fixed the dashboard

dashboard/dashboard.py

@@ -60,19 +60,34 @@ def update_next_cycle(seconds):
 
 
 def get_status_summary():
-    progress = load_progress()
+    progress_data = load_progress()
-    books = get_books()
+    progress = progress_data.get("progress", {})
-    current_book = books[0] if books else None
+    current_book = None
-    current_line = progress.get(current_book, 0)
+    current_line = 0
+
+    # Find the book with the highest progress (e.g., currently being read)
+    if progress:
+        completed = set(progress_data.get("completed", []))
+        in_progress = {k: v for k, v in progress.items() if k not in completed}
+        if in_progress:
+            current_book = max(in_progress.items(), key=lambda x: x[1])[0]
+            current_line = in_progress[current_book]
+        current_line = progress[current_book]
+
     total_lines = 1
     if current_book:
-        with open(f"books/{current_book}", "r", encoding="utf-8") as f:
+        book_path = os.path.join("data", "books", current_book)
-            total_lines = len(f.readlines())
+        if os.path.exists(book_path):
+            with open(book_path, "r", encoding="utf-8") as f:
+                total_lines = len(f.readlines())
+        else:
+            current_book = None
+            current_line = 0
 
     return {
         "current_book": current_book,
         "current_line": current_line,
-        "percent_done": round((current_line / total_lines) * 100, 2),
+        "percent_done": round((current_line / total_lines) * 100, 2) if total_lines > 0 else 0,
         "memory_size": len(load_context()),
         "vocab_size": get_vocab_size(),
         "brainmap_size": len(get_brainmap()),
@@ -104,26 +119,14 @@ def growth():
 
 @app.route("/brainmap")
 def brainmap():
-    map_data = get_brainmap()
+    try:
-
+        with open("data/memory/brainmap_cache.json", "r", encoding="utf-8") as f:
-    nodes = []
+            cached = json.load(f)
-    links = []
+            nodes = cached.get("nodes", [])
-    MIN_LINK_WEIGHT = 2  # only show links seen at least 2 times
+            links = cached.get("links", [])
-    seen_words = set()
+    except Exception as e:
-
+        print(f"[Dashboard] Failed to load brainmap cache: {e}")
-    for word, connections in map_data.items():
+        nodes, links = [], []
-        for linked_word, weight in connections.items():
-            if weight >= MIN_LINK_WEIGHT:
-                links.append({
-                    "source": word,
-                    "target": linked_word,
-                    "value": weight
-                })
-                seen_words.add(word)
-                seen_words.add(linked_word)
-
-    for word in seen_words:
-        nodes.append({"id": word})
 
     return render_template("brainmap.html", nodes=json.dumps(nodes), links=json.dumps(links))
 

model/brainmap.py

@@ -3,7 +3,8 @@ import json
 import os
 from utils.unicleaner import clean_unicode
 
-BRAINMAP_PATH = "data/memory/brainmap.json"
+BRAINMAP_PATH = "data/memory/brainmap.json"         # actual connection data
+BRAINMAP_CACHE_PATH = "data/memory/brainmap_cache.json"  # for dashboard rendering only
 brainmap = {}
 
 MAX_CONNECTIONS = 50  # Max neighbors to keep per word
@@ -96,3 +97,35 @@ def prune_brainmap(min_neighbors=2, min_strength=2):
 
 def get_brainmap():
     return brainmap
+
+
+def refresh_brainmap_cache(min_weight=5, max_nodes=300):
+    map_data = get_brainmap()
+    links = []
+    seen_words = set()
+
+    for word, connections in map_data.items():
+        if not isinstance(connections, dict):
+            print(f"[Brainmap] Skipping corrupted entry: {word} => {type(connections)}")
+            continue
+
+        for linked_word, weight in connections.items():
+            if weight >= min_weight:
+                links.append({
+                    "source": word,
+                    "target": linked_word,
+                    "value": weight
+                })
+                seen_words.add(word)
+                seen_words.add(linked_word)
+
+    nodes = [{"id": word} for word in seen_words]
+
+    if len(nodes) > max_nodes:
+        nodes = nodes[:max_nodes]
+        node_set = {n["id"] for n in nodes}
+        links = [l for l in links if l["source"] in node_set and l["target"] in node_set]
+
+    os.makedirs("data/memory", exist_ok=True)
+    with open(BRAINMAP_CACHE_PATH, "w", encoding="utf-8") as f:
+        json.dump({"nodes": nodes, "links": links}, f, indent=2)

model/trainer.py

@@ -2,7 +2,7 @@ import torch
 import time
 from model.dynamic_expand import expand_model_if_needed, _last_expansion_time
 from model.brain_state import model, tokenizer, DEVICE, loss_fn, optimizer, scheduler
-from model.brainmap import add_to_brainmap
+from model.brainmap import add_to_brainmap, refresh_brainmap_cache
 from model.journal import record_to_journal
 from context.context import add_to_context, get_recent_context
 
@@ -65,6 +65,7 @@ async def train_on_message(text: str, source: str = "user"):
 
     add_to_brainmap(augmented_text.split())
     add_to_context(text, source=source)
+    refresh_brainmap_cache()
 
     record_to_journal({
         "timestamp": time.time(),