Updated the brainmap
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6067337dc8
commit
c082a9882b
@ -3,19 +3,23 @@ import os
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import time
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from typing import List
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CONTEXT_FILE = "data/memory/context.json"
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CONTEXT_PATH = "data/memory/context.json"
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MAX_MEMORY = 100
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def load_context() -> List[dict]:
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if os.path.exists(CONTEXT_FILE):
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with open(CONTEXT_FILE, "r", encoding="utf-8") as f:
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def load_context():
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if not os.path.exists(CONTEXT_PATH):
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return []
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try:
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with open(CONTEXT_PATH, "r", encoding="utf-8") as f:
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return json.load(f)
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return []
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except json.JSONDecodeError:
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print("[Context] Corrupted context.json. Returning empty list.")
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return []
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def save_context(mem: List[dict]):
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with open(CONTEXT_FILE, "w", encoding="utf-8") as f:
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with open(CONTEXT_PATH, "w", encoding="utf-8") as f:
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json.dump(mem[-MAX_MEMORY:], f, indent=2)
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@ -9,13 +9,12 @@ from model.journal import read_journal_entries
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from model.dreams import load_dreams
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from model.tokenizer import Tokenizer
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from model.abstraction import cluster_vocab
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from model.dreams import load_dreams
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from model.scheduler import get_time_until_next_action, get_next_action_label
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from context.context import load_context
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from reader.reader import get_books, load_progress
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app = Flask(__name__)
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app = Flask(__name__, static_folder="static")
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tokenizer = Tokenizer()
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next_cycle_time = time.time() + 900 # Example: 15 minutes from now
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@ -128,7 +127,7 @@ def brainmap():
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print(f"[Dashboard] Failed to load brainmap cache: {e}")
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nodes, links = [], []
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return render_template("brainmap.html", nodes=json.dumps(nodes), links=json.dumps(links))
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return render_template("brainmap.html", nodes=nodes, links=links)
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@app.route("/journal")
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@ -2,16 +2,14 @@
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<meta http-equiv="refresh" content="60">
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<title>Ruby's Brain Map</title>
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<script src="https://cdn.jsdelivr.net/npm/force-graph"></script>
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<style>
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body {
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margin: 0;
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background-color: #121212;
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color: #e0e0e0;
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margin: 0;
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padding: 0;
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overflow: hidden;
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font-family: 'Segoe UI', Tahoma, sans-serif;
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}
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#graph {
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width: 100vw;
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@ -21,21 +19,37 @@
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position: absolute;
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top: 10px;
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left: 10px;
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background-color: #1e1e1e;
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padding: 10px;
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background: rgba(0, 0, 0, 0.7);
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padding: 8px 14px;
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border-radius: 8px;
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z-index: 1000;
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}
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.nav a {
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color: #e0e0e0;
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margin-right: 20px;
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margin-right: 15px;
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text-decoration: none;
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font-weight: bold;
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}
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#searchBox {
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position: absolute;
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top: 10px;
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right: 10px;
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z-index: 1000;
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padding: 6px 10px;
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font-size: 14px;
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border-radius: 6px;
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border: none;
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outline: none;
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background-color: #1e1e1e;
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color: #fff;
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width: 200px;
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}
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</style>
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</head>
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<body>
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<div class="nav">
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<a href="/">🏠 Home</a>
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<a href="/">📊 Dashboard</a>
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<a href="/journal">📓 Journal</a>
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<a href="/concepts">🧠 Concepts</a>
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<a href="/brainmap">🕸️ Brain Map</a>
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@ -44,21 +58,55 @@
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<a href="/health">❤️ Health</a>
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</div>
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<input type="text" id="searchBox" placeholder="Search word...">
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<div id="graph"></div>
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<script>
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const graphData = {
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nodes: {{ nodes | safe }},
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links: {{ links | safe }}
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};
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const graphData = {
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nodes: {{ nodes | safe }},
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links: {{ links | safe }}
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};
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const Graph = ForceGraph()(document.getElementById('graph'))
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.graphData(graphData)
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.nodeAutoColorBy('id')
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.nodeLabel('id')
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.linkWidth(link => Math.log(link.value + 1))
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.linkColor(() => 'rgba(255,255,255,0.2)')
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.backgroundColor('#121212');
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const degreeMap = {};
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graphData.links.forEach(link => {
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degreeMap[link.source] = (degreeMap[link.source] || 0) + 1;
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degreeMap[link.target] = (degreeMap[link.target] || 0) + 1;
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});
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const Graph = ForceGraph()(document.getElementById("graph"))
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.graphData(graphData)
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.nodeId("id")
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.nodeLabel(node => `${node.id} (${degreeMap[node.id] || 0} links)`)
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.nodeAutoColorBy("group")
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.nodeVal(node => Math.max(2, degreeMap[node.id] || 1))
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.linkWidth(link => Math.log(link.value + 1))
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.linkColor(() => "rgba(255,255,255,0.2)")
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.backgroundColor("#121212");
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// Search filter
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document.getElementById("searchBox").addEventListener("input", e => {
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const search = e.target.value.toLowerCase();
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Graph.nodeVisibility(node => node.id.toLowerCase().includes(search));
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});
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// Click-to-highlight
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Graph.onNodeClick(node => {
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const neighbors = new Set();
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graphData.links.forEach(link => {
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if (link.source === node.id) neighbors.add(link.target);
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else if (link.target === node.id) neighbors.add(link.source);
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});
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Graph.nodeColor(n =>
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n.id === node.id ? "#ffff00" :
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neighbors.has(n.id) ? "#ffa500" : "#333"
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);
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Graph.linkColor(l =>
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l.source === node.id || l.target === node.id ? "#ffffff" : "rgba(255,255,255,0.05)"
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);
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});
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</script>
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</body>
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@ -1,6 +1,9 @@
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import re
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import json
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import os
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import shutil
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from sklearn.cluster import KMeans
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import numpy as np
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from utils.unicleaner import clean_unicode
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BRAINMAP_PATH = "data/memory/brainmap.json" # actual connection data
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@ -99,7 +102,12 @@ def get_brainmap():
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return brainmap
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def refresh_brainmap_cache(min_weight=5, max_nodes=300):
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def refresh_brainmap_cache(min_weight=2, max_nodes=300):
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"""
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Generates a clustered brainmap view and writes to:
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- data/memory/brainmap_cache.json (master copy)
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- static/brainmap.json (served to frontend)
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"""
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map_data = get_brainmap()
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links = []
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seen_words = set()
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@ -108,7 +116,6 @@ def refresh_brainmap_cache(min_weight=5, max_nodes=300):
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if not isinstance(connections, dict):
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print(f"[Brainmap] Skipping corrupted entry: {word} => {type(connections)}")
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continue
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for linked_word, weight in connections.items():
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if weight >= min_weight:
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links.append({
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@ -119,13 +126,44 @@ def refresh_brainmap_cache(min_weight=5, max_nodes=300):
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seen_words.add(word)
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seen_words.add(linked_word)
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nodes = [{"id": word} for word in seen_words]
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node_set = {link["source"] for link in links} | {link["target"] for link in links}
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nodes = sorted(node_set)
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if len(nodes) > max_nodes:
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nodes = nodes[:max_nodes]
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node_set = {n["id"] for n in nodes}
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node_set = set(nodes)
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links = [l for l in links if l["source"] in node_set and l["target"] in node_set]
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index_lookup = {word: i for i, word in enumerate(nodes)}
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word_vectors = []
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for word in nodes:
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vec = np.zeros(len(nodes), dtype=np.float32)
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connections = map_data.get(word, {})
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for other, strength in connections.items():
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if other in index_lookup:
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vec[index_lookup[other]] = strength
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word_vectors.append(vec)
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if len(word_vectors) < 2:
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print("[Brainmap] Not enough nodes to cluster.")
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return
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kmeans = KMeans(n_clusters=min(8, len(nodes)), n_init="auto")
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labels = kmeans.fit_predict(word_vectors)
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clustered_nodes = [{"id": word, "group": int(label)} for word, label in zip(nodes, labels)]
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output = {
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"nodes": clustered_nodes,
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"links": links
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}
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os.makedirs("data/memory", exist_ok=True)
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with open(BRAINMAP_CACHE_PATH, "w", encoding="utf-8") as f:
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json.dump({"nodes": nodes, "links": links}, f, indent=2)
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os.makedirs("static", exist_ok=True)
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cache_path = "data/memory/brainmap_cache.json"
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static_path = "static/brainmap.json"
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with open(cache_path, "w", encoding="utf-8") as f:
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json.dump(output, f, indent=2)
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shutil.copyfile(cache_path, static_path)
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# print(f"[Brainmap] Cache written to {cache_path} and copied to {static_path}")
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@ -4,7 +4,7 @@ import os
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import time
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from model.tokenizer import VOCAB_PATH
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from model.dreams import DREAM_LOG_PATH
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from context.context import CONTEXT_FILE
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from context.context import CONTEXT_PATH
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from model.brainmap import load_brainmap, save_brainmap
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CLEANUP_LOG = "data/logs/cleanup.log"
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@ -59,15 +59,15 @@ def cleanup_dreams():
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def cleanup_context():
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if not os.path.exists(CONTEXT_FILE):
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if not os.path.exists(CONTEXT_PATH):
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return
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with open(CONTEXT_FILE, "r", encoding="utf-8") as f:
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with open(CONTEXT_PATH, "r", encoding="utf-8") as f:
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context = json.load(f)
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filtered = context[-100:]
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with open(CONTEXT_FILE, "w", encoding="utf-8") as f:
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with open(CONTEXT_PATH, "w", encoding="utf-8") as f:
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json.dump(filtered, f, indent=2)
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if len(filtered) < len(context):
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