#!/usr/bin/env python3 import os, json, sqlite3, hashlib, time from http.server import HTTPServer, BaseHTTPRequestHandler from urllib.parse import urlparse PORT = int(os.environ.get('PORT', 7860)) DATA_DIR, NODE_ID = './data', os.environ.get('SPACE_ID', 'hf-brain') db, stats = None, {'tensors': 0, 'patterns': 0, 'queries': 0, 'start': time.time()} def init_db(): global db os.makedirs(DATA_DIR, exist_ok=True) db = sqlite3.connect(f'{DATA_DIR}/brain.db', check_same_thread=False) db.execute('CREATE TABLE IF NOT EXISTS chunks (id INTEGER PRIMARY KEY, hash TEXT UNIQUE, content TEXT, ts REAL)') db.execute('CREATE TABLE IF NOT EXISTS tensors (id INTEGER PRIMARY KEY, name TEXT, source TEXT, meta TEXT, ts REAL)') db.commit() stats['patterns'] = db.execute('SELECT COUNT(*) FROM chunks').fetchone()[0] stats['tensors'] = db.execute('SELECT COUNT(*) FROM tensors').fetchone()[0] class Handler(BaseHTTPRequestHandler): def log_message(self, *a): pass def do_GET(self): p = urlparse(self.path).path if p == '/health': self.json({'status': 'healthy'}) elif p == '/status': self.json({'node': NODE_ID, 'status': 'online', 'tensors_learned': stats['tensors'], 'patterns_learned': stats['patterns']}) else: self.json({'name': 'MEGAMIND', 'node': NODE_ID}) def do_POST(self): body = self.rfile.read(int(self.headers.get('Content-Length', 0))).decode() data = json.loads(body) if body else {} p = urlparse(self.path).path if p == '/learn': c = data.get('content', '')[:10000] h = hashlib.sha256(c.encode()).hexdigest()[:16] db.execute('INSERT OR IGNORE INTO chunks (hash, content, ts) VALUES (?, ?, ?)', (h, c, time.time())) db.commit(); stats['patterns'] += 1 self.json({'status': 'learned'}) else: self.json({}) def json(self, d): self.send_response(200); self.send_header('Content-Type', 'application/json'); self.end_headers() self.wfile.write(json.dumps(d).encode()) if __name__ == '__main__': print(f'MEGAMIND Brain [{NODE_ID}]'); init_db() HTTPServer(('0.0.0.0', PORT), Handler).serve_forever()