Python brain - no CGO needed

Dockerfile

@@ -1,18 +1,10 @@
 FROM python:3.11-slim
-
-RUN apt-get update && apt-get install -y --no-install-recommends ca-certificates curl && rm -rf /var/lib/apt/lists/*
-
-RUN useradd -m -u 1000 megamind
+RUN useradd -m -u 1000 user
 WORKDIR /app
-RUN mkdir -p /app/data && chown -R megamind:megamind /app
-
-COPY brain /app/brain
-COPY start.sh /app/start.sh
-RUN chmod +x /app/brain /app/start.sh
-
-USER megamind
+RUN mkdir -p /app/data && chown -R user:user /app
+COPY brain.py /app/brain.py
+RUN chmod +x /app/brain.py
+USER user
 EXPOSE 7860
-
-HEALTHCHECK --interval=30s --timeout=10s --start-period=60s --retries=3 CMD curl -sf http://localhost:7860/health || exit 1
-
-CMD ["/app/start.sh"]
+HEALTHCHECK --interval=30s --timeout=10s --start-period=10s CMD curl -sf http://localhost:7860/health || exit 1
+CMD ["python3", "/app/brain.py"]

README.md

@@ -8,4 +8,4 @@ pinned: false
 ---
 
 # MEGAMIND VECTOR
-Specialty: mathematics
+MEGAMIND Federation Node - Learning AI tensors and patterns

brain.py

@@ -0,0 +1,62 @@
+#!/usr/bin/env python3
+"""MEGAMIND Brain - Python Edition for HuggingFace Spaces"""
+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 = os.environ.get('DATA_DIR', './data')
+NODE_ID = os.environ.get('SPACE_ID', 'hf-brain')
+
+db = None
+stats = {'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()
+    # Count existing
+    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'], 'queries': stats['queries'], 'uptime': f"{time.time()-stats['start']:.0f}s"})
+        elif p == '/': self.json({'name': 'MEGAMIND Brain', 'node': NODE_ID, 'version': '1.0-py'})
+        else: self.send_error(404)
+    def do_POST(self):
+        p = urlparse(self.path).path
+        body = self.rfile.read(int(self.headers.get('Content-Length', 0))).decode()
+        data = json.loads(body) if body else {}
+        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', 'hash': h})
+        elif p == '/learn-tensor':
+            db.execute('INSERT INTO tensors (name, source, meta, ts) VALUES (?, ?, ?, ?)',
+                (data.get('name',''), data.get('source',''), json.dumps(data.get('metadata',{})), time.time()))
+            db.commit(); stats['tensors'] += 1
+            self.json({'status': 'learned'})
+        elif p == '/query':
+            stats['queries'] += 1
+            r = [row[0][:500] for row in db.execute('SELECT content FROM chunks WHERE content LIKE ? LIMIT 10', (f"%{data.get('query','')}%",))]
+            self.json({'results': r, 'count': len(r)})
+        else: self.send_error(404)
+    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}] starting on port {PORT}')
+    init_db()
+    print(f'Loaded: {stats["tensors"]} tensors, {stats["patterns"]} patterns')
+    HTTPServer(('0.0.0.0', PORT), Handler).serve_forever()