8000,"clicks":400,"spend":150.0},
+                 {"campaign_id":103,"day":"2024-03-12","impressions":5000,"clicks":250,"spend":80.0},
+                 {"campaign_id":104,"day":"2024-03-12","impressions":7000,"clicks":210,"spend":110.0},
+                 {"campaign_id":106,"day":"2024-06-12","impressions":9500,"clicks":520,"spend":190.0}
+             ]},
+            {"name":"leads","pk":["lead_id"],
+             "columns":[
+                 {"name":"lead_id","type":"INTEGER"},
+                 {"name":"campaign_id","type":"INTEGER"},
+                 {"name":"source","type":"TEXT"},
+                 {"name":"qualified","type":"INTEGER"},
+                 {"name":"revenue","type":"REAL"}
+             ],
+             "fks":[{"columns":["campaign_id"],"ref_table":"campaigns","ref_columns":["campaign_id"]}],
+             "rows":[
+                 {"lead_id":1,"campaign_id":101,"source":"LP1","qualified":1,"revenue":400},
+                 {"lead_id":2,"campaign_id":101,"source":"LP2","qualified":0,"revenue":0},
+                 {"lead_id":3,"campaign_id":102,"source":"FB","qualified":1,"revenue":250},
+                 {"lead_id":4,"campaign_id":103,"source":"Email","qualified":1,"revenue":300},
+                 {"lead_id":5,"campaign_id":104,"source":"DSP","qualified":0,"revenue":0},
+                 {"lead_id":6,"campaign_id":106,"source":"FB","qualified":1,"revenue":500}
+             ]}
+        ]
+    },
+    "questions":[
+        {"id":"M_Q01","category":"SELECT *","difficulty":1,
+         "prompt_md":"Show all channels.",
+         "answer_sql":["SELECT * FROM channels;"]},
+        {"id":"M_Q02","category":"SELECT columns","difficulty":1,
+         "prompt_md":"Return `campaign_name`, `budget` from `campaigns`.",
+         "answer_sql":["SELECT campaign_name, budget FROM campaigns;"]},
+        {"id":"M_Q03","category":"WHERE","difficulty":1,
+         "prompt_md":"Campaigns with budget ≥ 3000 (show `campaign_name`, `budget`).",
+         "answer_sql":["SELECT campaign_name, budget FROM campaigns WHERE budget >= 3000;"]},
+        {"id":"M_Q04","category":"Aliases","difficulty":1,
+         "prompt_md":"Join `campaigns` (`c`) with `channels` (`ch`) and show `c.campaign_name`, `ch.channel_name`.",
+         "answer_sql":["SELECT c.campaign_name, ch.channel_name FROM campaigns c JOIN channels ch ON c.channel_id=ch.channel_id;"],
+         "requires_aliases":True,"required_aliases":["c","ch"]},
+        {"id":"M_Q05","category":"JOIN (INNER)","difficulty":2,
+         "prompt_md":"Join `ad_stats` with `campaigns` and return `campaign_name`, total `clicks`.",
+         "answer_sql":["SELECT c.campaign_name, SUM(s.clicks) AS total_clicks FROM ad_stats s INNER JOIN campaigns c ON s.campaign_id=c.campaign_id GROUP BY c.campaign_name;"]},
+        {"id":"M_Q06","category":"JOIN (LEFT)","difficulty":2,
+         "prompt_md":"Channels and number of campaigns (include channels with zero). Columns `channel_name`, `campaigns`.",
+         "answer_sql":["SELECT ch.channel_name, COUNT(c.campaign_id) AS campaigns FROM channels ch LEFT JOIN campaigns c ON ch.channel_id=c.channel_id GROUP BY ch.channel_name;"]},
+        {"id":"M_Q07","category":"VIEW","difficulty":2,
+         "prompt_md":"Create view `vw_cost_per_click` with `campaign_id`, `day`, `spend/clicks` as `cpc` (avoid divide-by-zero).",
+         "answer_sql":["CREATE VIEW vw_cost_per_click AS SELECT campaign_id, day, CASE WHEN clicks=0 THEN NULL ELSE spend*1.0/clicks END AS cpc FROM ad_stats;"]},
+        {"id":"M_Q08","category":"CTAS / SELECT INTO","difficulty":2,
+         "prompt_md":"Create table `qualified_leads` of leads where `qualified=1`.",
+         "answer_sql":[
+             "CREATE TABLE qualified_leads AS SELECT * FROM leads WHERE qualified=1;",
+             "SELECT * INTO qualified_leads FROM leads WHERE qualified=1;"
+         ]}
+    ]
+}
+
+# Helpers to get a pack by domain (normalize key)
+def get_fallback_pack_for(domain_name: str) -> Tuple[Dict[str,Any], List[Dict[str,Any]]]:
+    key = (domain_name or "").strip().lower()
+    for k, pack in FALLBACK_PACKS.items():
+        if k.lower() == key:
+            return pack["schema"], pack["questions"]
+    # default to bookstore
+    p = FALLBACK_PACKS["bookstore"]
+    return p["schema"], p["questions"]
+
+# -------------------- OpenAI prompts + parsing helpers --------------------
+ALLOWED_DOMAINS = ["bookstore", "retail sales", "wholesaler", "sales tax", "oil and gas wells", "marketing"]
+
+DOMAIN_CYCLE_POS = 0  # will be set after first install
+
+def _domain_prompt(prev_domain: Optional[str], preferred_domain: Optional[str]) -> str:
+    target = preferred_domain or "bookstore"
+    extra = f" (previous domain was '{prev_domain}', do not reuse it)" if prev_domain else ""
     return f"""
 Return ONLY a valid JSON object (no markdown, no prose).
-The JSON must have: domain (string), tables (3–4 table objects), and questions (8–12 question objects).{extra}
+You MUST set the top-level property "domain" to EXACTLY "{target}" (string match).{extra}
 
-Rules:
-- One domain chosen from: bookstore, retail sales, wholesaler, sales tax, oil and gas wells, marketing.
-- Tables: SQLite-friendly. Use snake_case. Each table has: name, pk (list of column names),
-  columns (list of {{name,type}}), fks (list of {{columns,ref_table,ref_columns}}), rows (8–15 small seed rows).
-- Questions: categories among "SELECT *", "SELECT columns", "WHERE", "Aliases",
-  "JOIN (INNER)", "JOIN (LEFT)", "Aggregation", "VIEW", "CTAS / SELECT INTO".
-  Include at least one LEFT JOIN, one VIEW creation, one CTAS or SELECT INTO.
-  Provide 1–3 'answer_sql' strings per question. Prefer SQLite-compatible SQL. Do NOT use RIGHT/FULL OUTER JOIN.
-  For 1–2 questions, set requires_aliases=true and list required_aliases.
+The JSON must have:
+- "domain": "{target}"
+- "tables": 3–4 table objects
+- "questions": 8–12 question objects
 
-Example top-level keys:
-{{"domain":"retail sales","tables":[...],"questions":[...]}}
+Tables:
+- SQLite-friendly. Use snake_case.
+- Each table: name, pk (list), columns (list of {{name,type}}), fks (list of {{columns,ref_table,ref_columns}}), rows (8–15 small seed rows).
+
+Questions:
+- Categories among: "SELECT *", "SELECT columns", "WHERE", "Aliases",
+  "JOIN (INNER)", "JOIN (LEFT)", "Aggregation", "VIEW", "CTAS / SELECT INTO".
+- Include at least one LEFT JOIN, one VIEW, one CTAS or SELECT INTO.
+- Provide 1–3 'answer_sql' strings per question.
+- Prefer SQLite-compatible SQL. Do NOT use RIGHT/FULL OUTER JOIN.
 """
 
 def _loose_json_parse(s: str) -> Optional[dict]:
@@ -475,11 +1022,11 @@ def _canon_tables(tables: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
         })
     return out
 
-def llm_generate_domain_and_questions(prev_domain: Optional[str]):
+def llm_generate_domain_and_questions(prev_domain: Optional[str], preferred_domain: Optional[str]):
     if not OPENAI_AVAILABLE or not os.getenv("OPENAI_API_KEY"):
         return None, "OpenAI client not available or OPENAI_API_KEY missing.", None, {"accepted_questions":0,"dropped_questions":0}
     errors = []
-    prompt = _domain_prompt(prev_domain)
+    prompt = _domain_prompt(prev_domain, preferred_domain)
     for model in _candidate_models():
         try:
             try:
@@ -501,9 +1048,12 @@ def llm_generate_domain_and_questions(prev_domain: Optional[str]):
             obj_raw = _loose_json_parse(data_text or "")
             if not obj_raw:
                 raise RuntimeError("Could not parse JSON from model output.")
-            for k in DOMAIN_AND_QUESTIONS_SCHEMA["required"]:
+            for k in ["domain","tables","questions"]:
                 if k not in obj_raw:
                     raise RuntimeError(f"Missing key '{k}'")
+            # Force domain to preferred
+            if preferred_domain and (str(obj_raw.get("domain","")).strip().lower() != preferred_domain.strip().lower()):
+                raise RuntimeError(f"Model returned domain '{obj_raw.get('domain')}', expected '{preferred_domain}'.")
             tables = _canon_tables(obj_raw.get("tables", []))
             if not tables: raise RuntimeError("No usable tables in LLM output.")
             obj_raw["tables"] = tables
@@ -569,21 +1119,49 @@ def install_schema(con: sqlite3.Connection, schema: Dict[str,Any]):
                     (schema.get("domain","unknown"), json.dumps(schema)))
         con.commit()
 
-def bootstrap_domain_with_llm_or_fallback(prev_domain: Optional[str]):
-    obj, err, model_used, stats = llm_generate_domain_and_questions(prev_domain)
+def bootstrap_domain_with_llm_or_fallback(prev_domain: Optional[str], preferred_domain: str):
+    obj, err, model_used, stats = llm_generate_domain_and_questions(prev_domain, preferred_domain)
     if obj is None:
-        return FALLBACK_SCHEMA, FALLBACK_QUESTIONS, {"source":"fallback","model":None,"error":err,"accepted":0,"dropped":0}
+        # Use domain-specific fallback pack
+        schema, questions = get_fallback_pack_for(preferred_domain)
+        info = {"source":"fallback","model":None,"error":err,"accepted":0,"dropped":0}
+        return schema, questions, info
     return obj, obj["questions"], {"source":"openai","model":model_used,"error":None,"accepted":stats["accepted_questions"],"dropped":stats["dropped_questions"]}
 
-def install_schema_and_prepare_questions(prev_domain: Optional[str]):
-    schema, questions, info = bootstrap_domain_with_llm_or_fallback(prev_domain)
+def install_schema_and_prepare_questions(prev_domain: Optional[str], preferred_domain: str):
+    schema, questions, info = bootstrap_domain_with_llm_or_fallback(prev_domain, preferred_domain)
     install_schema(CONN, schema)
     if not questions:
-        questions = FALLBACK_QUESTIONS
+        # fallback to domain pack (still preferred)
+        schema2, questions2 = get_fallback_pack_for(preferred_domain)
+        install_schema(CONN, schema2)
+        schema, questions, info = schema2, questions2, {"source":"fallback","model":None,"error":"No usable questions from LLM","accepted":0,"dropped":0}
     return schema, questions, info
 
-# -------------------- Session globals --------------------
-CURRENT_SCHEMA, CURRENT_QS, CURRENT_INFO = install_schema_and_prepare_questions(prev_domain=None)
+# -------------------- Domain cycling --------------------
+def _norm(s: str) -> str:
+    return (s or "").strip().lower()
+
+def _index_of_domain(name: str) -> int:
+    low = _norm(name)
+    for i, d in enumerate(ALLOWED_DOMAINS):
+        if _norm(d) == low:
+            return i
+    return 0
+
+def choose_next_domain(prev_domain: Optional[str]) -> str:
+    global DOMAIN_CYCLE_POS
+    if prev_domain is None:
+        DOMAIN_CYCLE_POS = 0
+        return ALLOWED_DOMAINS[DOMAIN_CYCLE_POS]
+    idx = _index_of_domain(prev_domain)
+    DOMAIN_CYCLE_POS = (idx + 1) % len(ALLOWED_DOMAINS)
+    return ALLOWED_DOMAINS[DOMAIN_CYCLE_POS]
+
+# -------------------- Initialize first domain --------------------
+# Start with bookstore for determinism on first boot
+CURRENT_SCHEMA, CURRENT_QS, CURRENT_INFO = install_schema_and_prepare_questions(prev_domain=None, preferred_domain="bookstore")
+DOMAIN_CYCLE_POS = _index_of_domain(CURRENT_SCHEMA.get("domain","bookstore"))
 
 # -------------------- Progress + mastery --------------------
 def upsert_user(con: sqlite3.Connection, user_id: str, name: str):
@@ -617,7 +1195,7 @@ def fetch_attempts(con: sqlite3.Connection, user_id: str) -> pd.DataFrame:
         return pd.read_sql_query("SELECT * FROM attempts WHERE user_id=? ORDER BY id DESC", con, params=(user_id,))
 
 def pick_next_question(user_id: str) -> Dict[str,Any]:
-    pool = CURRENT_QS if CURRENT_QS else FALLBACK_QUESTIONS
+    pool = CURRENT_QS if CURRENT_QS else get_fallback_pack_for(CURRENT_SCHEMA.get("domain","bookstore"))[1]
     df = fetch_attempts(CONN, user_id)
     stats = topic_stats(df)
     stats = stats.sort_values(by=["accuracy","attempts"], ascending=[True, True]) if not stats.empty else stats
@@ -910,8 +1488,19 @@ def show_hint(session: dict):
     }.get(cat, "Identify keys from the schema and join on them.")
     return gr.update(value=f"**Hint:** {hint}", visible=True)
 
+def export_progress(user_name: str):
+    slug = "-".join((user_name or "").lower().split())
+    if not slug: return None
+    user_id = slug[:64]
+    with DB_LOCK:
+        df = pd.read_sql_query("SELECT * FROM attempts WHERE user_id=? ORDER BY id DESC", CONN, params=(user_id,))
+    os.makedirs(EXPORT_DIR, exist_ok=True)
+    path = os.path.abspath(os.path.join(EXPORT_DIR, f"{user_id}_progress.csv"))
+    (pd.DataFrame([{"info":"No attempts yet."}]) if df.empty else df).to_csv(path, index=False)
+    return path
+
 def _domain_status_md():
-    if CURRENT_INFO.get("source","openai"):
+    if CURRENT_INFO.get("source","openai") == "openai":
         accepted = CURRENT_INFO.get("accepted",0); dropped = CURRENT_INFO.get("dropped",0)
         return (f"✅ **Domain via OpenAI** `{CURRENT_INFO.get('model','?')}` → **{CURRENT_SCHEMA.get('domain','?')}**. "
                 f"Accepted questions: {accepted}, dropped: {dropped}.  \n"
@@ -929,11 +1518,12 @@ def list_tables_for_preview():
     """)
     return df["name"].tolist() if not df.empty else ["(no tables)"]
 
-# >>> FIX: Always reseed a question on randomize (creates a guest session if needed)
+# Always reseed a question on randomize (creates a guest session if needed)
 def regenerate_domain(session: dict):
     global CURRENT_SCHEMA, CURRENT_QS, CURRENT_INFO
     prev = CURRENT_SCHEMA.get("domain") if CURRENT_SCHEMA else None
-    CURRENT_SCHEMA, CURRENT_QS, CURRENT_INFO = install_schema_and_prepare_questions(prev_domain=prev)
+    preferred = choose_next_domain(prev)
+    CURRENT_SCHEMA, CURRENT_QS, CURRENT_INFO = install_schema_and_prepare_questions(prev_domain=prev, preferred_domain=preferred)
     erd = draw_dynamic_erd(CURRENT_SCHEMA)
     status = _domain_status_md()
 
@@ -947,11 +1537,8 @@ def regenerate_domain(session: dict):
     q = pick_next_question(session["user_id"])
     session.update({"qid": q["id"], "q": q, "start_ts": time.time()})
 
-    # Fresh mastery and cleared result preview
     stats = topic_stats(fetch_attempts(CONN, session["user_id"]))
     empty_df = pd.DataFrame()
-
-    # Refresh dropdown
     dd_update = gr.update(choices=list_tables_for_preview(), value=None)
 
     return (
@@ -1056,11 +1643,11 @@ with gr.Blocks(title="Adaptive SQL Trainer — Randomized Domains") as demo:
         outputs=[feedback_md],
     )
     export_btn.click(
-        lambda user: os.path.abspath(os.path.join(EXPORT_DIR, f"{'-'.join((user or '').lower().split())[:64]}_progress.csv")),
+        export_progress,
         inputs=[export_name],
         outputs=[export_file],
     )
-    regen_btn.click(  # one callback: reseed question, refresh dropdown, clear previews
+    regen_btn.click(
         regenerate_domain,
         inputs=[session_state],
         outputs=[regen_fb, er_image, prompt_md, sql_input, tbl_dd, mastery_df, result_df, session_state],