"""
ContentTypeProfile protocol and config dataclasses.

Each profile defines the pipeline topology (as a JSONB blob), stage configs,
brand dictionary, VLM prompt templates, and aggregation strategy.

When profiles are persisted, the pipeline field is a JSONB column.
For now, profiles are code-only and pipeline_config() returns a hardcoded value.
"""

from __future__ import annotations

from dataclasses import dataclass, field
from typing import Any, Dict, Protocol

from detect.models import BrandDetection, DetectionReport
from core.schema.models.pipeline_config import PipelineConfig, StageRef, Edge


@dataclass
class FrameExtractionConfig:
    fps: float = 2.0
    max_frames: int = 500


@dataclass
class SceneFilterConfig:
    hamming_threshold: int = 8
    enabled: bool = True


@dataclass
class DetectionConfig:
    model_name: str = "yolov8n.pt"
    confidence_threshold: float = 0.3
    target_classes: list[str] = field(default_factory=lambda: ["logo", "text"])


@dataclass
class OCRConfig:
    languages: list[str] = field(default_factory=lambda: ["en"])
    min_confidence: float = 0.5


@dataclass
class ResolverConfig:
    fuzzy_threshold: int = 75


@dataclass
class RegionAnalysisConfig:
    enabled: bool = True
    # Edge detection (Canny + HoughLinesP)
    edge_canny_low: int = 50
    edge_canny_high: int = 150
    edge_hough_threshold: int = 80
    edge_hough_min_length: int = 100
    edge_hough_max_gap: int = 10
    edge_pair_max_distance: int = 200
    edge_pair_min_distance: int = 15


@dataclass
class CropContext:
    image: bytes
    surrounding_text: str = ""
    position_hint: str = ""


def pipeline_config_from_dict(data: Dict[str, Any]) -> PipelineConfig:
    """Deserialize a PipelineConfig from a JSONB dict."""
    stages = [StageRef(**s) for s in data.get("stages", [])]
    edges = [Edge(**e) for e in data.get("edges", [])]
    return PipelineConfig(
        name=data.get("name", ""),
        profile_name=data.get("profile_name", ""),
        stages=stages,
        edges=edges,
        routing_rules=data.get("routing_rules", {}),
    )


class ContentTypeProfile(Protocol):
    name: str
    pipeline: Dict[str, Any]  # JSONB blob — PipelineConfig shape

    def pipeline_config(self) -> PipelineConfig: ...
    def frame_extraction_config(self) -> FrameExtractionConfig: ...
    def scene_filter_config(self) -> SceneFilterConfig: ...
    def region_analysis_config(self) -> RegionAnalysisConfig: ...
    def detection_config(self) -> DetectionConfig: ...
    def ocr_config(self) -> OCRConfig: ...
    def resolver_config(self) -> ResolverConfig: ...
    def vlm_prompt(self, crop_context: CropContext) -> str: ...
    def aggregate(self, detections: list[BrandDetection]) -> DetectionReport: ...
    def auxiliary_detections(self, source: str) -> list[BrandDetection]: ...