#!/usr/bin/env python3
"""
workforce-manager.py — Autonomous agent workforce management

Three capabilities:
1. AUTO-ASSIGN: Match unassigned issues to the right agent by difficulty
2. QUALITY SCORE: Track merge rate per agent, demote poor performers
3. CREDIT MONITOR: Alert when agent quotas are likely exhausted

Runs as a periodic script called by the orchestrator or cron.

ACCEPTANCE CRITERIA:
  Auto-assign:
    - Scans all repos for unassigned issues
    - Scores issue difficulty (0-10) based on: labels, title keywords, file count
    - Maps difficulty to agent tier: hard(8-10)→perplexity, medium(4-7)→gemini/manus, easy(0-3)→kimi
    - Assigns via Gitea API, adds appropriate labels
    - Never assigns EPICs (those need human decision)
    - Never reassigns already-assigned issues
    - Respects agent capacity (max concurrent issues per agent)

  Quality scoring:
    - Pulls all closed PRs from last 7 days per agent
    - Calculates: merge rate, avg time to merge, rejection count
    - Agents below 40% merge rate get demoted one tier
    - Agents above 80% merge rate get promoted one tier
    - Writes scorecard to ~/.hermes/logs/agent-scorecards.json

  Credit monitoring:
    - Tracks daily PR count per agent
    - Manus: alert if >250 credits used (300/day limit)
    - Loop agents: alert if error rate spikes (likely rate limited)
    - Writes alerts to ~/.hermes/logs/workforce-alerts.json
"""

import json
import os
import sys
import time
import urllib.request
from datetime import datetime, timedelta, timezone
from collections import defaultdict

# === CONFIG ===
GITEA_URL = "http://143.198.27.163:3000"
TOKEN_FILE = os.path.expanduser("~/.hermes/gitea_token_vps")
LOG_DIR = os.path.expanduser("~/.hermes/logs")
SCORECARD_FILE = os.path.join(LOG_DIR, "agent-scorecards.json")
ALERTS_FILE = os.path.join(LOG_DIR, "workforce-alerts.json")

REPOS = [
    "Timmy_Foundation/the-nexus",
    "Timmy_Foundation/autolora",
]

# Agent tiers: which agents handle which difficulty
AGENT_TIERS = {
    "heavy": ["perplexity"],           # 8-10 difficulty
    "medium": ["gemini", "manus"],     # 4-7 difficulty
    "grunt": ["kimi"],                 # 0-3 difficulty
}

# Max concurrent issues per agent
MAX_CONCURRENT = {
    "perplexity": 2,   # one-shot, manual
    "manus": 2,        # one-shot, 300 credits/day
    "gemini": 5,       # 3-worker loop
    "kimi": 3,         # 1-worker loop
    "claude": 10,      # 10-worker loop, managed by its own loop
}

# Credit limits (daily)
CREDIT_LIMITS = {
    "manus": 300,
}

# Keywords that indicate difficulty
HARD_KEYWORDS = [
    "sovereignty", "nostr", "nip-", "economic", "architecture",
    "protocol", "edge intelligence", "memory graph", "identity",
    "cryptograph", "zero-knowledge", "consensus", "p2p",
    "distributed", "rlhf", "grpo", "training pipeline",
]
MEDIUM_KEYWORDS = [
    "feature", "integration", "api", "websocket", "three.js",
    "portal", "dashboard", "visualization", "agent", "deploy",
    "docker", "ssl", "infrastructure", "mcp", "inference",
]
EASY_KEYWORDS = [
    "refactor", "test", "docstring", "typo", "format", "lint",
    "rename", "cleanup", "dead code", "move", "extract",
    "add unit test", "fix import", "update readme",
]


def api(method, path, data=None):
    """Make a Gitea API call."""
    with open(TOKEN_FILE) as f:
        token = f.read().strip()
    url = f"{GITEA_URL}/api/v1{path}"
    headers = {
        "Authorization": f"token {token}",
        "Content-Type": "application/json",
    }
    if data:
        req = urllib.request.Request(url, json.dumps(data).encode(), headers, method=method)
    else:
        req = urllib.request.Request(url, headers=headers, method=method)
    try:
        resp = urllib.request.urlopen(req, timeout=15)
        return json.loads(resp.read())
    except Exception as e:
        return {"error": str(e)}


def score_difficulty(issue):
    """Score an issue 0-10 based on title, labels, and signals."""
    title = issue["title"].lower()
    labels = [l["name"].lower() for l in issue.get("labels", [])]
    score = 5  # default medium

    # EPICs are always 10 (but we skip them for auto-assign)
    if "[epic]" in title or "epic:" in title:
        return 10

    # Label-based scoring
    if "p0-critical" in labels:
        score += 2
    if "p1-important" in labels:
        score += 1
    if "p2-backlog" in labels:
        score -= 1
    if "needs-design" in labels:
        score += 2
    if "sovereignty" in labels or "nostr" in labels:
        score += 2
    if "infrastructure" in labels:
        score += 1

    # Keyword-based scoring
    for kw in HARD_KEYWORDS:
        if kw in title:
            score += 2
            break
    for kw in EASY_KEYWORDS:
        if kw in title:
            score -= 2
            break

    return max(0, min(10, score))


def get_agent_for_difficulty(score, current_loads):
    """Pick the best agent for a given difficulty score."""
    if score >= 8:
        tier = "heavy"
    elif score >= 4:
        tier = "medium"
    else:
        tier = "grunt"

    candidates = AGENT_TIERS[tier]
    # Pick the agent with the most capacity
    best = None
    best_capacity = -1
    for agent in candidates:
        max_c = MAX_CONCURRENT.get(agent, 3)
        current = current_loads.get(agent, 0)
        capacity = max_c - current
        if capacity > best_capacity:
            best_capacity = capacity
            best = agent
    if best_capacity <= 0:
        # All agents in tier are full, try next tier down
        fallback_order = ["medium", "grunt"] if tier == "heavy" else ["grunt"]
        for fb_tier in fallback_order:
            for agent in AGENT_TIERS[fb_tier]:
                max_c = MAX_CONCURRENT.get(agent, 3)
                current = current_loads.get(agent, 0)
                if max_c - current > 0:
                    return agent
        return None
    return best


def auto_assign():
    """Scan repos for unassigned issues and assign to appropriate agents."""
    print("=== AUTO-ASSIGN ===")
    
    # Get current agent loads (open issues per agent)
    current_loads = defaultdict(int)
    all_unassigned = []
    
    for repo in REPOS:
        issues = api("GET", f"/repos/{repo}/issues?state=open&type=issues&limit=50")
        if isinstance(issues, dict) and "error" in issues:
            print(f"  ERROR fetching {repo}: {issues['error']}")
            continue
        for issue in issues:
            assignees = [a["login"] for a in (issue.get("assignees") or [])]
            if assignees:
                for a in assignees:
                    current_loads[a] += 1
            else:
                issue["_repo"] = repo
                all_unassigned.append(issue)
    
    print(f"  Agent loads: {dict(current_loads)}")
    print(f"  Unassigned issues: {len(all_unassigned)}")
    
    assigned_count = 0
    for issue in all_unassigned:
        title = issue["title"].lower()
        
        # Skip EPICs — those need human decision
        if "[epic]" in title or "epic:" in title:
            print(f"  SKIP #{issue['number']} (EPIC): {issue['title'][:60]}")
            continue
        
        # Skip META/audit/showcase
        if "[meta]" in title or "[audit]" in title or "[showcase]" in title:
            print(f"  SKIP #{issue['number']} (meta): {issue['title'][:60]}")
            continue
        
        score = score_difficulty(issue)
        agent = get_agent_for_difficulty(score, current_loads)
        
        if agent is None:
            print(f"  SKIP #{issue['number']} (all agents full): {issue['title'][:60]}")
            continue
        
        # Assign
        repo = issue["_repo"]
        result = api("PATCH", f"/repos/{repo}/issues/{issue['number']}", {
            "assignees": [agent]
        })
        
        if "error" not in result:
            current_loads[agent] += 1
            assigned_count += 1
            tier = "HEAVY" if score >= 8 else "MEDIUM" if score >= 4 else "GRUNT"
            print(f"  ASSIGN #{issue['number']} -> {agent} (score={score} {tier}): {issue['title'][:50]}")
        else:
            print(f"  ERROR assigning #{issue['number']}: {result['error']}")
    
    print(f"  Assigned {assigned_count} issues this cycle.")
    return assigned_count


def quality_score():
    """Calculate merge rate and quality metrics per agent over last 7 days."""
    print("\n=== QUALITY SCORING ===")
    
    since = (datetime.now(timezone.utc) - timedelta(days=7)).strftime("%Y-%m-%dT%H:%M:%SZ")
    agent_stats = defaultdict(lambda: {"merged": 0, "closed_unmerged": 0, "open": 0, "total": 0})
    
    for repo in REPOS:
        # Merged PRs
        merged = api("GET", f"/repos/{repo}/pulls?state=closed&sort=updated&limit=50")
        if isinstance(merged, dict) and "error" in merged:
            continue
        for pr in merged:
            if pr.get("updated_at", "") < since:
                continue
            agent = pr["user"]["login"]
            agent_stats[agent]["total"] += 1
            if pr.get("merged"):
                agent_stats[agent]["merged"] += 1
            else:
                agent_stats[agent]["closed_unmerged"] += 1
        
        # Open PRs
        open_prs = api("GET", f"/repos/{repo}/pulls?state=open&limit=50")
        if isinstance(open_prs, dict) and "error" in open_prs:
            continue
        for pr in open_prs:
            agent = pr["user"]["login"]
            agent_stats[agent]["open"] += 1
            agent_stats[agent]["total"] += 1
    
    scorecards = {}
    for agent, stats in sorted(agent_stats.items()):
        total = stats["total"]
        if total == 0:
            continue
        merge_rate = stats["merged"] / max(total, 1) * 100
        
        # Determine tier adjustment
        if merge_rate >= 80:
            recommendation = "PROMOTE — high merge rate"
        elif merge_rate < 40 and total >= 3:
            recommendation = "DEMOTE — low merge rate"
        else:
            recommendation = "HOLD — acceptable"
        
        scorecards[agent] = {
            "merged": stats["merged"],
            "closed_unmerged": stats["closed_unmerged"],
            "open": stats["open"],
            "total": total,
            "merge_rate": round(merge_rate, 1),
            "recommendation": recommendation,
            "updated": datetime.now(timezone.utc).isoformat(),
        }
        
        print(f"  {agent:15s} merged={stats['merged']:3d} rejected={stats['closed_unmerged']:3d} open={stats['open']:3d} rate={merge_rate:5.1f}% {recommendation}")
    
    # Save scorecards
    with open(SCORECARD_FILE, "w") as f:
        json.dump(scorecards, f, indent=2)
    print(f"  Scorecards saved to {SCORECARD_FILE}")
    
    return scorecards


def credit_monitor():
    """Track daily usage per agent and alert on approaching limits."""
    print("\n=== CREDIT MONITORING ===")
    
    today = datetime.now(timezone.utc).strftime("%Y-%m-%d")
    daily_counts = defaultdict(int)
    
    for repo in REPOS:
        # Count PRs created today per agent
        prs = api("GET", f"/repos/{repo}/pulls?state=all&sort=created&limit=50")
        if isinstance(prs, dict) and "error" in prs:
            continue
        for pr in prs:
            created = pr.get("created_at", "")[:10]
            if created == today:
                agent = pr["user"]["login"]
                daily_counts[agent] += 1
    
    alerts = []
    for agent, count in sorted(daily_counts.items()):
        limit = CREDIT_LIMITS.get(agent)
        if limit:
            pct = count / limit * 100
            status = f"{count}/{limit} ({pct:.0f}%)"
            if pct >= 80:
                alert = f"WARNING: {agent} at {status} daily credits"
                alerts.append({"agent": agent, "type": "credit_limit", "message": alert, "time": datetime.now(timezone.utc).isoformat()})
                print(f"  ⚠️  {alert}")
            else:
                print(f"  {agent:15s} {status}")
        else:
            print(f"  {agent:15s} {count} PRs today (no credit limit)")
    
    # Check loop health via log files
    loop_logs = {
        "claude": "claude-loop.log",
        "gemini": "gemini-loop.log",
        "kimi": "kimi-loop.log",
    }
    for agent, logfile in loop_logs.items():
        logpath = os.path.join(LOG_DIR, logfile)
        if not os.path.exists(logpath):
            continue
        # Count errors in last 50 lines
        try:
            with open(logpath) as f:
                lines = f.readlines()[-50:]
            errors = sum(1 for l in lines if "FAIL" in l or "ERROR" in l or "rate" in l.lower())
            if errors >= 10:
                alert = f"WARNING: {agent} loop has {errors} errors in last 50 log lines (possible rate limit)"
                alerts.append({"agent": agent, "type": "error_spike", "message": alert, "time": datetime.now(timezone.utc).isoformat()})
                print(f"  ⚠️  {alert}")
        except:
            pass
    
    # Save alerts
    existing = []
    if os.path.exists(ALERTS_FILE):
        try:
            with open(ALERTS_FILE) as f:
                existing = json.load(f)
        except:
            pass
    existing.extend(alerts)
    # Keep last 100 alerts
    existing = existing[-100:]
    with open(ALERTS_FILE, "w") as f:
        json.dump(existing, f, indent=2)
    
    if not alerts:
        print("  No alerts. All systems nominal.")
    
    return alerts


def main():
    os.makedirs(LOG_DIR, exist_ok=True)
    
    mode = sys.argv[1] if len(sys.argv) > 1 else "all"
    
    if mode in ("all", "assign"):
        auto_assign()
    if mode in ("all", "score"):
        quality_score()
    if mode in ("all", "credits"):
        credit_monitor()


if __name__ == "__main__":
    main()