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Dockerfile

@@ -1,26 +1,7 @@
-FROM alpine:3.19
-
-# Install dependencies
-RUN apk add --no-cache ca-certificates curl iptables ip6tables iproute2 bash
-
-# Install Tailscale
-RUN curl -fsSL https://pkgs.tailscale.com/stable/tailscale_1.56.1_amd64.tgz | tar xz -C /usr/local/bin --strip-components=1
-
-# Create user
-RUN adduser -D -u 1000 megamind
-RUN mkdir -p /app/data /var/run/tailscale /var/lib/tailscale
-RUN chown -R megamind:megamind /app /var/run/tailscale /var/lib/tailscale
-
+FROM python:3.11-slim
+RUN apt-get update && apt-get install -y curl && rm -rf /var/lib/apt/lists/*
 WORKDIR /app
-
-# Copy binary and config
-COPY megamind /app/megamind
-COPY config.json /app/config.json
-COPY startup.sh /app/startup.sh
-
-RUN chmod +x /app/megamind /app/startup.sh
-
-# HF Spaces uses port 7860
+COPY brain.py /app/
 EXPOSE 7860
-
-CMD ["/app/startup.sh"]
+HEALTHCHECK --interval=30s --timeout=10s CMD curl -sf http://localhost:7860/health || exit 1
+CMD ["python3", "/app/brain.py"]

README.md

@@ -1,16 +1,7 @@
 ---
 title: MEGAMIND HF-LEARNER
 emoji: 🧠
-colorFrom: purple
-colorTo: blue
 sdk: docker
-pinned: false
 ---
-
 # MEGAMIND HF-LEARNER
-
-Part of the MEGAMIND distributed AGI federation.
-
-**Specialty:** machine learning
-
-This mind connects to the federation via NATS and contributes to collective intelligence queries.
+Federation Node

brain.py

@@ -0,0 +1,40 @@
+#!/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()