from dataclasses import dataclass, asdict
from typing import Dict, Any
from ..core.config import Config
from .fingerprint import Fingerprinter
from .pvi import PVIEstimator

@dataclass
class DatasetFingerprint:
    stats: Dict[str, Any]
    visual: Dict[str, Any]
    complexity: Dict[str, Any]
    quality: Dict[str, Any]

class DatasetIntelligenceEngine:
    """
    Core engine for understanding dataset characteristics before any training happens.
    """

    def __init__(self, config: Config = None):
        self.config = config if config else Config()
        self.fingerprinter = Fingerprinter(self.config)
        self.pvi_estimator = PVIEstimator(self.config)

    def analyze(self, dataset_path: str) -> Dict[str, Any]:
        """
        Performs a comprehensive analysis of the dataset.
        Returns a dictionary representation of the DatasetFingerprint.
        """
        print(f"Analyzing dataset at: {dataset_path}")
        
        # 1. Basic Statistics
        stats = self.fingerprinter.compute_stats(dataset_path)
        print(f"Found {stats['num_classes']} classes and {stats['total_samples']} samples.")
        
        # 2. Visual Fingerprint
        visual = self.fingerprinter.compute_visual_fingerprint(dataset_path)
        
        # 3. Complexity Estimation (PVI, ID, ED)
        pvi_stats = self.pvi_estimator.compute_pvi(dataset_path)
        complexity = {
            "pvi": pvi_stats,
            "intrinsic_dimension": "estimated_12.5" # Placeholder
        }
        
        # 4. Quality Check (Logic to be added)
        quality = {
            "duplicates_found": 0,
            "suspected_mislabels": 0
        }
        
        fingerprint = DatasetFingerprint(
            stats=stats,
            visual=visual,
            complexity=complexity,
            quality=quality
        )
        
        return asdict(fingerprint)
