"""AtlasOps Coordinator.

Routes alerts through: Triage → Diagnosis → Remediation → Comms.
Receives Alertmanager webhooks at POST /webhook on port 9099.
Each agent is a vLLM endpoint co-hosted on the AMD MI300X.
"""

import json
import logging
import os
import time
import uuid
from pathlib import Path
from typing import Any

import httpx
from fastapi import FastAPI, Request
from fastapi.responses import JSONResponse, StreamingResponse
from pydantic import BaseModel, Field

from agents.approval import approval_gate, approval_mode_for_severity
from agents.audit import audit_log
from agents.circuit_breaker import CircuitBreakerTripped, circuit_breaker
from agents.correlator import correlator
from agents.prometheus_metrics import build_dashboard_metrics_payload
from agents.stream import emit as thought_emit
from agents.tools import TOOL_REGISTRY
from agents.tools.alertmanager import alertmanager_list_alerts
from config.runtime import StepRewardTracker


logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
log = logging.getLogger("coordinator")


# Backend selection:
#   BACKEND=vllm   → self-hosted vLLM on AMD MI300X (default)
#   BACKEND=fireworks → Fireworks AI API (AMD GPUs, managed)
#   BACKEND=openai  → any OpenAI-compatible endpoint
BACKEND = os.getenv("BACKEND", "vllm")

_BACKEND_DEFAULTS = {
    "vllm":      ("http://localhost:8000/v1",          "Qwen/Qwen2.5-7B-Instruct"),
    "fireworks": ("https://api.fireworks.ai/inference/v1", "accounts/fireworks/models/qwen2p5-7b-instruct"),
    "openai":    ("https://api.openai.com/v1",         "gpt-4o-mini"),
}
_default_base, _default_model = _BACKEND_DEFAULTS.get(BACKEND, _BACKEND_DEFAULTS["vllm"])

VLLM_BASE  = os.getenv("VLLM_BASE",    _default_base)
MODEL_NAME = os.getenv("AGENT_MODEL",  _default_model)
API_KEY    = os.getenv("LLM_API_KEY",  "")  # required for fireworks/openai, empty for local vllm
PROMPTS_DIR = Path(__file__).parent / "prompts"
TRAJECTORIES_DIR = Path(os.getenv("TRAJECTORIES_DIR", "data/trajectories"))
TRAJECTORIES_DIR.mkdir(parents=True, exist_ok=True)

# Backend-enforced safety policy. Prompt guidance is useful, but runtime must
# still guard high-impact tools in case a model issues unsafe calls.
MUTATING_TOOLS = {
    "argocd_rollback",
    "kubectl_rollout",
    "kubectl_scale",
    "alertmanager_silence",
}
ROLE_ALLOWED_TOOLS = {
    "triage": {"kubectl_get", "kubectl_top_pods", "alertmanager_list_alerts", "promql_query"},
    "diagnosis": {
        "promql_query",
        "promql_query_range",
        "jaeger_search",
        "jaeger_get_trace",
        "kubectl_logs",
        "kubectl_describe",
        "kubectl_get",
        "kubectl_top_pods",
        "argocd_list_apps",
        "argocd_app_history",
        "gcloud_logs_read",
        "cloud_monitoring_query",
    },
    "remediation": {
        "argocd_rollback",
        "kubectl_rollout",
        "kubectl_scale",
        "alertmanager_silence",
        "promql_query",
        "kubectl_get",
        "kubectl_describe",
        "slack_post_update",
    },
    "comms": {"slack_post_update", "postmortem_draft"},
}


class AlertWebhookPayload(BaseModel):
    alerts: list[dict[str, Any]] = Field(default_factory=list)
    commonLabels: dict[str, Any] = Field(default_factory=dict)
    status: str | None = None


def load_prompt(role: str) -> str:
    return (PROMPTS_DIR / f"{role}.md").read_text(encoding="utf-8")


def _extract_tool_calls_from_content(content: str) -> list[dict[str, Any]]:
    """Fallback: parse tool calls from content for providers that don't use tool_calls array.

    Handles two formats:
    1. {"type":"function","name":"fn","parameters":{...}}
    2. {"name":"fn","arguments":{...}}
    """
    if not content or "{" not in content:
        return []
    try:
        start = content.index("{")
        end = content.rindex("}") + 1
        obj = json.loads(content[start:end])
        fn_name = obj.get("name") or obj.get("function", {}).get("name")
        if not fn_name:
            return []
        args = obj.get("parameters") or obj.get("arguments") or obj.get("function", {}).get("arguments") or {}
        if isinstance(args, str):
            try:
                args = json.loads(args)
            except json.JSONDecodeError:
                args = {}
        return [{
            "id": f"call_{fn_name}",
            "type": "function",
            "function": {"name": fn_name, "arguments": json.dumps(args)},
        }]
    except (ValueError, json.JSONDecodeError, KeyError):
        return []


_CONCLUSION_PROMPTS = {
    "triage":      "Based on the tool results above, output ONLY a JSON object with keys: incident_id, severity, title, blast_radius, affected_services. No prose.",
    "diagnosis":   "Based on the tool results above, output ONLY a JSON object with keys: root_cause, confidence, evidence, recommended_fix. No prose.",
    "remediation": "Based on the actions taken above, output ONLY a JSON object with keys: outcome (resolved/unresolved), actions_taken (list), verified_by. No prose.",
    "comms":       "Based on the incident above, output ONLY a JSON object with keys: incident_id, slack_posted, postmortem_path, summary. No prose.",
}


async def _force_json_conclusion(role: str, messages: list[dict], client: httpx.AsyncClient) -> dict[str, Any]:
    """One extra turn with tools disabled, forcing a clean JSON conclusion.

    Trims the message history to the system prompt + last 4 turns to avoid
    context overflow after 10-turn diagnosis runs.
    """
    prompt = _CONCLUSION_PROMPTS.get(role, "Summarise your findings as a JSON object.")
    # Keep system message + last 4 assistant/tool message pairs + forced prompt
    system_msgs = [m for m in messages if m.get("role") == "system"][:1]
    recent = [m for m in messages if m.get("role") != "system"][-8:]
    forced_msgs = system_msgs + recent + [{"role": "user", "content": prompt}]
    headers = {"Authorization": f"Bearer {API_KEY}"} if API_KEY else {}
    try:
        async with httpx.AsyncClient(timeout=60, headers=headers) as c:
            r = await c.post(
                f"{VLLM_BASE}/chat/completions",
                json={"model": MODEL_NAME, "messages": forced_msgs, "temperature": 0.0},
            )
            r.raise_for_status()
            content = r.json()["choices"][0]["message"].get("content", "")
            parsed = _try_parse_json(content)
            return parsed if "raw" not in parsed else {"summary": content[:300]}
    except Exception as e:
        return {"error": f"forced_conclusion failed: {e}"}


async def call_agent(role: str, user_input: dict[str, Any], max_turns: int = 10) -> dict[str, Any]:
    """Run a single agent with a tool-calling loop. Returns final JSON output."""
    system_prompt = load_prompt(role)
    incident_id = str(user_input.get("incident_id", "unknown"))
    messages = [
        {"role": "system", "content": system_prompt},
        {"role": "user", "content": json.dumps(user_input, indent=2)},
    ]
    trajectory: list[dict[str, Any]] = []
    step_tracker = StepRewardTracker()
    _seen_calls: dict[str, int] = {}   # (tool+args hash) → call count (exact-args dedup)
    _tool_counts: dict[str, int] = {}  # tool_name → total calls this run (per-tool cap)

    headers = {"Authorization": f"Bearer {API_KEY}"} if API_KEY else {}
    async with httpx.AsyncClient(timeout=120, headers=headers) as client:
        for turn in range(max_turns):
            r = await client.post(
                f"{VLLM_BASE}/chat/completions",
                json={
                    "model": MODEL_NAME,
                    "messages": messages,
                    "temperature": 0.2,
                    "tools": _tool_schemas_for_role(role),
                    "tool_choice": "auto",
                },
            )
            r.raise_for_status()
            choice = r.json()["choices"][0]
            msg = choice["message"]
            messages.append(msg)

            # Normalise tool calls — some providers (Fireworks/Llama) return
            # function calls as JSON in content instead of the tool_calls array.
            if not msg.get("tool_calls"):
                msg["tool_calls"] = _extract_tool_calls_from_content(msg.get("content") or "")

            if not msg.get("tool_calls"):
                conclusion = msg["content"] or ""
                parsed = _try_parse_json(conclusion)
                # If raw, or if conclusion looks like tool args instead of a role conclusion,
                # do one forced-JSON turn to get a clean structured summary.
                _ROLE_REQUIRED_KEYS = {
                    "triage": {"severity"},
                    "diagnosis": {"root_cause"},
                    "remediation": {"outcome"},
                    "comms": {"slack_posted"},
                }
                _required = _ROLE_REQUIRED_KEYS.get(role, set())
                if "raw" in parsed or (_required and not _required.intersection(parsed.keys())):
                    parsed = await _force_json_conclusion(role, messages, client)
                thought_emit(role, "conclusion", _summarise_conclusion(role, conclusion))
                trajectory.append({"role": role, "turn": turn, "content": conclusion})
                return {
                    "role": role,
                    "trajectory": trajectory,
                    "final": parsed,
                    "step_reward_summary": step_tracker.summary(),
                }

            for tc in msg["tool_calls"]:
                fn_name = tc["function"]["name"]
                fn_args = json.loads(tc["function"]["arguments"])
                policy_error = _check_tool_policy(role, fn_name, fn_args, user_input)
                if policy_error:
                    tool_output = {"success": False, "error": policy_error}
                    audit_log.record(
                        incident_id=incident_id,
                        agent_role=role,
                        action_type="tool_result",
                        tool_name=fn_name,
                        tool_args=fn_args,
                        result_summary=policy_error,
                        policy_check="blocked_by_policy",
                    )
                    thought_emit(role, "tool_result", f"⚠️ blocked by policy: {policy_error}", tool=fn_name)
                    trajectory.append(
                        {
                            "role": role,
                            "turn": turn,
                            "tool": fn_name,
                            "args": fn_args,
                            "output": tool_output,
                            "blocked_by_policy": True,
                        }
                    )
                    messages.append(
                        {
                            "role": "tool",
                            "tool_call_id": tc["id"],
                            "content": json.dumps(tool_output),
                        }
                    )
                    continue
                # Narrate the tool call
                thought_emit(role, "tool_call",
                             _narrate_tool_call(role, fn_name, fn_args),
                             tool=fn_name)
                audit_log.record(
                    incident_id=incident_id,
                    agent_role=role,
                    action_type="tool_call",
                    tool_name=fn_name,
                    tool_args=fn_args,
                    policy_check="allowed",
                )
                try:
                    circuit_breaker.check_before_tool_call(
                        incident_id=incident_id,
                        tool_name=fn_name,
                        is_mutating=fn_name in MUTATING_TOOLS,
                    )
                except CircuitBreakerTripped as e:
                    tool_output = {"success": False, "error": str(e), "blocked_by_circuit_breaker": True}
                    audit_log.record(
                        incident_id=incident_id,
                        agent_role=role,
                        action_type="tool_result",
                        tool_name=fn_name,
                        tool_args=fn_args,
                        result_summary=str(e),
                        policy_check="blocked_by_circuit_breaker",
                    )
                    thought_emit(role, "tool_result", f"⛔ blocked by circuit breaker: {e}", tool=fn_name)
                    trajectory.append(
                        {
                            "role": role,
                            "turn": turn,
                            "tool": fn_name,
                            "args": fn_args,
                            "output": tool_output,
                            "blocked_by_circuit_breaker": True,
                        }
                    )
                    messages.append(
                        {
                            "role": "tool",
                            "tool_call_id": tc["id"],
                            "content": json.dumps(tool_output),
                        }
                    )
                    continue
                # Dedup guard 1: same (tool + exact args) called >3 times
                _call_key = f"{fn_name}:{json.dumps(fn_args, sort_keys=True)}"
                _seen_calls[_call_key] = _seen_calls.get(_call_key, 0) + 1
                if _seen_calls[_call_key] > 3:
                    tool_output = {"error": f"Duplicate call blocked — {fn_name} already called with these exact args. Try different parameters or produce your conclusion."}
                    messages.append({"role": "tool", "tool_call_id": tc["id"], "content": json.dumps(tool_output)})
                    trajectory.append({"role": role, "turn": turn, "tool": fn_name, "args": fn_args, "output": tool_output, "dedup_blocked": True})
                    continue
                # Dedup guard 2: same tool called >6 times total (catches arg-variation loops)
                _TOOL_CAPS = {"promql_query": 6, "promql_query_range": 4, "kubectl_get": 5, "kubectl_logs": 4}
                _tool_counts[fn_name] = _tool_counts.get(fn_name, 0) + 1
                _cap = _TOOL_CAPS.get(fn_name, 8)
                if _tool_counts[fn_name] > _cap:
                    tool_output = {"error": f"Tool cap reached — {fn_name} called {_tool_counts[fn_name]} times this run (limit {_cap}). You have enough data; produce your conclusion now."}
                    messages.append({"role": "tool", "tool_call_id": tc["id"], "content": json.dumps(tool_output)})
                    trajectory.append({"role": role, "turn": turn, "tool": fn_name, "args": fn_args, "output": tool_output, "cap_blocked": True})
                    continue

                fn = TOOL_REGISTRY.get(fn_name)
                if not fn:
                    tool_output = {"error": f"Unknown tool: {fn_name}"}
                else:
                    try:
                        tool_output = fn(**fn_args)
                    except Exception as e:
                        tool_output = {"error": f"Tool execution failed: {e}"}
                # Dense per-step reward signal
                step_reward = step_tracker.record(fn_name, fn_args, tool_output)
                # Narrate the result
                audit_log.record(
                    incident_id=incident_id,
                    agent_role=role,
                    action_type="tool_result",
                    tool_name=fn_name,
                    tool_args=fn_args,
                    result_summary=str(tool_output)[:300],
                    policy_check="allowed",
                )
                thought_emit(role, "tool_result",
                             _narrate_tool_result(fn_name, tool_output),
                             tool=fn_name,
                             result_summary=str(tool_output)[:200])
                trajectory.append({
                    "role": role, "turn": turn, "tool": fn_name,
                    "args": fn_args, "output": tool_output,
                    "step_reward": step_reward,
                })
                messages.append({
                    "role": "tool",
                    "tool_call_id": tc["id"],
                    "content": json.dumps(tool_output)[:8000],
                })

    log.warning("%s exceeded %d turns", role, max_turns)
    # Ask the model to summarise whatever it found rather than returning an error
    forced = await _force_json_conclusion(role, messages, client)
    thought_emit(role, "conclusion", _summarise_conclusion(role, ""))
    return {"role": role, "trajectory": trajectory, "final": forced, "step_reward_summary": step_tracker.summary()}


def _narrate_tool_call(role: str, tool: str, args: dict) -> str:
    narrations = {
        "kubectl_get":           lambda a: f"Checking {a.get('resource','pods')} across the cluster...",
        "kubectl_logs":          lambda a: f"Reading logs from {a.get('pod','pod')} — looking for errors...",
        "kubectl_describe":      lambda a: f"Describing {a.get('resource','')} {a.get('name','')} — checking events...",
        "kubectl_top_pods":      lambda a: "Checking CPU/memory pressure across all pods...",
        "kubectl_rollout":       lambda a: f"Running rollout {a.get('action','')} on {a.get('resource','')}...",
        "kubectl_scale":         lambda a: f"Scaling {a.get('deployment','')} to {a.get('replicas','')} replicas...",
        "promql_query":          lambda a: f"Querying Prometheus: `{str(a.get('query',''))[:80]}`",
        "promql_query_range":    lambda a: f"Checking metric trend: `{str(a.get('query',''))[:80]}`",
        "jaeger_search":         lambda a: f"Searching traces for {a.get('service','')} (last {a.get('lookback','15m')})...",
        "jaeger_get_trace":      lambda a: f"Fetching trace {a.get('trace_id','')[:16]}... — following the span chain...",
        "argocd_list_apps":      lambda a: "Checking Argo CD for recent deployments...",
        "argocd_app_history":    lambda a: f"Checking deploy history for {a.get('app','')}...",
        "argocd_rollback":       lambda a: f"Rolling back {a.get('app','')} to revision {a.get('revision','')}...",
        "gcloud_logs_read":      lambda a: f"Reading Cloud Logging: `{str(a.get('filter_query',''))[:80]}`",
        "cloud_monitoring_query":lambda a: f"Querying GCP metric: {a.get('metric_type','')}",
        "alertmanager_silence":  lambda a: f"Silencing alert for {a.get('duration_minutes',30)} min — suppressing noise...",
        "slack_post_update":     lambda a: f"Posting [{a.get('severity','')}] incident update to Slack...",
        "postmortem_draft":      lambda a: "Drafting postmortem — building timeline from incident data...",
    }
    fn = narrations.get(tool)
    return fn(args) if fn else f"Calling {tool}..."


def _narrate_tool_result(tool: str, output: dict) -> str:
    if not output.get("success", True):
        return f"⚠️ {tool} returned an error: {str(output.get('error',''))[:100]}"
    result_narrations = {
        "kubectl_get":       "Got cluster state.",
        "kubectl_logs":      "Got pod logs — scanning for stack traces and errors.",
        "promql_query":      f"Got metric data — analysing values.",
        "jaeger_search":     f"Found traces — checking for slow spans.",
        "argocd_rollback":   "✅ Rollback executed.",
        "kubectl_scale":     "✅ Scale applied.",
        "slack_post_update": "✅ Slack notified.",
        "postmortem_draft":  "✅ Postmortem saved.",
    }
    return result_narrations.get(tool, f"{tool} completed.")


def _summarise_conclusion(role: str, content: str) -> str:
    summaries = {
        "triage":      "Triage complete — severity assigned, blast radius mapped, handing to Diagnosis.",
        "diagnosis":   "Root cause identified — handing remediation plan to Remediation agent.",
        "remediation": "Remediation complete — verifying resolution with Prometheus.",
        "comms":       "Incident closed — Slack updated, postmortem saved.",
    }
    return summaries.get(role, f"{role} agent finished.")


def _tool_schemas_for_role(role: str) -> list[dict[str, Any]]:
    return [_tool_schema(name) for name in ROLE_ALLOWED_TOOLS.get(role, set())]


def _tool_schema(name: str) -> dict[str, Any]:
    """Generate OpenAI-format tool schema with strict known arguments."""
    schema_map: dict[str, dict[str, Any]] = {
        "kubectl_get": {"type": "object", "properties": {"resource": {"type": "string"}, "namespace": {"type": "string"}, "output": {"type": "string"}}, "required": ["resource"], "additionalProperties": False},
        "kubectl_describe": {"type": "object", "properties": {"resource": {"type": "string"}, "name": {"type": "string"}, "namespace": {"type": "string"}}, "required": ["resource", "name"], "additionalProperties": False},
        "kubectl_logs": {"type": "object", "properties": {"pod": {"type": "string"}, "namespace": {"type": "string"}, "tail": {"type": "integer"}, "container": {"type": "string"}}, "required": ["pod"], "additionalProperties": False},
        "kubectl_top_pods": {"type": "object", "properties": {"namespace": {"type": "string"}}, "additionalProperties": False},
        "kubectl_rollout": {"type": "object", "properties": {"action": {"type": "string"}, "resource": {"type": "string"}, "namespace": {"type": "string"}}, "required": ["action", "resource"], "additionalProperties": False},
        "kubectl_scale": {"type": "object", "properties": {"deployment": {"type": "string"}, "replicas": {"type": "integer"}, "namespace": {"type": "string"}}, "required": ["deployment", "replicas"], "additionalProperties": False},
        "promql_query": {"type": "object", "properties": {"query": {"type": "string"}}, "required": ["query"], "additionalProperties": False},
        "promql_query_range": {"type": "object", "properties": {"query": {"type": "string"}, "start": {"type": "number"}, "end": {"type": "number"}, "step": {"type": "string"}}, "required": ["query"], "additionalProperties": False},
        "jaeger_search": {"type": "object", "properties": {"service": {"type": "string"}, "lookback": {"type": "string"}, "limit": {"type": "integer"}, "min_duration": {"type": "string"}}, "required": ["service"], "additionalProperties": False},
        "jaeger_get_trace": {"type": "object", "properties": {"trace_id": {"type": "string"}}, "required": ["trace_id"], "additionalProperties": False},
        "argocd_list_apps": {"type": "object", "properties": {}, "additionalProperties": False},
        "argocd_app_history": {"type": "object", "properties": {"app": {"type": "string"}}, "required": ["app"], "additionalProperties": False},
        "argocd_rollback": {"type": "object", "properties": {"app": {"type": "string"}, "revision": {"type": "string"}}, "required": ["app", "revision"], "additionalProperties": False},
        "gcloud_logs_read": {"type": "object", "properties": {"filter_query": {"type": "string"}, "limit": {"type": "integer"}}, "required": ["filter_query"], "additionalProperties": False},
        "cloud_monitoring_query": {"type": "object", "properties": {"metric_type": {"type": "string"}, "lookback_seconds": {"type": "integer"}}, "required": ["metric_type"], "additionalProperties": False},
        "alertmanager_list_alerts": {"type": "object", "properties": {"active_only": {"type": "boolean"}}, "additionalProperties": False},
        "alertmanager_silence": {"type": "object", "properties": {"matchers": {"type": "array"}, "duration_minutes": {"type": "integer"}, "comment": {"type": "string"}}, "required": ["matchers"], "additionalProperties": False},
        "slack_post_update": {"type": "object", "properties": {"channel": {"type": "string"}, "severity": {"type": "string"}, "title": {"type": "string"}, "summary": {"type": "string"}, "action_items": {"type": "array"}}, "required": ["channel", "severity", "title", "summary"], "additionalProperties": False},
        "postmortem_draft": {"type": "object", "properties": {"incident": {"type": "object"}, "output_path": {"type": "string"}}, "required": ["incident"], "additionalProperties": False},
    }
    return {
        "type": "function",
        "function": {
            "name": name,
            "description": f"Real SRE tool: {name}",
            "parameters": schema_map.get(name, {"type": "object", "additionalProperties": False}),
        },
    }


def _extract_severity(user_input: dict[str, Any]) -> str:
    triage = user_input.get("triage", {}) if isinstance(user_input, dict) else {}
    sev = str(triage.get("severity", "")).upper()
    return sev if sev in {"P0", "P1", "P2", "P3"} else "UNKNOWN"


def _check_tool_policy(role: str, tool: str, args: dict[str, Any], user_input: dict[str, Any]) -> str | None:
    if tool not in ROLE_ALLOWED_TOOLS.get(role, set()):
        return f"tool `{tool}` not allowed for role `{role}`"

    if role != "remediation" and tool in MUTATING_TOOLS:
        return f"mutating tool `{tool}` blocked outside remediation role"

    # Guard high-impact operations to avoid accidental destructive actions.
    if role == "remediation":
        severity = _extract_severity(user_input)
        if tool == "alertmanager_silence":
            duration = int(args.get("duration_minutes", 30))
            if duration > 30:
                return "silence duration cannot exceed 30 minutes"
        if tool == "kubectl_scale":
            replicas = int(args.get("replicas", 0))
            if replicas < 0 or replicas > 20:
                return "replicas must stay within 0..20"
        if tool == "argocd_rollback" and severity in {"UNKNOWN", "P3"}:
            return "rollback requires confirmed incident severity P0/P1/P2"
    return None


_TOOL_NAMES_SET = {
    "kubectl_get", "kubectl_logs", "kubectl_describe", "kubectl_top_pods",
    "kubectl_rollout", "kubectl_scale", "promql_query", "promql_query_range",
    "jaeger_search", "jaeger_get_trace", "argocd_list_apps", "argocd_app_history",
    "argocd_rollback", "gcloud_logs_read", "cloud_monitoring_query",
    "alertmanager_list_alerts", "alertmanager_silence",
    "slack_post_update", "postmortem_draft",
}


def _try_parse_json(content: str) -> dict[str, Any]:
    """Extract the largest outermost JSON object from content using bracket-matching.

    Finds each top-level '{...}' block (depth=0) in left-to-right order,
    skipping pure tool-call fragments. Returns the first non-tool-call match
    that has the most keys (largest result).
    """
    if not content:
        return {}
    candidates: list[dict] = []
    i = 0
    while i < len(content):
        if content[i] != "{":
            i += 1
            continue
        depth = 0
        in_string = False
        escape_next = False
        j = i
        while j < len(content):
            c = content[j]
            if escape_next:
                escape_next = False
            elif c == "\\" and in_string:
                escape_next = True
            elif c == '"':
                in_string = not in_string
            elif not in_string:
                if c == "{":
                    depth += 1
                elif c == "}":
                    depth -= 1
                    if depth == 0:
                        try:
                            obj = json.loads(content[i : j + 1])
                            if isinstance(obj, dict) and obj.get("name") not in _TOOL_NAMES_SET:
                                candidates.append(obj)
                        except json.JSONDecodeError:
                            pass
                        break
            j += 1
        i += 1
    if candidates:
        return max(candidates, key=lambda o: len(o))
    return {"raw": content[:500]}


def _remediation_plan_summary(triage: dict[str, Any], diagnosis: dict[str, Any]) -> str:
    severity = str(triage.get("severity", "UNKNOWN"))
    root_cause = str(diagnosis.get("root_cause", "unspecified root cause"))
    plan = str(diagnosis.get("recommended_fix", diagnosis.get("next_action", "apply safe rollback/scale")))
    return f"{severity} incident; root cause: {root_cause}; proposal: {plan}"


def _manual_remediation_record(incident_id: str, triage: dict[str, Any], diagnosis: dict[str, Any]) -> dict[str, Any]:
    summary = _remediation_plan_summary(triage, diagnosis)
    runbook = [
        "Validate blast radius with kubectl_get, promql_query, and jaeger_search.",
        "Review latest Argo CD history for affected app and identify safe rollback target.",
        "Apply remediation manually (rollback/scale) using change-management policy.",
        "Verify error rate and saturation return to baseline for at least 5 minutes.",
        "Document resolution and communicate timeline in comms update.",
    ]
    return {
        "role": "remediation",
        "trajectory": [],
        "final": {
            "incident_id": incident_id,
            "mode": "manual",
            "status": "skipped_execution",
            "summary": summary,
            "runbook": runbook,
        },
    }


def _live_judge_requested() -> bool:
    """Post-incident scoring with the external judge (typically 72B on HF or vLLM).

    Default: ON when ATLASOPS_USE_HF_INFERENCE=1 so Space demos exercise both models.
    Opt out: ATLASOPS_LIVE_JUDGE=0
    """
    flag = os.getenv("ATLASOPS_LIVE_JUDGE", "").strip().lower()
    if flag in ("0", "false", "no", "off"):
        return False
    if flag in ("1", "true", "yes", "on"):
        return True
    return os.getenv("ATLASOPS_USE_HF_INFERENCE", "").strip().lower() in ("1", "true", "yes")


async def handle_incident(alert: dict[str, Any], incident_id: str | None = None) -> dict[str, Any]:
    """Run the full agent chain for one incident."""
    incident_id = incident_id or f"inc-{int(time.time())}-{uuid.uuid4().hex[:6]}"
    log.info("[%s] handling alert: %s", incident_id, alert.get("commonLabels", {}).get("alertname"))
    audit_log.record(
        incident_id=incident_id,
        agent_role="coordinator",
        action_type="incident_start",
        result_summary=alert.get("commonLabels", {}).get("alertname", "unknown-alert"),
    )
    circuit_breaker.start_incident()

    resolved = False
    try:
        triage = await call_agent("triage", {"incident_id": incident_id, "alert": alert})
        diagnosis = await call_agent("diagnosis", {"incident_id": incident_id, "triage": triage["final"]})
        severity = _extract_severity({"triage": triage.get("final", {})})
        approval_mode = approval_mode_for_severity(severity)
        remediation_input = {
            "incident_id": incident_id,
            "triage": triage["final"],
            "diagnosis": diagnosis["final"],
            "approval_mode": approval_mode,
        }
        if approval_mode == "manual":
            thought_emit(
                "remediation",
                "waiting_approval",
                "Manual mode for P0 incident — generating runbook for human execution.",
            )
            remediation = _manual_remediation_record(incident_id, triage["final"], diagnosis["final"])
        elif approval_mode == "approve":
            summary = _remediation_plan_summary(triage["final"], diagnosis["final"])
            req = approval_gate.request(incident_id=incident_id, severity=severity, summary=summary)
            audit_log.record(
                incident_id=incident_id,
                agent_role="remediation",
                action_type="approval_requested",
                result_summary=summary,
                policy_check="requires_approval",
            )
            thought_emit(
                "remediation",
                "waiting_approval",
                f"Awaiting human approval for remediation plan (token: {req.token}).",
            )
            approval_result = await approval_gate.wait_for_decision(incident_id)
            status = approval_result["status"]
            # "timeout" auto-approves so demos/unattended runs still complete.
            # Only an explicit "rejected" decision skips remediation.
            if status == "rejected":
                audit_log.record(
                    incident_id=incident_id,
                    agent_role="remediation",
                    action_type="approval_decision",
                    result_summary=status,
                    approved_by=approval_result.get("approved_by", ""),
                    policy_check="approval_denied",
                )
                thought_emit("remediation", "conclusion", "Remediation skipped — approval rejected by operator.")
                remediation = {
                    "role": "remediation",
                    "trajectory": [],
                    "final": {
                        "incident_id": incident_id,
                        "mode": "approve",
                        "status": "approval_rejected",
                        "approval": approval_result,
                    },
                }
            else:
                approver = approval_result.get("approved_by") or (
                    "auto-timeout" if status == "timeout" else "human-operator"
                )
                audit_log.record(
                    incident_id=incident_id,
                    agent_role="remediation",
                    action_type="approval_decision",
                    result_summary="approved",
                    approved_by=approver,
                    policy_check="approval_granted",
                )
                thought_emit("remediation", "thinking", f"Approval granted by {approver}; executing plan.")
                remediation_input["approval"] = approval_result
                remediation = await call_agent("remediation", remediation_input)
        else:
            remediation = await call_agent("remediation", remediation_input)
        comms = await call_agent("comms", {
            "incident_id": incident_id,
            "triage": triage["final"],
            "diagnosis": diagnosis["final"],
            "remediation": remediation["final"],
        })

        full_record = {
            "incident_id": incident_id,
            "alert": alert,
            "triage": triage,
            "diagnosis": diagnosis,
            "remediation": remediation,
            "comms": comms,
        }
        (TRAJECTORIES_DIR / f"{incident_id}.json").write_text(
            json.dumps(full_record, indent=2), encoding="utf-8",
        )
        if _live_judge_requested():
            from agents.judge import infer_tier_from_alert, judge_trajectory

            tier = infer_tier_from_alert(alert)
            jm = os.getenv("JUDGE_MODEL", "judge-model")
            thought_emit("comms", "tool_call", f"Scoring incident with external judge ({tier} rubric)…", tool="judge")
            try:
                scores = await judge_trajectory(full_record, tier=tier)
                ov = float(scores.get("overall", 0.0))
                crit = str(scores.get("critique", ""))[:400]
                thought_emit(
                    "comms",
                    "tool_result",
                    f"{jm} — overall {ov:.2f}. {crit}".strip(),
                    tool="judge_trajectory",
                )
            except Exception as e:
                log.warning("live judge failed: %s", e)
                thought_emit(
                    "comms",
                    "tool_result",
                    f"Judge request failed ({type(e).__name__}: {e}). Scores skipped.",
                    tool="judge_trajectory",
                )

        # Derive resolved from actual remediation outcome, not just non-rejection.
        # "skipped_execution" (P0 manual), "approval_rejected", "approval_timeout"
        # are all non-resolved states. Only explicit "resolved" outcome counts.
        remediation_final = remediation.get("final", {})
        resolved = (
            remediation_final.get("outcome") == "resolved"
            or remediation_final.get("status") == "resolved"
        )
        audit_log.record(
            incident_id=incident_id,
            agent_role="coordinator",
            action_type="incident_end",
            result_summary="resolved" if resolved else "not_resolved",
        )
        return full_record
    finally:
        circuit_breaker.finish_incident(incident_id, resolved=resolved)


app = FastAPI(title="AtlasOps Coordinator")


_WEBHOOK_SECRET = os.getenv("ALERTMANAGER_WEBHOOK_SECRET", "")


@app.post("/webhook")
async def webhook(request: Request):
    body = await request.body()

    # Validate Bearer token when secret is configured
    if _WEBHOOK_SECRET:
        import hmac as _hmac
        auth = request.headers.get("Authorization", "")
        if not auth.startswith("Bearer "):
            from fastapi import HTTPException
            raise HTTPException(status_code=401, detail="Missing Authorization header")
        token = auth.removeprefix("Bearer ").strip()
        if not _hmac.compare_digest(token.encode(), _WEBHOOK_SECRET.encode()):
            from fastapi import HTTPException
            raise HTTPException(status_code=401, detail="Invalid webhook secret")

    payload = AlertWebhookPayload.model_validate(json.loads(body)).model_dump()
    log.info("received alertmanager webhook: %d alerts", len(payload.get("alerts", [])))
    incident_id, _is_new, should_dispatch = correlator.ingest(payload)
    if not should_dispatch:
        return JSONResponse({"ok": True, "incident_id": incident_id, "correlated": True, "dispatched": False})
    correlator.mark_processing(incident_id, True)
    try:
        result = await handle_incident(payload, incident_id=incident_id)
        return JSONResponse({"ok": True, "incident_id": result["incident_id"], "correlated": True, "dispatched": True})
    finally:
        correlator.mark_processing(incident_id, False)


@app.get("/stream")
async def stream_thoughts():
    """SSE endpoint — dashboard subscribes here for live agent thoughts."""
    from agents.stream import subscribe
    return StreamingResponse(
        subscribe(),
        media_type="text/event-stream",
        headers={"Cache-Control": "no-cache", "X-Accel-Buffering": "no"},
    )


@app.get("/thoughts")
async def get_thoughts():
    """Return full thought history for the timeline tab."""
    from agents.stream import get_history
    return {"thoughts": get_history()}


@app.get("/health")
async def health():
    ju = os.getenv("JUDGE_URL", "").rstrip("/")
    return {
        "status": "ok",
        "vllm": VLLM_BASE,
        "model": MODEL_NAME,
        "judge_url": ju,
        "judge_model": os.getenv("JUDGE_MODEL", ""),
        "live_judge": _live_judge_requested(),
        "hf_inference_pack": os.getenv("ATLASOPS_USE_HF_INFERENCE", ""),
    }


@app.get("/metrics")
async def coordinator_prometheus_metrics():
    """When this app is mounted at `/api` on the Space, this serves `GET /api/metrics`."""
    return JSONResponse(await build_dashboard_metrics_payload())


@app.get("/alertmanager/alerts")
async def coordinator_alertmanager_feed():
    """Serves `GET /api/alertmanager/alerts` on the Space (mounted at `/api`)."""
    result = alertmanager_list_alerts(active_only=True)
    payload: dict[str, Any] = {"count": result.get("count", 0), "alerts": result.get("alerts", [])}
    if not result.get("success"):
        payload["error"] = result.get("error", "alertmanager_unreachable")
    return JSONResponse(payload)


if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=9099)