"""
LangGraph pipeline graph for brand detection.

Nodes execute real logic for extract+filter, stubs for the rest.
Each node emits graph_update events so the UI can visualize transitions.
"""

from __future__ import annotations

import os

from langgraph.graph import END, StateGraph

from detect import emit
from detect.models import PipelineStats
from detect.profiles import SoccerBroadcastProfile
from detect.state import DetectState
from detect.stages.frame_extractor import extract_frames
from detect.stages.scene_filter import scene_filter
from detect.stages.yolo_detector import detect_objects
from detect.stages.ocr_stage import run_ocr
from detect.stages.brand_resolver import resolve_brands
from detect.stages.vlm_local import escalate_vlm
from detect.stages.vlm_cloud import escalate_cloud
from detect.stages.aggregator import compile_report
from detect.tracing import trace_node, flush as flush_traces

INFERENCE_URL = os.environ.get("INFERENCE_URL")  # None = local mode

NODES = [
    "extract_frames",
    "filter_scenes",
    "detect_objects",
    "run_ocr",
    "match_brands",
    "escalate_vlm",
    "escalate_cloud",
    "compile_report",
]


def _get_profile(state: DetectState):
    name = state.get("profile_name", "soccer_broadcast")
    if name == "soccer_broadcast":
        return SoccerBroadcastProfile()
    raise ValueError(f"Unknown profile: {name}")


# Track node states across the pipeline run
_node_states: dict[str, dict[str, str]] = {}


def _emit_transition(state: DetectState, node: str, status: str):
    job_id = state.get("job_id")
    if not job_id:
        return

    # Initialize state tracking for this job
    if job_id not in _node_states:
        _node_states[job_id] = {n: "pending" for n in NODES}

    _node_states[job_id][node] = status

    nodes = [{"id": n, "status": _node_states[job_id][n]} for n in NODES]
    emit.graph_update(job_id, nodes)


# --- Node functions ---

def node_extract_frames(state: DetectState) -> dict:
    _emit_transition(state, "extract_frames", "running")

    with trace_node(state, "extract_frames") as span:
        profile = _get_profile(state)
        config = profile.frame_extraction_config()
        frames = extract_frames(state["video_path"], config, job_id=state.get("job_id"))
        span.set_output({"frames_extracted": len(frames)})

    _emit_transition(state, "extract_frames", "done")
    return {"frames": frames, "stats": PipelineStats(frames_extracted=len(frames))}


def node_filter_scenes(state: DetectState) -> dict:
    _emit_transition(state, "filter_scenes", "running")

    with trace_node(state, "filter_scenes") as span:
        profile = _get_profile(state)
        config = profile.scene_filter_config()
        frames = state.get("frames", [])
        kept = scene_filter(frames, config, job_id=state.get("job_id"))
        span.set_output({"frames_in": len(frames), "frames_kept": len(kept)})

    stats = state.get("stats", PipelineStats())
    stats.frames_after_scene_filter = len(kept)

    _emit_transition(state, "filter_scenes", "done")
    return {"filtered_frames": kept, "stats": stats}


def node_detect_objects(state: DetectState) -> dict:
    _emit_transition(state, "detect_objects", "running")

    with trace_node(state, "detect_objects") as span:
        profile = _get_profile(state)
        config = profile.detection_config()
        frames = state.get("filtered_frames", [])
        job_id = state.get("job_id")

        all_boxes = detect_objects(frames, config, inference_url=INFERENCE_URL, job_id=job_id)
        total_regions = sum(len(boxes) for boxes in all_boxes.values())
        span.set_output({"frames": len(frames), "regions_detected": total_regions})

    stats = state.get("stats", PipelineStats())
    stats.regions_detected = total_regions

    _emit_transition(state, "detect_objects", "done")
    return {"boxes_by_frame": all_boxes, "stats": stats}


def node_run_ocr(state: DetectState) -> dict:
    _emit_transition(state, "run_ocr", "running")

    with trace_node(state, "run_ocr") as span:
        profile = _get_profile(state)
        config = profile.ocr_config()
        frames = state.get("filtered_frames", [])
        boxes = state.get("boxes_by_frame", {})
        job_id = state.get("job_id")

        candidates = run_ocr(frames, boxes, config, inference_url=INFERENCE_URL, job_id=job_id)
        span.set_output({"regions_in": sum(len(b) for b in boxes.values()), "text_candidates": len(candidates)})

    stats = state.get("stats", PipelineStats())
    stats.regions_resolved_by_ocr = len(candidates)

    _emit_transition(state, "run_ocr", "done")
    return {"text_candidates": candidates, "stats": stats}


def node_match_brands(state: DetectState) -> dict:
    _emit_transition(state, "match_brands", "running")

    with trace_node(state, "match_brands") as span:
        profile = _get_profile(state)
        dictionary = profile.brand_dictionary()
        resolver_config = profile.resolver_config()
        candidates = state.get("text_candidates", [])
        job_id = state.get("job_id")

        matched, unresolved = resolve_brands(
            candidates, dictionary, resolver_config,
            content_type=profile.name, job_id=job_id,
        )
        span.set_output({"matched": len(matched), "unresolved": len(unresolved)})

    _emit_transition(state, "match_brands", "done")
    return {"detections": matched, "unresolved_candidates": unresolved}


def node_escalate_vlm(state: DetectState) -> dict:
    _emit_transition(state, "escalate_vlm", "running")

    with trace_node(state, "escalate_vlm") as span:
        profile = _get_profile(state)
        candidates = state.get("unresolved_candidates", [])
        job_id = state.get("job_id")

        vlm_matched, still_unresolved = escalate_vlm(
            candidates,
            vlm_prompt_fn=profile.vlm_prompt,
            inference_url=INFERENCE_URL,
            content_type=profile.name,
            job_id=job_id,
        )

        stats = state.get("stats", PipelineStats())
        stats.regions_escalated_to_local_vlm = len(candidates)
        span.set_output({"candidates": len(candidates), "matched": len(vlm_matched),
                         "still_unresolved": len(still_unresolved)})

    existing = state.get("detections", [])

    _emit_transition(state, "escalate_vlm", "done")
    return {
        "detections": existing + vlm_matched,
        "unresolved_candidates": still_unresolved,
        "stats": stats,
    }


def node_escalate_cloud(state: DetectState) -> dict:
    _emit_transition(state, "escalate_cloud", "running")

    with trace_node(state, "escalate_cloud") as span:
        profile = _get_profile(state)
        candidates = state.get("unresolved_candidates", [])
        job_id = state.get("job_id")
        stats = state.get("stats", PipelineStats())

        cloud_matched = escalate_cloud(
            candidates,
            vlm_prompt_fn=profile.vlm_prompt,
            stats=stats,
            content_type=profile.name,
            job_id=job_id,
        )

        span.set_output({"candidates": len(candidates), "matched": len(cloud_matched),
                         "cloud_calls": stats.cloud_llm_calls,
                         "cost_usd": stats.estimated_cloud_cost_usd})

    existing = state.get("detections", [])

    _emit_transition(state, "escalate_cloud", "done")
    return {"detections": existing + cloud_matched, "stats": stats}


def node_compile_report(state: DetectState) -> dict:
    _emit_transition(state, "compile_report", "running")

    with trace_node(state, "compile_report") as span:
        profile = _get_profile(state)
        detections = state.get("detections", [])
        stats = state.get("stats", PipelineStats())
        job_id = state.get("job_id")

        report = compile_report(
            detections=detections,
            stats=stats,
            video_source=state.get("video_path", ""),
            content_type=profile.name,
            job_id=job_id,
        )

        span.set_output({"brands": len(report.brands), "detections": len(report.timeline)})

    flush_traces()
    _emit_transition(state, "compile_report", "done")
    return {"report": report}


# --- Graph construction ---

def build_graph() -> StateGraph:
    graph = StateGraph(DetectState)

    graph.add_node("extract_frames", node_extract_frames)
    graph.add_node("filter_scenes", node_filter_scenes)
    graph.add_node("detect_objects", node_detect_objects)
    graph.add_node("run_ocr", node_run_ocr)
    graph.add_node("match_brands", node_match_brands)
    graph.add_node("escalate_vlm", node_escalate_vlm)
    graph.add_node("escalate_cloud", node_escalate_cloud)
    graph.add_node("compile_report", node_compile_report)

    graph.set_entry_point("extract_frames")
    graph.add_edge("extract_frames", "filter_scenes")
    graph.add_edge("filter_scenes", "detect_objects")
    graph.add_edge("detect_objects", "run_ocr")
    graph.add_edge("run_ocr", "match_brands")
    graph.add_edge("match_brands", "escalate_vlm")
    graph.add_edge("escalate_vlm", "escalate_cloud")
    graph.add_edge("escalate_cloud", "compile_report")
    graph.add_edge("compile_report", END)

    return graph


def get_pipeline():
    """Return a compiled, runnable pipeline."""
    return build_graph().compile()