81 lines
2.1 KiB
Python
81 lines
2.1 KiB
Python
from flask import Flask, render_template
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from model.brainmap import get_brainmap
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from model.journal import read_journal_entries
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from model.memory 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 context.context import load_context
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import json
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import os
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import time
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app = Flask(__name__)
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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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def load_loss_data():
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path = "data/logs/loss.log"
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if not os.path.exists(path):
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return []
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with open(path, "r", encoding="utf-8") as f:
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lines = f.readlines()
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return [float(line.strip().split(",")[1]) for line in lines[-50:]]
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def update_next_cycle(seconds):
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global next_cycle_time
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next_cycle_time = time.time() + seconds
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@app.route("/")
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def index():
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dreams = load_dreams()
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top_dreams = dreams[:5]
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memory_size = len(load_context())
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loss_data = load_loss_data()
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remaining = max(0, int(next_cycle_time - time.time()))
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return render_template("index.html",
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vocab_size=len(tokenizer.vocab),
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top_dreams=top_dreams,
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memory_size=memory_size,
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loss_data=loss_data,
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next_cycle=remaining)
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@app.route("/brainmap")
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def brainmap():
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map_data = get_brainmap()
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nodes = []
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links = []
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for word, connections in map_data.items():
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nodes.append({"id": word})
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for linked_word, weight in connections.items():
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links.append({
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"source": word,
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"target": linked_word,
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"value": weight
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})
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return render_template("brainmap.html", nodes=json.dumps(nodes), links=json.dumps(links))
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@app.route("/journal")
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def journal():
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entries = read_journal_entries()
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return render_template("journal.html", entries=entries)
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@app.route("/concepts")
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def concepts():
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clusters = cluster_vocab(n_clusters=10)
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return render_template("concepts.html", clusters=clusters)
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def run_dashboard():
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app.run(host="0.0.0.0", port=5000, debug=False, use_reloader=False)
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