Update app.py

app.py

@@ -3,7 +3,7 @@
 # - 3–4 related tables with seed rows installed in SQLite.
 # - 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.
+# - ERD shows tables only (no edges) and bolds the tables you reference while typing.
 # - Domain picker now round-robins across: bookstore, retail sales, wholesaler, sales tax, oil & gas wells, marketing.
 
 import os
@@ -62,15 +62,12 @@ def _fig_to_pil(fig) -> Image.Image:
 def draw_dynamic_erd(
     schema: Dict[str, Any],
     highlight_tables: Optional[Set[str]] = None,
-    highlight_edges: Optional[Set[Tuple[str, str]]] = None,
+    highlight_edges: Optional[Set[Tuple[str, str]]] = None,  # kept for API compatibility; ignored
 ) -> Image.Image:
     """
-    Draw tables + FK edges. If highlight_* provided, overlay those tables/edges in bold.
-    highlight_edges uses (src_table, dst_table) with dst_table = referenced table.
+    Draw tables (no FK edges). If highlight_tables provided, those table borders are bolded.
     """
     highlight_tables = set(highlight_tables or [])
-    def _norm_edge(a, b): return tuple(sorted([a, b]))
-    H = set(_norm_edge(*e) for e in (highlight_edges or set()))
 
     tables = schema.get("tables", [])
     fig, ax = plt.subplots(figsize=PLOT_FIGSIZE); ax.axis("off")
@@ -78,23 +75,18 @@ def draw_dynamic_erd(
         ax.text(0.5, 0.5, "No tables to diagram.", ha="center", va="center")
         return _fig_to_pil(fig)
 
+    # Layout tables horizontally
     n = len(tables)
     margin = 0.03
     width = (1 - margin * (n + 1)) / max(n, 1)
     height = 0.70
     y = 0.20
 
-    fk_edges = []
-    for t in tables:
-        for fk in t.get("fks", []) or []:
-            dst = fk.get("ref_table")
-            if dst:
-                fk_edges.append((t["name"], dst))
-
-    boxes: Dict[str, Tuple[float,float,float,float]] = {}
+    # Draw table boxes + columns (no edges)
     for i, t in enumerate(tables):
         tx = margin + i * (width + margin)
-        boxes[t["name"]] = (tx, y, width, height)
+
+        # Border weight highlights usage
         lw = 2.0 if t["name"] in highlight_tables else 1.2
         ax.add_patch(Rectangle((tx, y), width, height, fill=False, lw=lw))
         ax.text(tx + 0.01, y + height - 0.04, t["name"], fontsize=10, ha="left", va="top", weight="bold")
@@ -118,28 +110,10 @@ def draw_dynamic_erd(
             ax.text(tx + 0.016, yy, f"{nm}{tag}", fontsize=9, ha="left", va="top")
             yy -= 0.055
 
-    for (src, dst) in fk_edges:
-        if src not in boxes or dst not in boxes: 
-            continue
-        (x1, y1, w1, h1) = boxes[src]
-        (x2, y2, w2, h2) = boxes[dst]
-        ax.annotate("",
-                    xy=(x2 + w2/2.0, y2 + h2),
-                    xytext=(x1 + w1/2.0, y1),
-                    arrowprops=dict(arrowstyle="->", lw=1.0, color="#cccccc"))
-
-    for (src, dst) in fk_edges:
-        if _norm_edge(src, dst) in H:
-            (x1, y1, w1, h1) = boxes[src]
-            (x2, y2, w2, h2) = boxes[dst]
-            ax.annotate("",
-                        xy=(x2 + w2/2.0, y2 + h2),
-                        xytext=(x1 + w1/2.0, y1),
-                        arrowprops=dict(arrowstyle="->", lw=2.6, color="#2b6cb0"))
-
     ax.text(0.5, 0.06, f"Domain: {schema.get('domain','unknown')}", fontsize=9, ha="center")
     return _fig_to_pil(fig)
 
+# Parse JOINs from SQL to highlight tables (edges ignored now)
 JOIN_TBL_RE = re.compile(r"\b(?:from|join)\s+([a-z_]\w*)(?:\s+(?:as\s+)?([a-z_]\w*))?", re.IGNORECASE)
 EQ_ON_RE = re.compile(r"([a-z_]\w*)\.[a-z_]\w*\s*=\s*([a-z_]\w*)\.[a-z_]\w*", re.IGNORECASE)
 USING_RE = re.compile(r"\bjoin\s+([a-z_]\w*)(?:\s+(?:as\s+)?([a-z_]\w*))?\s+using\s*\(", re.IGNORECASE)
@@ -158,6 +132,7 @@ def sql_highlights(sql: str, schema: Dict[str, Any]) -> Tuple[Set[str], Set[Tupl
         alias_to_table[alias] = table
         join_order.append(alias)
 
+    # Edges are computed but not used by the renderer anymore
     edges: Set[Tuple[str, str]] = set()
     for a1, a2 in EQ_ON_RE.findall(low):
         t1 = alias_to_table.get(a1, a1)
@@ -174,10 +149,77 @@ def sql_highlights(sql: str, schema: Dict[str, Any]) -> Tuple[Set[str], Set[Tupl
 
     used_tables = {alias_to_table.get(a, a) for a in join_order}
     schema_tables = {t["name"] for t in schema.get("tables", [])}
-    edges = { (a, b) for (a, b) in edges if a in schema_tables and b in schema_tables }
-    used_tables = { t for t in used_tables if t in schema_tables }
+    edges = {(a, b) for (a, b) in edges if a in schema_tables and b in schema_tables}
+    used_tables = {t for t in used_tables if t in schema_tables}
     return used_tables, edges
 
+# -------------------- SQL keyword highlighting (preview) --------------------
+def _html_escape(s: str) -> str:
+    return s.replace("&", "&amp;").replace("<", "&lt;").replace(">", "&gt;")
+
+# Multi-word phrases first (to avoid double-highlighting)
+PHRASE_CLASSES = [
+    (r"\bgroup\s+by\b", "kw-group"),
+    (r"\border\s+by\b", "kw-order"),
+    (r"\bleft\s+join\b", "kw-join"),
+    (r"\binner\s+join\b", "kw-join"),
+    (r"\bright\s+join\b", "kw-join"),
+    (r"\bfull\s+(?:outer\s+)?join\b", "kw-join"),
+    (r"\bcreate\s+view\b", "kw-ddl"),
+    (r"\bcreate\s+table\b", "kw-ddl"),
+    (r"\binsert\s+into\b", "kw-ddl"),
+    (r"\bselect\s+into\b", "kw-ddl"),
+    (r"\bprimary\s+key\b", "kw-ddl"),
+    (r"\bforeign\s+key\b", "kw-ddl"),
+]
+
+TOKEN_CLASSES = [
+    (r"\bselect\b", "kw-core"),
+    (r"\bfrom\b", "kw-core"),
+    (r"\bwhere\b", "kw-core"),
+    (r"\bjoin\b", "kw-join"),
+    (r"\bon\b", "kw-join"),
+    (r"\busing\b", "kw-join"),
+    (r"\bhaving\b", "kw-group"),
+    (r"\blimit\b", "kw-order"),
+    (r"\boffset\b", "kw-order"),
+    (r"\bdistinct\b", "kw-core"),
+    (r"\bcase\b", "kw-core"),
+    (r"\bwhen\b", "kw-core"),
+    (r"\bthen\b", "kw-core"),
+    (r"\belse\b", "kw-core"),
+    (r"\bend\b", "kw-core"),
+    (r"\band\b", "kw-core"),
+    (r"\bor\b", "kw-core"),
+    (r"\bnot\b", "kw-core"),
+    (r"\bin\b", "kw-core"),
+    (r"\bis\b", "kw-core"),
+    (r"\bnull\b", "kw-core"),
+    (r"\blike\b", "kw-core"),
+    (r"\bbetween\b", "kw-core"),
+    (r"\bunion\b", "kw-core"),
+    (r"\ball\b", "kw-core"),
+    (r"\bupdate\b", "kw-ddl"),
+    (r"\bdelete\b", "kw-ddl"),
+    (r"\bview\b", "kw-ddl"),
+    (r"\btable\b", "kw-ddl"),
+    (r"\binto\b", "kw-ddl"),
+    (r"\bvalues\b", "kw-ddl"),
+    (r"\bcreate\b", "kw-ddl"),
+]
+
+def highlight_sql_html(sql: str) -> str:
+    if not sql:
+        return ""
+    txt = _html_escape(sql)
+    # Phrases first
+    for pat, cls in PHRASE_CLASSES:
+        txt = re.sub(pat, lambda m: f'<span class="kw {cls}">{m.group(0).upper()}</span>', txt, flags=re.IGNORECASE)
+    # Single tokens
+    for pat, cls in TOKEN_CLASSES:
+        txt = re.sub(pat, lambda m: f'<span class="kw {cls}">{m.group(0).upper()}</span>', txt, flags=re.IGNORECASE)
+    return f'<div class="sql-preview"><pre><code>{txt}</code></pre></div>'
+
 # -------------------- SQLite + locking --------------------
 DB_DIR = "/data" if os.path.exists("/data") else "."
 DB_PATH = os.path.join(DB_DIR, "sql_trainer_dynamic.db")
@@ -234,9 +276,8 @@ def init_progress_tables(con: sqlite3.Connection):
 init_progress_tables(CONN)
 
 # -------------------- Built-in fallback domain packs --------------------
-# Each pack: {"schema": {...}, "questions": [...]}
+# (packs identical to your last version; omitted comments for brevity)
 FALLBACK_PACKS: Dict[str, Dict[str, Any]] = {}
-
 # --- Bookstore (existing) ---
 FALLBACK_PACKS["bookstore"] = {
     "schema": {
@@ -300,14 +341,14 @@ FALLBACK_PACKS["bookstore"] = {
                     {"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},
+                    {"book_id":105,"title":"Norwegian Wood","author_id":3,"store_id":3,"category":"Fiction","price":18.00},
+                    {"book_id":106,"title":"Kafka on the Shore","author_id":3,"store_id":1,"category":"Fiction","price":21.00},
+                    {"book_id":107,"title":"Things Fall Apart","author_id":4,"store_id":1,"category":"Fiction","price":10.00},
+                    {"book_id":108,"title":"Pride and Prejudice","author_id":5,"store_id":2,"category":"Fiction","price":9.00},
+                    {"book_id":109,"title":"Harry Potter and the Sorcerer's Stone","author_id":6,"store_id":3,"category":"Children","price":22.00},
+                    {"book_id":110,"title":"Harry Potter and the Chamber of Secrets","author_id":6,"store_id":3,"category":"Children","price":23.00},
+                    {"book_id":111,"title":"Sapiens","author_id":7,"store_id":1,"category":"History","price":26.00},
+                    {"book_id":112,"title":"Homo Deus","author_id":7,"store_id":2,"category":"History","price":28.00},
                 ],
             },
         ]
@@ -343,102 +384,82 @@ FALLBACK_PACKS["bookstore"] = {
          ]},
     ]
 }
-
 # --- 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}
-                ]
-            }
-        ]
-    },
+            {"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`.",
@@ -470,7 +491,6 @@ FALLBACK_PACKS["retail sales"] = {
          ]}
     ]
 }
-
 # --- Wholesaler ---
 FALLBACK_PACKS["wholesaler"] = {
     "schema":{
@@ -480,22 +500,19 @@ FALLBACK_PACKS["wholesaler"] = {
              "columns":[
                  {"name":"supplier_id","type":"INTEGER"},
                  {"name":"supplier_name","type":"TEXT"},
-                 {"name":"country","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"}
-             ]},
+                 {"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"}
-             ],
+                 {"name":"unit_cost","type":"REAL"}],
              "fks":[],
              "rows":[
                  {"item_id":101,"item_name":"Olive Oil 1L","unit_cost":4.20},
@@ -505,14 +522,12 @@ FALLBACK_PACKS["wholesaler"] = {
                  {"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}
-             ]},
+                 {"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"}
-             ],
+                 {"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"},
@@ -520,19 +535,16 @@ FALLBACK_PACKS["wholesaler"] = {
                  {"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"}
-             ]},
+                 {"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"}
-             ],
+                 {"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"]}
-             ],
+                 {"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},
@@ -541,9 +553,7 @@ FALLBACK_PACKS["wholesaler"] = {
                  {"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}
-             ]}
-        ]
+                 {"po_id":206,"item_id":108,"qty":250,"line_cost":525.0}]}]
     },
     "questions":[
         {"id":"W_Q01","category":"SELECT *","difficulty":1,
@@ -576,7 +586,6 @@ FALLBACK_PACKS["wholesaler"] = {
          ]}
     ]
 }
-
 # --- Sales tax ---
 FALLBACK_PACKS["sales tax"] = {
     "schema":{
@@ -586,8 +595,7 @@ FALLBACK_PACKS["sales tax"] = {
              "columns":[
                  {"name":"jurisdiction_id","type":"INTEGER"},
                  {"name":"name","type":"TEXT"},
-                 {"name":"state","type":"TEXT"}
-             ],
+                 {"name":"state","type":"TEXT"}],
              "fks":[],
              "rows":[
                  {"jurisdiction_id":1,"name":"King County","state":"WA"},
@@ -595,32 +603,28 @@ FALLBACK_PACKS["sales tax"] = {
                  {"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"}
-             ]},
+                 {"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"}
-             ],
+                 {"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":102,"jurisdiction_id":2,"category":"general","rate":0.000},
                  {"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}
-             ]},
+                 {"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"}
-             ],
+                 {"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},
@@ -632,9 +636,7 @@ FALLBACK_PACKS["sales tax"] = {
                  {"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}
-             ]}
-        ]
+                 {"txn_id":210,"txn_date":"2024-03-25","amount":199.99,"category":"general","jurisdiction_id":4}]}]
     },
     "questions":[
         {"id":"TX_Q01","category":"SELECT *","difficulty":1,
@@ -667,7 +669,6 @@ FALLBACK_PACKS["sales tax"] = {
          ]}
     ]
 }
-
 # --- Oil & gas wells ---
 FALLBACK_PACKS["oil and gas wells"] = {
     "schema":{
@@ -679,8 +680,7 @@ FALLBACK_PACKS["oil and gas wells"] = {
                  {"name":"well_name","type":"TEXT"},
                  {"name":"location","type":"TEXT"},
                  {"name":"status","type":"TEXT"},
-                 {"name":"depth","type":"INTEGER"}
-             ],
+                 {"name":"depth","type":"INTEGER"}],
              "fks":[],
              "rows":[
                  {"well_id":1,"well_name":"Alpha-1","location":"TX-TRV","status":"producing","depth":12000},
@@ -688,47 +688,40 @@ FALLBACK_PACKS["oil and gas wells"] = {
                  {"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}
-             ]},
+                 {"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"}
-             ],
+                 {"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"}
-             ]},
+                 {"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"}
-             ],
+                 {"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"]}
-             ],
+                 {"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"}
-             ]},
+                 {"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"}
-             ],
+                 {"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},
@@ -738,9 +731,7 @@ FALLBACK_PACKS["oil and gas wells"] = {
                  {"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}
-             ]}
-        ]
+                 {"prod_id":1008,"well_id":4,"month":"2024-02","oil_bbl":100,"gas_mcf":400}]}]
     },
     "questions":[
         {"id":"OG_Q01","category":"SELECT *","difficulty":1,
@@ -773,7 +764,6 @@ FALLBACK_PACKS["oil and gas wells"] = {
          ]}
     ]
 }
-
 # --- Marketing ---
 FALLBACK_PACKS["marketing"] = {
     "schema":{
@@ -782,23 +772,20 @@ FALLBACK_PACKS["marketing"] = {
             {"name":"channels","pk":["channel_id"],
              "columns":[
                  {"name":"channel_id","type":"INTEGER"},
-                 {"name":"channel_name","type":"TEXT"}
-             ],
+                 {"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"}
-             ]},
+                 {"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"}
-             ],
+                 {"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},
@@ -806,16 +793,14 @@ FALLBACK_PACKS["marketing"] = {
                  {"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}
-             ]},
+                 {"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"}
-             ],
+                 {"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},
@@ -823,16 +808,14 @@ FALLBACK_PACKS["marketing"] = {
                  {"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}
-             ]},
+                 {"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"}
-             ],
+                 {"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},
@@ -840,9 +823,7 @@ FALLBACK_PACKS["marketing"] = {
                  {"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}
-             ]}
-        ]
+                 {"lead_id":6,"campaign_id":106,"source":"FB","qualified":1,"revenue":500}]}]
     },
     "questions":[
         {"id":"M_Q01","category":"SELECT *","difficulty":1,
@@ -985,6 +966,13 @@ def _canon_question(q: Dict[str, Any]) -> Optional[Dict[str, Any]]:
         req_aliases = [a.strip() for a in re.split(r"[,\s]+", req_aliases) if a.strip()]
     elif not isinstance(req_aliases, list):
         req_aliases = []
+
+    # Soften ambiguous aggregation prompts so users know what to return
+    if cat == "Aggregation":
+        pl = (prompt or "").lower()
+        if ("for each" in pl or "per " in pl) and not re.search(r"`[^`]+`", prompt or ""):
+            prompt += "\n\n*Note:* Return the grouping column and the aggregate. If the wording says “for each …”, include entities with zero by using a LEFT JOIN."
+
     return {
         "id": str(q.get("id") or f"LLM_{int(time.time()*1000)}_{random.randint(100,999)}"),
         "category": cat,
@@ -1122,7 +1110,6 @@ def install_schema(con: sqlite3.Connection, schema: Dict[str,Any]):
 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:
-        # 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
@@ -1132,7 +1119,6 @@ def install_schema_and_prepare_questions(prev_domain: Optional[str], preferred_d
     schema, questions, info = bootstrap_domain_with_llm_or_fallback(prev_domain, preferred_domain)
     install_schema(CONN, schema)
     if not 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}
@@ -1159,7 +1145,6 @@ def choose_next_domain(prev_domain: Optional[str]) -> str:
     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"))
 
@@ -1250,17 +1235,27 @@ def detect_cartesian(con: sqlite3.Connection, sql: str, df_result: pd.DataFrame)
             return "Possible cartesian product: no join condition detected."
     return None
 
+# Column enforcement policy — relax unless prompt makes it explicit
 def should_enforce_columns(q: Dict[str, Any]) -> bool:
+    """
+    Enforce exact projection only when the prompt explicitly asks for columns,
+    or when DDL creates a new object where column shape matters.
+    """
     cat = (q.get("category") or "").strip()
-    if cat in ("SELECT columns", "Aggregation", "VIEW", "CTAS / SELECT INTO"):
+    if cat in ("SELECT columns", "VIEW", "CTAS / SELECT INTO"):
         return True
-    prompt = (q.get("prompt_md") or "").lower()
-    if re.search(r"`[^`]+`", q.get("prompt_md") or ""):
+
+    prompt = (q.get("prompt_md") or "")
+    prompt_l = prompt.lower()
+
+    # Signals that projection is explicitly requested
+    if re.search(r"`[^`]+`", prompt):                     # backticked names
         return True
-    if re.search(r"\((?:show|return|display)[^)]+\)", prompt):
+    if re.search(r"\((?:show|return|display)[^)]+\)", prompt_l):
         return True
-    if re.search(r"\b(show|return|display|select)\b[^.]{0,100}\b(columns?|fields?|name|title|price)\b", prompt):
+    if re.search(r"\b(show|return|display|select)\b[^.]{0,120}\b(columns?|fields?|name|title|price|count|sum|avg|max|min)\b", prompt_l):
         return True
+
     return False
 
 def _normalize_columns(df: pd.DataFrame) -> pd.DataFrame:
@@ -1363,13 +1358,19 @@ def answer_df(answer_sql: List[str]) -> Optional[pd.DataFrame]:
             continue
     return None
 
+def _canonical_expects_left_join(q: Dict[str, Any]) -> bool:
+    canon = " ".join(q.get("answer_sql", [])).lower()
+    return " left join " in canon
+
 def validate_answer(q: Dict[str,Any], student_sql: str, df_student: Optional[pd.DataFrame]) -> Tuple[bool, str]:
     df_expected = answer_df(q["answer_sql"])
     if df_expected is None:
         return (df_student is not None), f"**Explanation:** Your statement executed successfully for this task."
     if df_student is None:
         return False, f"**Explanation:** Expected data result differs."
-    if should_enforce_columns(q):
+
+    enforce_cols = should_enforce_columns(q)
+    if enforce_cols:
         ok, note = results_equal_or_superset(df_student, df_expected)
         if ok and note == "extra_columns":
             return True, "**Note:** You returned extra columns. The rows match; try selecting only the requested columns next time."
@@ -1377,9 +1378,11 @@ def validate_answer(q: Dict[str,Any], student_sql: str, df_student: Optional[pd.
             return True, "**Explanation:** Your result matches a canonical solution."
         return False, f"**Explanation:** Compare your result to a canonical solution."
     else:
-        ok = results_equal_rowcount_only(df_student, df_expected)
-        if ok:
+        # Projection not enforced → compare coverage (row counts)
+        if df_student.shape[0] == df_expected.shape[0]:
             return True, "**Explanation:** Columns weren’t specified for this task; row count matches the canonical answer."
+        if df_student.shape[0] < df_expected.shape[0] and _canonical_expects_left_join(q):
+            return False, "**Hint:** This task expects *every* entity (including those with zero related rows). Use a `LEFT JOIN` from the dimension table."
         return False, "**Explanation:** For this task we compared row counts (projection not enforced) and they didn’t match."
 
 def log_attempt(user_id: str, qid: str, category: str, correct: bool, sql_text: str,
@@ -1432,7 +1435,8 @@ def render_preview(sql_text: str, session: dict):
         return gr.update(value="", visible=False), draw_dynamic_erd(CURRENT_SCHEMA)
     hi_tables, hi_edges = sql_highlights(s, CURRENT_SCHEMA)
     erd = draw_dynamic_erd(CURRENT_SCHEMA, highlight_tables=hi_tables, highlight_edges=hi_edges)
-    return gr.update(value=f"**Preview:**\n\n```sql\n{s}\n```", visible=True), erd
+    html = highlight_sql_html(s)
+    return gr.update(value=html, visible=True), erd
 
 def submit_answer(sql_text: str, session: dict):
     if not session or "user_id" not in session or "q" not in session:
@@ -1561,14 +1565,28 @@ def preview_table(tbl: str):
         return pd.DataFrame([{"error": str(e)}])
 
 # -------------------- UI --------------------
+SQL_PREVIEW_STYLES = """
+<style>
+.sql-preview pre { white-space: pre-wrap; margin: 0; }
+.sql-preview code { font-family: ui-monospace, SFMono-Regular, Menlo, Consolas, "Liberation Mono", monospace; font-size: 13px; }
+.kw { font-weight: 600; }
+.kw-core { color: #1f6feb; }   /* SELECT, FROM, WHERE, etc. */
+.kw-join { color: #b80672; }   /* JOIN family */
+.kw-group { color: #107c41; }  /* GROUP BY / HAVING */
+.kw-order { color: #6f42c1; }  /* ORDER BY / LIMIT / OFFSET */
+.kw-ddl { color: #b26c12; }    /* CREATE/INSERT/UPDATE/DELETE/etc. */
+</style>
+"""
+
 with gr.Blocks(title="Adaptive SQL Trainer — Randomized Domains") as demo:
+    gr.Markdown(SQL_PREVIEW_STYLES)
     gr.Markdown(
         """
         # 🧪 Adaptive SQL Trainer — Randomized Domains (SQLite)
         - Uses **OpenAI** (if configured) to randomize a domain (bookstore, retail sales, wholesaler,
           sales tax, oil & gas wells, marketing), generate **3–4 tables** and **8–12** questions.
         - Practice `SELECT`, `WHERE`, `JOIN` (INNER/LEFT), **aliases**, **views**, and **CTAS / SELECT INTO**.
-        - **ERD highlights your JOINs** as you type; all FK edges remain visible in light gray.
+        - **ERD shows tables only** and bolds the ones referenced in your SQL.
         """
     )
 
@@ -1659,4 +1677,4 @@ with gr.Blocks(title="Adaptive SQL Trainer — Randomized Domains") as demo:
     )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()