Ultralytics MCP Server

import streamlit as st import subprocess import torch import os from pathlib import Path import pandas as pd # Page configuration st.set_page_config( page_title="DENTEX AI Platform", page_icon="🦷", layout="wide", initial_sidebar_state="expanded" ) def get_nvidia_smi_info(): """Get live GPU information from nvidia-smi""" try: result = subprocess.run( ["nvidia-smi", "--query-gpu=name,temperature.gpu,utilization.gpu,utilization.memory,memory.used,memory.total,power.draw,power.limit", "--format=csv,noheader,nounits"], capture_output=True, text=True ) if result.returncode == 0: gpu_data = result.stdout.strip().split(', ') return { 'name': gpu_data[0], 'temperature': int(gpu_data[1]), 'gpu_utilization': int(gpu_data[2]), 'memory_utilization': int(gpu_data[3]), 'memory_used': int(gpu_data[4]), 'memory_total': int(gpu_data[5]), 'power_draw': float(gpu_data[6]), 'power_limit': float(gpu_data[7]), 'available': True } except Exception as e: pass # Fallback to torch try: if torch.cuda.is_available(): gpu_name = torch.cuda.get_device_name(0) gpu_memory = torch.cuda.get_device_properties(0).total_memory gpu_memory_gb = gpu_memory / (1024**3) torch.cuda.empty_cache() gpu_memory_allocated = torch.cuda.memory_allocated(0) gpu_memory_available = gpu_memory - gpu_memory_allocated gpu_memory_available_gb = gpu_memory_available / (1024**3) return { 'name': gpu_name, 'memory_total_gb': gpu_memory_gb, 'memory_available_gb': gpu_memory_available_gb, 'available': True, 'torch_fallback': True } else: return {'name': 'No GPU Available', 'available': False} except Exception as e: return {'name': f'GPU Error: {str(e)}', 'available': False} def check_training_status(): """Check if training is currently running""" try: result = subprocess.run( ["bash", "-c", "ps aux | grep -E 'train_script|model.train' | grep -v grep | wc -l"], cwd="/ultralytics", capture_output=True, text=True ) return int(result.stdout.strip()) > 0 except: return False def get_system_stats(): """Get comprehensive system statistics""" try: # CPU Usage cpu_result = subprocess.run( ["bash", "-c", "top -bn1 | grep 'Cpu(s)' | awk '{print $2}' | sed 's/%us,//'"], capture_output=True, text=True ) cpu_usage = cpu_result.stdout.strip() if cpu_result.returncode == 0 else "N/A" # Memory Usage mem_result = subprocess.run( ["bash", "-c", "free -h | grep '^Mem:' | awk '{print $3 \"/\" $2}'"], capture_output=True, text=True ) memory_usage = mem_result.stdout.strip() if mem_result.returncode == 0 else "N/A" # Disk Usage disk_result = subprocess.run( ["bash", "-c", "df -h / | tail -1 | awk '{print $3 \"/\" $2 \" (\" $5 \")\"}'"], capture_output=True, text=True ) disk_usage = disk_result.stdout.strip() if disk_result.returncode == 0 else "N/A" return { 'cpu_usage': cpu_usage, 'memory_usage': memory_usage, 'disk_usage': disk_usage } except: return { 'cpu_usage': "N/A", 'memory_usage': "N/A", 'disk_usage': "N/A" } def get_latest_training_results(): """Get latest training results""" try: runs_dir = Path("/ultralytics/runs/detect") if not runs_dir.exists(): return None # Find latest training training_dirs = [d for d in runs_dir.iterdir() if d.is_dir() and (d.name.startswith("dentex_") or d.name.startswith("custom_"))] if not training_dirs: return None latest_dir = max(training_dirs, key=lambda x: x.stat().st_mtime) results_file = latest_dir / "results.csv" if results_file.exists(): df = pd.read_csv(results_file) return { 'training_dir': str(latest_dir.name), 'epochs_completed': len(df), 'current_metrics': df.iloc[-1].to_dict() if len(df) > 0 else {}, } except Exception as e: return None def get_workspace_models(): """Get list of workspace models""" try: workspace_models = Path("/workspace/trained_models") if workspace_models.exists(): model_files = list(workspace_models.glob("*.pt")) return len(model_files) return 0 except: return 0 # Header st.markdown(""" <div style="background: linear-gradient(90deg, #0066CC 0%, #004499 100%); color: white; padding: 2rem; border-radius: 15px; margin-bottom: 2rem; text-align: center;"> <h1>🦷 DENTEX AI Platform</h1> <p style="font-size: 1.2em;">Professional Dental X-Ray Detection & Training Platform</p> </div> """, unsafe_allow_html=True) # Navigation st.markdown("### 🚀 Quick Navigation") col1, col2 = st.columns(2) with col1: if st.button("🎯 **Train Models**", use_container_width=True, type="primary"): st.switch_page("pages/train.py") st.markdown(""" <div style="background: #f0f8ff; border: 1px solid #0066CC; padding: 1rem; border-radius: 8px; margin-top: 0.5rem;"> <p style="margin: 0; color: #000;"><strong>Model Training</strong></p> <p style="margin: 0; color: #666; font-size: 0.9em;">Train YOLO models on DENTEX datasets or custom data</p> </div> """, unsafe_allow_html=True) with col2: if st.button("🔍 **Inference & Detection**", use_container_width=True, type="secondary"): st.switch_page("pages/inference.py") st.markdown(""" <div style="background: #f0fff0; border: 1px solid #28a745; padding: 1rem; border-radius: 8px; margin-top: 0.5rem;"> <p style="margin: 0; color: #000;"><strong>Model Inference</strong></p> <p style="margin: 0; color: #666; font-size: 0.9em;">Run detection on images using trained models</p> </div> """, unsafe_allow_html=True) st.markdown("---") # System Status Dashboard st.markdown("### 📊 System Status Dashboard") # Get system information gpu_info = get_nvidia_smi_info() system_stats = get_system_stats() training_active = check_training_status() latest_training = get_latest_training_results() model_count = get_workspace_models() # Status Overview col1, col2, col3, col4 = st.columns(4) with col1: if training_active: st.metric("🔄 Training Status", "ACTIVE", delta="Running") else: st.metric("⏸️ Training Status", "IDLE", delta="Ready") with col2: if gpu_info['available']: if 'torch_fallback' in gpu_info: st.metric("💾 GPU Memory", f"{gpu_info['memory_available_gb']:.1f} GB", delta="Available") else: memory_usage_percent = (gpu_info['memory_used'] / gpu_info['memory_total']) * 100 st.metric("💾 GPU Memory", f"{memory_usage_percent:.1f}%", delta="Used") else: st.metric("💾 GPU Memory", "N/A", delta="No GPU") with col3: st.metric("🤖 Trained Models", str(model_count), delta="In Workspace") with col4: if latest_training: st.metric("📈 Last Training", f"{latest_training['epochs_completed']} epochs", delta="Completed") else: st.metric("📈 Last Training", "None", delta="No history") st.markdown("---") # Detailed System Information col1, col2 = st.columns([2, 1]) with col1: st.subheader("🖥️ Hardware Status") # GPU Information if gpu_info['available']: if 'torch_fallback' in gpu_info: st.markdown(f""" <div style="background: #f0f8ff; border: 2px solid #0066CC; color: #000000; padding: 1rem; border-radius: 8px; margin-bottom: 1rem;"> <h4 style="color: #0066CC; margin-top: 0;">🖥️ GPU Status</h4> <p style="color: #000000; margin: 0.3rem 0;"><strong>Device:</strong> {gpu_info['name']}</p> <p style="color: #000000; margin: 0.3rem 0;"><strong>Total VRAM:</strong> {gpu_info['memory_total_gb']:.1f} GB</p> <p style="color: #000000; margin: 0.3rem 0;"><strong>Available:</strong> {gpu_info['memory_available_gb']:.1f} GB</p> <p style="color: #28a745; margin: 0.3rem 0;"><strong>Status:</strong> ✅ Ready for Training</p> </div> """, unsafe_allow_html=True) else: memory_usage_percent = (gpu_info['memory_used'] / gpu_info['memory_total']) * 100 power_usage_percent = (gpu_info['power_draw'] / gpu_info['power_limit']) * 100 # Temperature color coding temp_color = "#28a745" if gpu_info['temperature'] < 70 else "#ffc107" if gpu_info['temperature'] < 80 else "#dc3545" st.markdown(f""" <div style="background: #f0f8ff; border: 2px solid #28a745; color: #000000; padding: 1rem; border-radius: 8px; margin-bottom: 1rem;"> <h4 style="color: #28a745; margin-top: 0;">🖥️ GPU Status (Live)</h4> <p style="color: #000000; margin: 0.3rem 0;"><strong>Device:</strong> {gpu_info['name']}</p> <p style="color: {temp_color}; margin: 0.3rem 0;"><strong>Temperature:</strong> {gpu_info['temperature']}°C</p> <p style="color: #000000; margin: 0.3rem 0;"><strong>GPU Load:</strong> {gpu_info['gpu_utilization']}%</p> <p style="color: #000000; margin: 0.3rem 0;"><strong>Memory:</strong> {gpu_info['memory_used']} MB / {gpu_info['memory_total']} MB ({memory_usage_percent:.1f}%)</p> <p style="color: #000000; margin: 0.3rem 0;"><strong>Power:</strong> {gpu_info['power_draw']:.1f}W / {gpu_info['power_limit']:.1f}W ({power_usage_percent:.1f}%)</p> </div> """, unsafe_allow_html=True) else: st.markdown(f""" <div style="background: #fff3cd; border: 2px solid #ffc107; color: #000000; padding: 1rem; border-radius: 8px; margin-bottom: 1rem;"> <h4 style="color: #856404; margin-top: 0;">⚠️ GPU Status</h4> <p style="color: #000000; margin: 0.3rem 0;">{gpu_info['name']}</p> <p style="color: #dc3545; margin: 0.3rem 0;"><strong>Status:</strong> ❌ GPU Not Available</p> </div> """, unsafe_allow_html=True) # System Resources st.markdown(f""" <div style="background: #f8f9fa; border: 2px solid #6c757d; color: #000000; padding: 1rem; border-radius: 8px; margin-bottom: 1rem;"> <h4 style="color: #495057; margin-top: 0;">💻 System Resources</h4> <p style="color: #000000; margin: 0.3rem 0;"><strong>CPU Usage:</strong> {system_stats['cpu_usage']}</p> <p style="color: #000000; margin: 0.3rem 0;"><strong>Memory:</strong> {system_stats['memory_usage']}</p> <p style="color: #000000; margin: 0.3rem 0;"><strong>Disk:</strong> {system_stats['disk_usage']}</p> </div> """, unsafe_allow_html=True) with col2: st.subheader("📈 Quick Stats") # Training status if training_active: st.success("🔄 **Training Active**") if latest_training: st.info(f"📊 **Current:** {latest_training['training_dir']}") st.info(f"🔢 **Epochs:** {latest_training['epochs_completed']}") if latest_training['current_metrics']: metrics = latest_training['current_metrics'] st.info(f"📈 **mAP50:** {metrics.get('metrics/mAP50(B)', 0):.3f}") else: st.info("⏸️ **No Active Training**") st.markdown("---") # Available datasets st.subheader("📊 Available Data") # DENTEX datasets dentex_path = Path("/DENTEX/YOLO_MultiLevel_Datasets") if dentex_path.exists(): dentex_count = len([d for d in dentex_path.iterdir() if d.is_dir() and d.name.startswith("YOLO_")]) st.text(f"🦷 DENTEX Datasets: {dentex_count}") else: st.text("🦷 DENTEX Datasets: 0") # Custom datasets custom_path = Path("/ultralytics/custom_datasets") if custom_path.exists(): custom_count = len([d for d in custom_path.iterdir() if d.is_dir()]) st.text(f"📁 Custom Datasets: {custom_count}") else: st.text("📁 Custom Datasets: 0") st.text(f"🤖 Workspace Models: {model_count}") # Auto-refresh every 30 seconds if 'refresh_counter' not in st.session_state: st.session_state.refresh_counter = 0 st.session_state.refresh_counter += 1 if st.session_state.refresh_counter % 30 == 0: st.rerun() # Footer st.markdown("---") st.markdown(""" <div style="text-align: center; color: #666; padding: 1rem;"> <p>🦷 DENTEX AI Platform - Professional Dental Detection with Real-Time Monitoring</p> <p style="font-size: 0.9em;">Live GPU monitoring • Custom dataset support • Workspace model management</p> </div> """, unsafe_allow_html=True) # Auto-refresh button col1, col2, col3 = st.columns([1, 1, 1]) with col2: if st.button("🔄 Refresh Dashboard", use_container_width=True): st.rerun()