"""
OSINT service — orchestrates reverse-image-search across multiple
providers and merges results into a unified evidence set.

This is NOT a provider itself — it's a service that:
  1. Runs the existing reverse-search providers (serpapi, social_lookup)
     via the orchestrator.
  2. Collects raw results from each provider.
  3. Merges + deduplicates via cores.osint.merger.
  4. Classifies each source (social/news/forum/marketplace/etc.) via
     cores.osint.sources.
  5. Returns an OSINTResult with confidence-scored, classified matches.

Pure stdlib for the merging logic; relies on existing providers for
network calls.
"""

from __future__ import annotations

import time
from typing import Optional

from cores.osint import merge_reverse_results, classify_source, SourceType
from metrics.collector import MetricsCollector
from models.jobs import JobRequest
from models.reports import OSINTResult, OSINTMatch
from models.providers import ProviderCapability
from orchestrator.runner import Orchestrator
from pipeline import InputValidator, ImagePreprocessor, ImageHasher, FeatureExtractor
from utils.logging import execution_context, new_execution_id


class OSINTService:
    """Orchestrates reverse-image-search across providers + merges results."""

    def __init__(
        self,
        orchestrator: Orchestrator,
        metrics: MetricsCollector,
        validator: InputValidator,
        preprocessor: ImagePreprocessor,
        hasher: ImageHasher,
        feature_extractor: FeatureExtractor,
    ) -> None:
        self._orchestrator = orchestrator
        self._metrics = metrics
        self._validator = validator
        self._preprocessor = preprocessor
        self._hasher = hasher
        self._feature_extractor = feature_extractor

    async def reverse_search(self, request: JobRequest) -> dict:
        """Run reverse-image-search across all enabled providers + merge."""
        eid = new_execution_id()
        with execution_context(execution_id=eid, provider_id="osint_service"):
            t0 = time.perf_counter()

            # Validate input — OSINT requires an image (URL or base64)
            vr = self._validator.validate(
                image_url=request.image_url,
                image_base64=request.image_base64,
            )
            if not vr.valid:
                return {"success": False, "error": vr.error, "error_type": "ValidationError"}

            # Preprocess
            if vr.source == "url":
                pre = self._preprocessor.from_url(request.image_url)
            else:
                pre = self._preprocessor.from_bytes(vr.image_bytes, vr.source)

            img_hash = self._hasher.hash(pre.image)
            pipeline_output = self._feature_extractor.extract(
                pre.image, img_hash, pre.width, pre.height, pre.source,
                original_bytes=pre.original_bytes,
                original_format=pre.original_format,
            )

            # If the user provided a URL, attach it so social_lookup can use it
            if request.image_url:
                pipeline_output.scrape_url = request.image_url

            # Run all reverse-search providers
            results = await self._orchestrator.run(
                pipeline_output=pipeline_output,
                capabilities=[ProviderCapability.REVERSE_SEARCH],
                provider_whitelist=request.providers or None,
                execution_id=eid,
            )

            # Collect raw matches per provider
            provider_results: dict[str, list[dict]] = {}
            providers_invoked: list[str] = []
            providers_succeeded: list[str] = []
            providers_failed: list[str] = []

            for name, result in results.items():
                providers_invoked.append(name)
                if result.success:
                    providers_succeeded.append(name)
                    raw_matches = result.normalized.get("results", [])
                    if raw_matches:
                        provider_results[name] = raw_matches
                else:
                    providers_failed.append(name)

            # Merge + classify
            merged = merge_reverse_results(provider_results)

            # Build source-type breakdown
            breakdown: dict[str, int] = {}
            for m in merged:
                st = m.get("source_type", "unknown")
                breakdown[st] = breakdown.get(st, 0) + 1

            elapsed = (time.perf_counter() - t0) * 1000.0
            osint_result = OSINTResult(
                providers_invoked=providers_invoked,
                providers_succeeded=providers_succeeded,
                providers_failed=providers_failed,
                total_matches=len(merged),
                matches=[OSINTMatch(**m) for m in merged],
                source_type_breakdown=breakdown,
                elapsed_ms=round(elapsed, 3),
            )

            self._metrics.timings.record("job.osint", elapsed)
            self._metrics.counters.inc("jobs.osint.completed")

            return {
                "success": True,
                "osint": osint_result.model_dump(),
                "elapsed_ms": round(elapsed, 3),
            }