Upload folder using huggingface_hub

Dockerfile

@@ -1,10 +1,7 @@
 FROM python:3.11-slim
-RUN useradd -m -u 1000 user
+RUN apt-get update && apt-get install -y curl && rm -rf /var/lib/apt/lists/*
 WORKDIR /app
-RUN mkdir -p /app/data && chown -R user:user /app
-COPY brain.py /app/brain.py
-RUN chmod +x /app/brain.py
-USER user
+COPY brain.py /app/
 EXPOSE 7860
-HEALTHCHECK --interval=30s --timeout=10s --start-period=10s CMD curl -sf http://localhost:7860/health || exit 1
+HEALTHCHECK --interval=30s --timeout=10s CMD curl -sf http://localhost:7860/health || exit 1
 CMD ["python3", "/app/brain.py"]

README.md

@@ -1,11 +1,7 @@
 ---
 title: MEGAMIND VECTOR
 emoji: 🧠
-colorFrom: purple
-colorTo: blue
 sdk: docker
-pinned: false
 ---
-
 # MEGAMIND VECTOR
-MEGAMIND Federation Node - Learning AI tensors and patterns
+Federation Node

brain.py

@@ -1,16 +1,10 @@
 #!/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()}
-
+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)
@@ -18,45 +12,29 @@ def init_db():
     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)
+        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):
-        p = urlparse(self.path).path
         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', '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)
+        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}] starting on port {PORT}')
-    init_db()
-    print(f'Loaded: {stats["tensors"]} tensors, {stats["patterns"]} patterns')
+    print(f'MEGAMIND Brain [{NODE_ID}]'); init_db()
     HTTPServer(('0.0.0.0', PORT), Handler).serve_forever()