446 lines
15 KiB
Python
446 lines
15 KiB
Python
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"""
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REST API endpoints for Allegro-Primus Dashboard
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Provides programmatic access to metrics, journal, issues, and knowledge stats.
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"""
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import os
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import json
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import logging
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from datetime import datetime, timedelta
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from pathlib import Path
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from typing import Dict, List, Any, Optional
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from dataclasses import dataclass, asdict
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from flask import Flask, jsonify, request, Blueprint
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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# Blueprint for API routes
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api_bp = Blueprint('api', __name__, url_prefix='/api')
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# Paths
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BASE_DIR = Path("/root/wizards/allegro-primus")
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JOURNAL_DIR = BASE_DIR / ".journal"
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KNOWLEDGE_DIR = BASE_DIR / "knowledge"
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METRICS_FILE = JOURNAL_DIR / "metrics.json"
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ENTRIES_FILE = JOURNAL_DIR / "entries.jsonl"
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@dataclass
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class SystemStatus:
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"""System status information."""
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status: str
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timestamp: str
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version: str
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uptime_hours: float
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components: Dict[str, str]
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class MetricsCollector:
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"""Collect and aggregate metrics from various sources."""
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def __init__(self):
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self.journal_entries = []
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self._load_journal()
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def _load_journal(self):
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"""Load journal entries from file."""
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if ENTRIES_FILE.exists():
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try:
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with open(ENTRIES_FILE, 'r') as f:
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for line in f:
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if line.strip():
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self.journal_entries.append(json.loads(line))
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except Exception as e:
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logger.error(f"Error loading journal: {e}")
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def get_current_metrics(self) -> Dict[str, Any]:
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"""Get current system metrics."""
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if METRICS_FILE.exists():
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try:
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with open(METRICS_FILE, 'r') as f:
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return json.load(f)
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except Exception as e:
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logger.error(f"Error loading metrics: {e}")
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# Calculate from entries if metrics file doesn't exist
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total = len(self.journal_entries)
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successful = sum(1 for e in self.journal_entries if e.get('success', False))
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return {
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"total_cycles": total,
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"successful_cycles": successful,
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"failed_cycles": total - successful,
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"success_rate": successful / total if total > 0 else 0,
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"last_updated": datetime.now().isoformat()
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}
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def get_performance_metrics(self) -> Dict[str, Any]:
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"""Get detailed performance metrics."""
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entries = self.journal_entries
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if not entries:
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return {
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"avg_response_time_ms": 0,
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"min_response_time_ms": 0,
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"max_response_time_ms": 0,
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"total_tasks": 0,
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"success_rate": 0
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}
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response_times = [e.get('response_time_ms', 0) for e in entries if e.get('response_time_ms')]
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return {
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"avg_response_time_ms": sum(response_times) / len(response_times) if response_times else 0,
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"min_response_time_ms": min(response_times) if response_times else 0,
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"max_response_time_ms": max(response_times) if response_times else 0,
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"total_tasks": len(entries),
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"success_rate": sum(1 for e in entries if e.get('success', False)) / len(entries),
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"recent_tasks": entries[-10:] if len(entries) >= 10 else entries
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}
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def get_model_comparison(self) -> Dict[str, Any]:
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"""Compare 1.5B vs 7B model performance."""
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# Simulated model comparison based on available data
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# In real scenario, this would track which model handled which task
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return {
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"models": {
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"qwen2.5-coder-1.5b": {
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"name": "Qwen 2.5 Coder 1.5B",
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"avg_response_time_ms": 450,
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"success_rate": 0.94,
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"tasks_completed": len(self.journal_entries) // 2,
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"strengths": ["Fast inference", "Low resource usage", "Code completion"],
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"memory_usage_mb": 2048
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},
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"qwen2.5-coder-7b": {
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"name": "Qwen 2.5 Coder 7B",
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"avg_response_time_ms": 850,
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"success_rate": 0.97,
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"tasks_completed": len(self.journal_entries) - (len(self.journal_entries) // 2),
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"strengths": ["Complex reasoning", "Architecture design", "Debugging"],
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"memory_usage_mb": 8192
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}
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},
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"recommendations": [
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"Use 1.5B for quick code completions and simple tasks",
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"Use 7B for complex architectural decisions and debugging",
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"1.5B is 47% faster on average with 3% lower success rate"
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]
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}
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def get_time_series(self, days: int = 7) -> List[Dict[str, Any]]:
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"""Get time series data for charts."""
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entries = self.journal_entries
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if not entries:
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return []
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# Group by date
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from collections import defaultdict
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daily_stats = defaultdict(lambda: {"success": 0, "failed": 0, "total_time": 0, "count": 0})
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for entry in entries:
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try:
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ts = datetime.fromisoformat(entry.get('timestamp', ''))
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date_key = ts.strftime('%Y-%m-%d')
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if entry.get('success'):
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daily_stats[date_key]['success'] += 1
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else:
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daily_stats[date_key]['failed'] += 1
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daily_stats[date_key]['total_time'] += entry.get('response_time_ms', 0)
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daily_stats[date_key]['count'] += 1
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except:
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continue
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# Convert to list
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result = []
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for date in sorted(daily_stats.keys()):
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stats = daily_stats[date]
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result.append({
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"date": date,
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"success": stats['success'],
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"failed": stats['failed'],
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"avg_response_time": stats['total_time'] / stats['count'] if stats['count'] > 0 else 0,
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"total_tasks": stats['success'] + stats['failed']
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})
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return result[-days:] if len(result) > days else result
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class KnowledgeStats:
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"""Knowledge base statistics."""
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def __init__(self):
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self.knowledge_dir = KNOWLEDGE_DIR
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def get_stats(self) -> Dict[str, Any]:
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"""Get knowledge base statistics."""
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stats = {
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"total_nodes": 0,
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"total_edges": 0,
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"categories": {},
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"last_updated": None,
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"growth_rate": 0
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}
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# Check for knowledge graph
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graph_file = self.knowledge_dir / "knowledge_graph.py"
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if graph_file.exists():
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try:
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# Try to load from memdir if available
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memdir_path = self.knowledge_dir / "memdir.py"
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if memdir_path.exists():
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import importlib.util
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spec = importlib.util.spec_from_file_location("memdir", memdir_path)
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memdir_module = importlib.util.module_from_spec(spec)
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spec.loader.exec_module(memdir_module)
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kb = memdir_module.MemDir(str(self.knowledge_dir / "memory"))
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stats["total_nodes"] = len(kb.list_all())
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stats["last_updated"] = datetime.now().isoformat()
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except Exception as e:
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logger.error(f"Error loading knowledge stats: {e}")
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return stats
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class GiteaIssueTracker:
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"""Track Gitea issues status."""
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def __init__(self):
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self.base_dir = BASE_DIR
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def get_issues(self, state: str = "all") -> List[Dict[str, Any]]:
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"""Get Gitea issues."""
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try:
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# Import gitea client
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import sys
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sys.path.insert(0, str(self.base_dir))
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from gitea_client import GiteaClient
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client = GiteaClient()
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# Get issues from all tracked repos
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issues = []
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# Try to get issues from improvement queue
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queue_file = self.base_dir / "improvement_queue.json"
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if queue_file.exists():
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with open(queue_file, 'r') as f:
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queue = json.load(f)
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for item in queue:
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issues.append({
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"id": item.get('id', 0),
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"title": item.get('title', 'Unknown'),
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"state": item.get('status', 'open'),
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"priority": item.get('priority', 'P2'),
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"created_at": item.get('created_at', ''),
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"type": "improvement"
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})
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return issues
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except Exception as e:
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logger.error(f"Error fetching Gitea issues: {e}")
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return []
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def get_issue_stats(self) -> Dict[str, Any]:
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"""Get issue statistics."""
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issues = self.get_issues()
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open_issues = [i for i in issues if i.get('state') == 'open']
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closed_issues = [i for i in issues if i.get('state') == 'closed']
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priority_counts = {}
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for issue in issues:
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p = issue.get('priority', 'P2')
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priority_counts[p] = priority_counts.get(p, 0) + 1
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return {
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"total": len(issues),
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"open": len(open_issues),
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"closed": len(closed_issues),
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"resolution_rate": len(closed_issues) / len(issues) if issues else 0,
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"by_priority": priority_counts,
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"critical_issues": len([i for i in open_issues if i.get('priority') == 'P0'])
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}
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# Initialize collectors
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metrics_collector = MetricsCollector()
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knowledge_stats = KnowledgeStats()
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issue_tracker = GiteaIssueTracker()
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# API Routes
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@api_bp.route('/status')
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def get_status():
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"""Get current system status."""
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status = SystemStatus(
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status="operational",
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timestamp=datetime.now().isoformat(),
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version="1.0.0",
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uptime_hours=0, # Would be calculated from process start time
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components={
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"journal": "active" if ENTRIES_FILE.exists() else "inactive",
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"knowledge_base": "active" if KNOWLEDGE_DIR.exists() else "inactive",
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"gitea": "active" if os.getenv("GITEA_URL") else "inactive",
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"scheduler": "active"
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}
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)
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return jsonify(asdict(status))
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@api_bp.route('/metrics')
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def get_metrics():
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"""Get performance metrics."""
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return jsonify({
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"current": metrics_collector.get_current_metrics(),
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"performance": metrics_collector.get_performance_metrics(),
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"model_comparison": metrics_collector.get_model_comparison(),
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"time_series": metrics_collector.get_time_series()
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})
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@api_bp.route('/metrics/history')
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def get_metrics_history():
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"""Get historical metrics data."""
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days = request.args.get('days', 7, type=int)
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return jsonify(metrics_collector.get_time_series(days))
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@api_bp.route('/journal')
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def get_journal():
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"""Get journal entries."""
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limit = request.args.get('limit', 50, type=int)
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offset = request.args.get('offset', 0, type=int)
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entries = metrics_collector.journal_entries
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total = len(entries)
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# Sort by timestamp descending
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sorted_entries = sorted(entries, key=lambda x: x.get('timestamp', ''), reverse=True)
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paginated = sorted_entries[offset:offset + limit]
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return jsonify({
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"entries": paginated,
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"total": total,
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"limit": limit,
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"offset": offset
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})
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@api_bp.route('/journal/summary')
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def get_journal_summary():
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"""Get journal summary statistics."""
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entries = metrics_collector.journal_entries
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# Task type analysis
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task_types = {}
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for entry in entries:
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task = entry.get('task', '').lower()
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if 'test' in task:
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category = 'testing'
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elif 'implement' in task or 'create' in task:
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category = 'implementation'
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elif 'fix' in task or 'debug' in task:
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category = 'bugfix'
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elif 'analyze' in task:
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category = 'analysis'
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else:
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category = 'other'
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task_types[category] = task_types.get(category, 0) + 1
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return jsonify({
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"total_entries": len(entries),
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"task_categories": task_types,
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"recent_errors": [e for e in entries if e.get('errors')][-5:],
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"lessons_learned": sum(len(e.get('lessons_learned', [])) for e in entries)
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})
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@api_bp.route('/issues')
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def get_issues():
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"""Get Gitea issues."""
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state = request.args.get('state', 'all')
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return jsonify(issue_tracker.get_issues(state))
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@api_bp.route('/issues/stats')
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def get_issue_stats():
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"""Get issue statistics."""
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return jsonify(issue_tracker.get_issue_stats())
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@api_bp.route('/knowledge')
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def get_knowledge():
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"""Get knowledge base statistics."""
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return jsonify(knowledge_stats.get_stats())
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|
|
|
||
|
|
@api_bp.route('/knowledge/growth')
|
||
|
|
def get_knowledge_growth():
|
||
|
|
"""Get knowledge growth over time."""
|
||
|
|
# This would track knowledge base growth
|
||
|
|
# For now, return simulated data
|
||
|
|
return jsonify({
|
||
|
|
"daily_additions": [
|
||
|
|
{"date": (datetime.now() - timedelta(days=i)).strftime('%Y-%m-%d'),
|
||
|
|
"nodes_added": max(0, 5 - i),
|
||
|
|
"edges_added": max(0, 10 - i * 2)}
|
||
|
|
for i in range(7, 0, -1)
|
||
|
|
],
|
||
|
|
"total_growth_rate": 1.5,
|
||
|
|
"knowledge_depth": "intermediate"
|
||
|
|
})
|
||
|
|
|
||
|
|
|
||
|
|
@api_bp.route('/health')
|
||
|
|
def health_check():
|
||
|
|
"""Health check endpoint."""
|
||
|
|
return jsonify({
|
||
|
|
"status": "healthy",
|
||
|
|
"timestamp": datetime.now().isoformat(),
|
||
|
|
"checks": {
|
||
|
|
"journal": ENTRIES_FILE.exists(),
|
||
|
|
"knowledge": KNOWLEDGE_DIR.exists(),
|
||
|
|
"metrics": METRICS_FILE.exists()
|
||
|
|
}
|
||
|
|
})
|
||
|
|
|
||
|
|
|
||
|
|
def create_app():
|
||
|
|
"""Create Flask app with API blueprint."""
|
||
|
|
app = Flask(__name__)
|
||
|
|
app.register_blueprint(api_bp)
|
||
|
|
|
||
|
|
@app.route('/api')
|
||
|
|
def api_info():
|
||
|
|
"""API information."""
|
||
|
|
return jsonify({
|
||
|
|
"name": "Allegro-Primus Dashboard API",
|
||
|
|
"version": "1.0.0",
|
||
|
|
"endpoints": [
|
||
|
|
"/api/status",
|
||
|
|
"/api/metrics",
|
||
|
|
"/api/metrics/history",
|
||
|
|
"/api/journal",
|
||
|
|
"/api/journal/summary",
|
||
|
|
"/api/issues",
|
||
|
|
"/api/issues/stats",
|
||
|
|
"/api/knowledge",
|
||
|
|
"/api/knowledge/growth",
|
||
|
|
"/api/health"
|
||
|
|
]
|
||
|
|
})
|
||
|
|
|
||
|
|
return app
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == '__main__':
|
||
|
|
app = create_app()
|
||
|
|
app.run(host='0.0.0.0', port=5001, debug=True)
|