diff --git a/app.py b/app.py index fd8938b..9e98ad0 100644 --- a/app.py +++ b/app.py @@ -18,6 +18,7 @@ import json import logging import math import sqlite3 +import subprocess import uuid import re from datetime import datetime, timezone @@ -25,6 +26,7 @@ from pathlib import Path from contextlib import asynccontextmanager import httpx +import psutil from fastapi import FastAPI, Request, HTTPException from fastapi.responses import HTMLResponse, StreamingResponse, JSONResponse @@ -38,7 +40,7 @@ syslog_handler.setFormatter(logging.Formatter('jarvischat[%(process)d]: %(leveln log.addHandler(syslog_handler) # --- Configuration --- -VERSION = "1.3.0" +VERSION = "1.3.1" OLLAMA_BASE = "http://localhost:11434" SEARXNG_BASE = "http://localhost:8888" DB_PATH = Path(__file__).parent / "jarvischat.db" @@ -429,6 +431,72 @@ async def search_status(): except: return {"available": False} +# --- System Stats --- + +def get_gpu_stats() -> dict: + """Get AMD GPU stats via rocm-smi.""" + try: + result = subprocess.run( + ["rocm-smi", "--showuse", "--showmemuse", "--json"], + capture_output=True, text=True, timeout=5 + ) + if result.returncode == 0: + data = json.loads(result.stdout) + # Parse rocm-smi JSON output + gpu_info = data.get("card0", {}) + gpu_use = gpu_info.get("GPU use (%)", 0) + vram_use = gpu_info.get("GPU Memory Allocated (VRAM%)", 0) + # Handle string or int values + if isinstance(gpu_use, str): + gpu_use = int(gpu_use.replace("%", "").strip() or 0) + if isinstance(vram_use, str): + vram_use = int(vram_use.replace("%", "").strip() or 0) + return {"gpu_percent": gpu_use, "vram_percent": vram_use, "available": True} + except subprocess.TimeoutExpired: + log.warning("rocm-smi timed out") + except FileNotFoundError: + log.debug("rocm-smi not found") + except json.JSONDecodeError: + # Fallback: parse text output + try: + result = subprocess.run( + ["rocm-smi", "--showuse", "--showmemuse"], + capture_output=True, text=True, timeout=5 + ) + gpu_use = 0 + vram_use = 0 + for line in result.stdout.split("\n"): + if "GPU use (%)" in line: + match = re.search(r"(\d+)", line.split(":")[-1]) + if match: + gpu_use = int(match.group(1)) + elif "GPU Memory Allocated (VRAM%)" in line: + match = re.search(r"(\d+)", line.split(":")[-1]) + if match: + vram_use = int(match.group(1)) + return {"gpu_percent": gpu_use, "vram_percent": vram_use, "available": True} + except Exception as e: + log.warning(f"rocm-smi parse error: {e}") + except Exception as e: + log.warning(f"GPU stats error: {e}") + return {"gpu_percent": 0, "vram_percent": 0, "available": False} + +@app.get("/api/stats") +async def system_stats(): + """Get system resource usage (CPU, memory, GPU).""" + cpu_percent = psutil.cpu_percent(interval=0.1) + memory = psutil.virtual_memory() + gpu = get_gpu_stats() + return { + "cpu_percent": round(cpu_percent, 1), + "memory_percent": round(memory.percent, 1), + "memory_used_gb": round(memory.used / (1024**3), 1), + "memory_total_gb": round(memory.total / (1024**3), 1), + "gpu_percent": gpu["gpu_percent"], + "vram_percent": gpu["vram_percent"], + "gpu_available": gpu["available"], + } + # --- Profile --- @app.get("/api/profile") @@ -870,6 +938,15 @@ body { font-family: var(--font-body); background: var(--bg-primary); color: var( .conv-item .conv-delete:hover { opacity: 1; } .sidebar-footer { padding: 12px 16px; border-top: 1px solid var(--border); font-size: 11px; color: var(--text-muted); font-family: var(--font-mono); } .sidebar-footer .status-row { display: flex; align-items: center; gap: 8px; margin-bottom: 4px; } +.stats-panel { margin-top: 10px; padding-top: 10px; border-top: 1px solid var(--border); } +.stat-row { display: flex; align-items: center; gap: 6px; margin-bottom: 6px; } +.stat-label { width: 36px; font-size: 10px; color: var(--text-muted); text-transform: uppercase; } +.stat-bar { flex: 1; height: 8px; background: var(--bg-tertiary); border-radius: 4px; overflow: hidden; } +.stat-fill { height: 100%; background: var(--accent); border-radius: 4px; transition: width 0.3s ease, background 0.3s ease; width: 0%; } +.stat-fill.gpu { background: var(--success); } +.stat-fill.warn { background: var(--warning); } +.stat-fill.danger { background: var(--danger); } +.stat-value { width: 32px; text-align: right; font-size: 10px; } /* Main */ .main { flex: 1; display: flex; flex-direction: column; height: 100vh; min-width: 0; } @@ -999,6 +1076,28 @@ body { font-family: var(--font-body); background: var(--bg-primary); color: var(
checking...
search: checking...
+
+
+ CPU +
+ --% +
+
+ MEM +
+ --% +
+
+ GPU +
+ --% +
+
+ VRAM +
+ --% +
+
@@ -1110,12 +1209,61 @@ document.addEventListener('DOMContentLoaded', async () => { await loadConversations(); checkOllamaStatus(); checkSearchStatus(); + updateSystemStats(); setInterval(checkOllamaStatus, 30000); setInterval(checkSearchStatus, 60000); + setInterval(updateSystemStats, 2000); document.getElementById('userInput').addEventListener('input', updateTokenThermometer); updateTokenThermometer(); }); +async function updateSystemStats() { + try { + const resp = await fetch('/api/stats'); + const data = await resp.json(); + + // Update CPU + const cpuFill = document.getElementById('cpuFill'); + const cpuValue = document.getElementById('cpuValue'); + cpuFill.style.width = data.cpu_percent + '%'; + cpuFill.className = 'stat-fill' + (data.cpu_percent >= 90 ? ' danger' : data.cpu_percent >= 70 ? ' warn' : ''); + cpuValue.textContent = data.cpu_percent + '%'; + + // Update Memory + const memFill = document.getElementById('memFill'); + const memValue = document.getElementById('memValue'); + memFill.style.width = data.memory_percent + '%'; + memFill.className = 'stat-fill' + (data.memory_percent >= 90 ? ' danger' : data.memory_percent >= 70 ? ' warn' : ''); + memValue.textContent = data.memory_percent + '%'; + + // Update GPU + const gpuFill = document.getElementById('gpuFill'); + const gpuValue = document.getElementById('gpuValue'); + if (data.gpu_available) { + gpuFill.style.width = data.gpu_percent + '%'; + gpuFill.className = 'stat-fill gpu' + (data.gpu_percent >= 90 ? ' danger' : data.gpu_percent >= 70 ? ' warn' : ''); + gpuValue.textContent = data.gpu_percent + '%'; + } else { + gpuFill.style.width = '0%'; + gpuValue.textContent = 'N/A'; + } + + // Update VRAM + const vramFill = document.getElementById('vramFill'); + const vramValue = document.getElementById('vramValue'); + if (data.gpu_available) { + vramFill.style.width = data.vram_percent + '%'; + vramFill.className = 'stat-fill gpu' + (data.vram_percent >= 90 ? ' danger' : data.vram_percent >= 70 ? ' warn' : ''); + vramValue.textContent = data.vram_percent + '%'; + } else { + vramFill.style.width = '0%'; + vramValue.textContent = 'N/A'; + } + } catch(e) { + console.log('Stats fetch error:', e); + } +} + async function checkOllamaStatus() { try { const resp = await fetch('/api/ps'); diff --git a/readme.md b/readme.md index 5f41323..8cac20b 100644 --- a/readme.md +++ b/readme.md @@ -2,7 +2,7 @@ **A lightweight Ollama coding companion that runs on Python 3.13** -![Version](https://img.shields.io/badge/version-1.3.0-blue) +![Version](https://img.shields.io/badge/version-1.3.1-blue) ![Python](https://img.shields.io/badge/python-3.13-green) ![License](https://img.shields.io/badge/license-MIT-orange) @@ -39,16 +39,21 @@ JarvisChat acts as middleware between your browser and Ollama. When the model's - Python 3.11+ (tested on 3.13) - Ollama running locally (default: `localhost:11434`) - SearXNG (optional, for web search — default: `localhost:8888`) +- ROCm (optional, for AMD GPU stats — `rocm-smi` must be in PATH) ## Installation ```bash # Clone or download app.py -git clone https://llgit.llamachile.shop/gramps/jarvischat.git -cd jarvischat +git clone https://github.com/llamachileshop-code/313_webui.git +cd 313_webui + +# Create virtual environment (recommended) +python3 -m venv venv +source venv/bin/activate # Install dependencies -pip install fastapi httpx uvicorn +pip install fastapi httpx uvicorn psutil # Run python app.py @@ -58,6 +63,11 @@ uvicorn app:app --host 0.0.0.0 --port 8080 Open `http://localhost:8080` in your browser. +**Note:** If running as a systemd service with a venv, install dependencies using the venv pip directly: +```bash +/opt/jarvischat/venv/bin/pip install fastapi httpx uvicorn psutil +``` + ## Running as a Service **Important:** Although JarvisChat is a single-file Python application, it's designed to run as a persistent service alongside Ollama — not as a one-off script. Both services should start on boot. @@ -74,8 +84,8 @@ Wants=ollama.service [Service] Type=simple -User=jarvischat -WorkingDirectory=/opt/jarvischat +User=your-username +WorkingDirectory=/path/to/313_webui ExecStart=/usr/bin/python3 app.py Restart=on-failure RestartSec=5 @@ -110,7 +120,7 @@ journalctl -t jarvischat -f Edit these constants at the top of `app.py`: ```python -VERSION = "1.3.0" +VERSION = "1.3.1" OLLAMA_BASE = "http://localhost:11434" SEARXNG_BASE = "http://localhost:8888" DEFAULT_MODEL = "deepseek-coder:6.7b" @@ -172,8 +182,9 @@ The count includes: profile + preset + conversation history + current input. Con | `/api/models` | GET | List Ollama models | | `/api/ps` | GET | Running models | | `/api/show` | POST | Model info (context size) | +| `/api/stats` | GET | System stats (CPU, memory, GPU, VRAM) | | `/api/chat` | POST | Stream chat (SSE) | -| `/api/conversations` | GET | List conversations | +| `/api/conversations` | GET/DELETE | List/delete all conversations | | `/api/conversations/{id}` | GET/DELETE | Get/delete conversation | | `/api/profile` | GET/PUT | Get/update profile | | `/api/presets` | GET/POST | List/create presets | @@ -222,6 +233,7 @@ The count includes: profile + preset + conversation history + current input. Con | Version | Changes | |---------|---------| +| 1.3.1 | System stats panel (CPU, memory, GPU, VRAM) in sidebar | | 1.3.0 | Delete all conversations button | | 1.2.9 | Token thermometer with live context tracking | | 1.2.8 | Logo in sidebar, llama emoji tagline |