"""
HaramGuard — RealTimePipeline
================================
Orchestrates all agents frame-by-frame.
`pipeline.state` is the single shared dict read by the Streamlit dashboard.

Agent execution order per frame:
  1. PerceptionAgent  → detect & track persons
  2. RiskAgent        → compute risk score
  3. ReflectionAgent  → self-critique & correct if biased
  4. OperationsAgent  → emit decision on level change (event-driven)
  5. CoordinatorAgent → LLM decides gates + actions for ALL priorities (P0/P1/P2)
"""

import time
import cv2
import numpy as np
import threading
from collections import deque

import config
from core.database import HajjFlowDB
from agents.perception_agent  import PerceptionAgent
from agents.risk_agent        import RiskAgent
from agents.reflection_agent  import ReflectionAgent
from agents.operations_agent  import OperationsAgent
from agents.coordinator_agent import CoordinatorAgent


class RealTimePipeline:

    def __init__(self,
                 video_path:    str,
                 groq_api_key:    str,
                 anthropic_key: str = None,
                 model_path:    str = 'yolov10n.pt',
                 db_path:       str = 'outputs/hajjflow_rt.db'):

        self.video_path = video_path
        self.db         = HajjFlowDB(db_path)

        # ── Instantiate agents ────────────────────────────────────────
        # Cached mode: read pre-computed detections; Live mode: run YOLO
        cached_path = getattr(config, 'CACHED_DETECTIONS_PATH', None)
        self.perception  = PerceptionAgent(model_path, anthropic_key=None, cached_path=cached_path)
        self.risk        = RiskAgent()
        self.reflection  = ReflectionAgent()
        self.operations  = OperationsAgent(self.db)
        self.coordinator = CoordinatorAgent(groq_api_key)
        self._last_effective_risk_level = 'LOW'

        self._frame_buffer = deque(maxlen=30)

        # ── Shared state — read by Streamlit dashboard ─────────────────
        self.state = {
            # PerceptionAgent outputs
            'frame_id':        0,
            'annotated':       None,      # np.ndarray BGR
            'person_count':    0,
            'density_score':   0.0,
            'fps':             0.0,

            # RiskAgent outputs (after ReflectionAgent correction)
            'risk_score':      0.0,
            'risk_level':      'LOW',
            'trend':           'stable',
            'risk_history':    [],        # list[dict] for chart

            # OperationsAgent outputs
            'latest_decision': None,      # Decision dataclass or None
            'decisions_log':   [],        # last 10 decisions from DB

            # CoordinatorAgent outputs (P0 only)
            'arabic_alert':      '',
            'coordinator_plan':  None,

            # ReflectionAgent outputs
            'reflection_summary': {},
            'last_reflection':    None,
        }
        print('🚀 [Pipeline] Initialized — all agents ready')

    # ── Video capture helper ──────────────────────────────────────────

    def get_video_capture(self) -> cv2.VideoCapture:
        cap = cv2.VideoCapture(self.video_path)
        if not cap.isOpened():
            raise RuntimeError(f'Cannot open video: {self.video_path}')
        return cap

    # ── Per-frame processing ──────────────────────────────────────────

    def process_one_frame(self, frame: np.ndarray) -> None:
        """
        Run all agents on a single frame and update self.state.
        Called from the Streamlit loop or batch runner.
        """
        t0 = time.time()

        # Agent 1 — Perception
        fr = self.perception.process_frame(frame)
        self._frame_buffer.append(fr)

        # Agent 2 — Risk scoring
        rr = self.risk.process_frame(fr)
        original_level_changed = rr.level_changed

        # Agent 3 — Reflection (self-critique)
        reflection = self.reflection.reflect(rr, fr)
        if reflection['bias_detected']:
            rr.risk_level = reflection['corrected_level']
            rr.risk_score = reflection['corrected_score']
        # Ensure event-driven decisions use effective risk after reflection.
        effective_level_changed = rr.risk_level != self._last_effective_risk_level
        rr.level_changed = bool(original_level_changed or effective_level_changed)
        self._last_effective_risk_level = rr.risk_level
        self.db.save_reflection(reflection)

        # Save risk event every 30 frames
        if fr.frame_id % 30 == 0:
            self.db.save_risk_event(rr)

        # Agent 4 — Operations (event-driven)
        decision = self.operations.process(rr, context='Mecca_Main_Area')

        # Agent 5 — Coordinator (all priorities: P0/P1/P2)
        # LLM runs in background thread so it doesn't block frame processing.
        if decision:
            self._launch_coordinator(rr, decision, list(self._frame_buffer))

        # ── Update shared state ───────────────────────────────────────
        fps = round(1.0 / (time.time() - t0 + 1e-9), 1)
        self.state.update({
            'frame_id':           fr.frame_id,
            'annotated':          fr.annotated,
            'person_count':       fr.person_count,
            'density_score':      fr.density_score,
            'density_ema':        rr.density_ema,
            'density_pct':        rr.density_pct,
            'fps':                fps,
            'risk_score':         rr.risk_score,
            'risk_level':         rr.risk_level,
            'trend':              rr.trend,
            'risk_history':       self.db.get_risk_history(60),
            'reflection_summary': self.reflection.get_summary(),
            'last_reflection':    reflection,
        })

        if decision:
            self.state['latest_decision'] = decision
            self.state['decisions_log']   = self.db.get_recent_decisions(10)

    def _launch_coordinator(self, rr, decision, frame_buffer):
        """Run CoordinatorAgent LLM call in a background thread."""
        def _run():
            try:
                plan = self.coordinator.call(rr, decision, frame_buffer)
                if plan:
                    decision.actions        = plan.get('immediate_actions', [])
                    decision.justification  = plan.get('actions_justification', '')
                    decision.selected_gates = plan.get('selected_gates', [])
                    self.db.save_coordinator_plan(rr.frame_id, plan)
                    self.state['coordinator_plan'] = plan
                    self.state['arabic_alert']     = plan.get('arabic_alert', '')
                    self.state['latest_decision']  = decision
                    self.state['decisions_log']    = self.db.get_recent_decisions(10)
            except Exception as exc:
                print(f'[Pipeline] Coordinator error: {exc}')

        threading.Thread(target=_run, daemon=True).start()