"""
Dataset Loader Module (Refactored)

Generic dataset loading supporting multiple formats:
- OBO (Open Biomedical Ontologies)
- CSV/TSV
- JSON/JSON-LD
- Custom adapters

Configuration-driven to support any domain, not just medical.
"""

import os
import re
import json
import csv
import logging
import hashlib
import urllib.request
from pathlib import Path
from abc import ABC, abstractmethod
from datetime import datetime
from typing import Dict, List, Optional, Tuple, Any, Type
from dataclasses import dataclass, field

from .knowledge_graph import Entity, EntityCategory, KnowledgeGraph
from .config import DatasetConfig, get_config

logger = logging.getLogger(__name__)


@dataclass
class OntologyTerm:
    """Generic ontology term representation."""
    id: str
    name: str
    definition: str = ""
    synonyms: List[str] = field(default_factory=list)
    xrefs: Dict[str, str] = field(default_factory=dict)
    is_a: List[str] = field(default_factory=list)
    relationships: List[Tuple[str, str]] = field(default_factory=list)
    namespace: str = ""
    is_obsolete: bool = False
    
    def to_entity(self, category: EntityCategory) -> Entity:
        """Convert to Entity."""
        return Entity(
            id=self.id,
            name=self.name,
            category=category,
            description=self.definition,
            synonyms=self.synonyms,
            xrefs=self.xrefs,
            properties={"is_a": self.is_a, "namespace": self.namespace}
        )


class DatasetAdapter(ABC):
    """Abstract base class for dataset adapters."""
    
    @abstractmethod
    def parse(self, content: str) -> Dict[str, OntologyTerm]:
        """Parse content and return dictionary of terms."""
        pass
    
    @abstractmethod
    def can_handle(self, source_type: str) -> bool:
        """Check if this adapter can handle the source type."""
        pass


class OBOAdapter(DatasetAdapter):
    """Parser for OBO (Open Biomedical Ontologies) format."""
    
    def can_handle(self, source_type: str) -> bool:
        return source_type.lower() == "obo"
    
    def parse(self, content: str) -> Dict[str, OntologyTerm]:
        """Parse OBO format content."""
        terms = {}
        
        # Split into stanzas
        stanzas = re.split(r'\n\[', content)
        
        for stanza in stanzas[1:]:  # Skip header
            if stanza.startswith('Term]'):
                term = self._parse_term(stanza[5:])
                if term and not term.is_obsolete:
                    terms[term.id] = term
        
        logger.info(f"Parsed {len(terms)} terms from OBO content")
        return terms
    
    def _parse_term(self, stanza: str) -> Optional[OntologyTerm]:
        """Parse a single term stanza."""
        data = {
            "id": "", "name": "", "definition": "",
            "synonyms": [], "xrefs": {}, "is_a": [],
            "relationships": [], "namespace": "", "is_obsolete": False
        }
        
        for line in stanza.split('\n'):
            line = line.strip()
            if not line or line.startswith('!') or ':' not in line:
                continue
            
            tag, _, value = line.partition(':')
            tag, value = tag.strip(), value.strip()
            
            if tag == 'id':
                data['id'] = value
            elif tag == 'name':
                data['name'] = value
            elif tag == 'def':
                match = re.match(r'"([^"]*)"', value)
                if match:
                    data['definition'] = match.group(1)
            elif tag == 'synonym':
                match = re.match(r'"([^"]*)"', value)
                if match:
                    data['synonyms'].append(match.group(1))
            elif tag == 'xref':
                if ':' in value:
                    xref_ns, _, xref_id = value.partition(':')
                    xref_id = xref_id.split()[0] if ' ' in xref_id else xref_id
                    data['xrefs'][xref_ns.strip()] = xref_id.strip()
            elif tag == 'is_a':
                parent_id = value.split('!')[0].strip()
                data['is_a'].append(parent_id)
            elif tag == 'relationship':
                parts = value.split()
                if len(parts) >= 2:
                    data['relationships'].append((parts[0], parts[1]))
            elif tag == 'is_obsolete':
                data['is_obsolete'] = value.lower() == 'true'
            elif tag == 'namespace':
                data['namespace'] = value
        
        if data['id'] and data['name']:
            return OntologyTerm(**data)
        return None


class CSVAdapter(DatasetAdapter):
    """Parser for CSV/TSV format datasets."""
    
    # Default column mappings
    DEFAULT_MAPPINGS = {
        "id": ["id", "ID", "identifier", "code"],
        "name": ["name", "Name", "label", "Label", "title"],
        "definition": ["definition", "description", "Description", "desc"],
        "synonyms": ["synonyms", "aliases", "alt_names"],
    }
    
    def __init__(self, column_mappings: Optional[Dict[str, str]] = None):
        self.column_mappings = column_mappings or {}
    
    def can_handle(self, source_type: str) -> bool:
        return source_type.lower() in ["csv", "tsv"]
    
    def parse(self, content: str) -> Dict[str, OntologyTerm]:
        """Parse CSV content."""
        terms = {}
        
        # Detect delimiter
        dialect = csv.Sniffer().sniff(content[:1024])
        reader = csv.DictReader(content.splitlines(), dialect=dialect)
        
        # Map columns
        col_map = self._map_columns(reader.fieldnames or [])
        
        for row in reader:
            term = self._row_to_term(row, col_map)
            if term:
                terms[term.id] = term
        
        logger.info(f"Parsed {len(terms)} terms from CSV content")
        return terms
    
    def _map_columns(self, fieldnames: List[str]) -> Dict[str, str]:
        """Map fieldnames to standard term fields."""
        col_map = {}
        
        for field, possible_names in self.DEFAULT_MAPPINGS.items():
            # Check explicit mappings first
            if field in self.column_mappings:
                col_map[field] = self.column_mappings[field]
            else:
                # Try to auto-detect
                for name in possible_names:
                    if name in fieldnames:
                        col_map[field] = name
                        break
        
        return col_map
    
    def _row_to_term(self, row: Dict, col_map: Dict[str, str]) -> Optional[OntologyTerm]:
        """Convert CSV row to OntologyTerm."""
        term_id = row.get(col_map.get("id", ""), "")
        name = row.get(col_map.get("name", ""), "")
        
        if not term_id or not name:
            return None
        
        definition = row.get(col_map.get("definition", ""), "")
        
        # Parse synonyms (comma-separated or JSON array)
        synonyms_raw = row.get(col_map.get("synonyms", ""), "")
        if synonyms_raw.startswith("["):
            try:
                synonyms = json.loads(synonyms_raw)
            except json.JSONDecodeError:
                synonyms = []
        else:
            synonyms = [s.strip() for s in synonyms_raw.split(",") if s.strip()]
        
        return OntologyTerm(
            id=term_id,
            name=name,
            definition=definition,
            synonyms=synonyms
        )


class JSONAdapter(DatasetAdapter):
    """Parser for JSON format datasets."""
    
    def __init__(self, terms_path: str = "terms", id_field: str = "id", name_field: str = "name"):
        self.terms_path = terms_path
        self.id_field = id_field
        self.name_field = name_field
    
    def can_handle(self, source_type: str) -> bool:
        return source_type.lower() in ["json", "json-ld"]
    
    def parse(self, content: str) -> Dict[str, OntologyTerm]:
        """Parse JSON content."""
        terms = {}
        data = json.loads(content)
        
        # Navigate to terms array
        items = data
        if self.terms_path:
            for key in self.terms_path.split("."):
                if isinstance(items, dict):
                    items = items.get(key, [])
                else:
                    break
        
        if not isinstance(items, list):
            items = [items] if isinstance(items, dict) else []
        
        for item in items:
            term = self._item_to_term(item)
            if term:
                terms[term.id] = term
        
        logger.info(f"Parsed {len(terms)} terms from JSON content")
        return terms
    
    def _item_to_term(self, item: Dict) -> Optional[OntologyTerm]:
        """Convert JSON item to OntologyTerm."""
        term_id = item.get(self.id_field, "")
        name = item.get(self.name_field, "")
        
        if not term_id or not name:
            return None
        
        return OntologyTerm(
            id=term_id,
            name=name,
            definition=item.get("definition", item.get("description", "")),
            synonyms=item.get("synonyms", item.get("aliases", [])),
            xrefs=item.get("xrefs", {}),
            is_a=item.get("is_a", item.get("parents", [])),
        )


class DatasetLoader:
    """
    Main dataset loader supporting multiple formats and sources.
    
    Usage:
        loader = DatasetLoader()
        loader.load_dataset(config)  # Single dataset
        loader.load_all_datasets()   # From config
    """
    
    def __init__(self, cache_dir: Optional[str] = None):
        self.cache_dir = Path(cache_dir or get_config().cache_dir)
        self.cache_dir.mkdir(parents=True, exist_ok=True)
        
        # Register adapters
        self.adapters: List[DatasetAdapter] = [
            OBOAdapter(),
            CSVAdapter(),
            JSONAdapter(),
        ]
        
        # Loaded data
        self.datasets: Dict[str, Dict[str, OntologyTerm]] = {}
    
    def register_adapter(self, adapter: DatasetAdapter):
        """Register a custom adapter."""
        self.adapters.insert(0, adapter)  # Custom adapters take priority
    
    def get_adapter(self, source_type: str) -> Optional[DatasetAdapter]:
        """Get adapter for source type."""
        for adapter in self.adapters:
            if adapter.can_handle(source_type):
                return adapter
        return None
    
    def load_dataset(self, config: DatasetConfig) -> Dict[str, OntologyTerm]:
        """Load a single dataset based on configuration."""
        logger.info(f"Loading dataset: {config.name}")
        
        # Check cache
        if config.cache_enabled:
            cached = self._load_from_cache(config)
            if cached:
                self.datasets[config.name] = cached
                return cached
        
        # Get content
        content = self._get_content(config)
        if not content:
            logger.warning(f"No content for dataset: {config.name}")
            return {}
        
        # Parse with appropriate adapter
        adapter = self.get_adapter(config.source_type)
        if not adapter:
            logger.error(f"No adapter for source type: {config.source_type}")
            return {}
        
        terms = adapter.parse(content)
        
        # Cache results
        if config.cache_enabled:
            self._save_to_cache(config, terms)
        
        self.datasets[config.name] = terms
        return terms
    
    def load_all_datasets(self) -> Dict[str, Dict[str, OntologyTerm]]:
        """Load all datasets from configuration."""
        config = get_config()
        for dataset_config in config.datasets:
            self.load_dataset(dataset_config)
        return self.datasets
    
    def _get_content(self, config: DatasetConfig) -> Optional[str]:
        """Get content from URL or file path."""
        # Try URL first
        if config.source_url:
            try:
                logger.info(f"Downloading from: {config.source_url}")
                req = urllib.request.Request(
                    config.source_url,
                    headers={'User-Agent': 'HITL-KG/1.0'}
                )
                with urllib.request.urlopen(req, timeout=60) as response:
                    return response.read().decode('utf-8')
            except Exception as e:
                logger.warning(f"Download failed: {e}")
        
        # Try local file
        if config.source_path:
            path = Path(config.source_path)
            if path.exists():
                return path.read_text(encoding='utf-8')
        
        return None
    
    def _cache_path(self, config: DatasetConfig) -> Path:
        """Get cache file path for a dataset."""
        return self.cache_dir / f"{config.name}_cache.json"
    
    def _load_from_cache(self, config: DatasetConfig) -> Optional[Dict[str, OntologyTerm]]:
        """Load dataset from cache if valid."""
        cache_path = self._cache_path(config)
        
        if not cache_path.exists():
            return None
        
        # Check age
        mtime = datetime.fromtimestamp(cache_path.stat().st_mtime)
        age_days = (datetime.now() - mtime).days
        if age_days > config.cache_max_age_days:
            return None
        
        try:
            with open(cache_path) as f:
                data = json.load(f)
            
            terms = {}
            for term_id, term_data in data.get("terms", {}).items():
                terms[term_id] = OntologyTerm(
                    id=term_data["id"],
                    name=term_data["name"],
                    definition=term_data.get("definition", ""),
                    synonyms=term_data.get("synonyms", []),
                    xrefs=term_data.get("xrefs", {}),
                    is_a=term_data.get("is_a", []),
                    relationships=term_data.get("relationships", []),
                    namespace=term_data.get("namespace", ""),
                )
            
            logger.info(f"Loaded {len(terms)} terms from cache: {config.name}")
            return terms
            
        except Exception as e:
            logger.warning(f"Cache load failed: {e}")
            return None
    
    def _save_to_cache(self, config: DatasetConfig, terms: Dict[str, OntologyTerm]):
        """Save dataset to cache."""
        try:
            cache_path = self._cache_path(config)
            
            data = {
                "name": config.name,
                "source_type": config.source_type,
                "timestamp": datetime.now().isoformat(),
                "terms": {
                    tid: {
                        "id": t.id,
                        "name": t.name,
                        "definition": t.definition,
                        "synonyms": t.synonyms,
                        "xrefs": t.xrefs,
                        "is_a": t.is_a,
                        "relationships": t.relationships,
                        "namespace": t.namespace,
                    }
                    for tid, t in terms.items()
                }
            }
            
            with open(cache_path, 'w') as f:
                json.dump(data, f)
            
            logger.info(f"Cached {len(terms)} terms for: {config.name}")
            
        except Exception as e:
            logger.warning(f"Cache save failed: {e}")


def build_knowledge_graph(loader: DatasetLoader) -> KnowledgeGraph:
    """
    Build a KnowledgeGraph from loaded datasets.
    
    This function:
    1. Converts OntologyTerms to Entities
    2. Creates relationships between entities
    3. Indexes entities for semantic search
    """
    kg = KnowledgeGraph()
    config = get_config()
    
    # Map dataset names to categories
    category_map = {
        ds.name: EntityCategory(ds.entity_category)
        for ds in config.datasets
        if ds.entity_category in [c.value for c in EntityCategory]
    }
    
    # Add entities from each dataset
    for dataset_name, terms in loader.datasets.items():
        category = category_map.get(dataset_name, EntityCategory.FINDING)
        
        for term_id, term in terms.items():
            entity = term.to_entity(category)
            kg.add_entity(entity)
    
    # Build relationships based on ontology structure
    _build_relationships(kg, loader)
    
    logger.info(f"Built KG with {len(kg.entities)} entities")
    return kg


def _build_relationships(kg: KnowledgeGraph, loader: DatasetLoader):
    """Build relationships between entities."""
    
    # Disease-symptom associations (curated mappings)
    disease_symptom_mappings = _get_disease_symptom_mappings()
    
    for disease_id, symptom_mappings in disease_symptom_mappings.items():
        if disease_id not in kg.entities:
            continue
        
        for symptom_name, confidence in symptom_mappings:
            # Find symptom entity by name
            symptom_entity = None
            for entity in kg.entities.values():
                if entity.category == EntityCategory.SYMPTOM:
                    if (entity.name.lower() == symptom_name.lower() or
                        symptom_name.lower() in [s.lower() for s in entity.synonyms]):
                        symptom_entity = entity
                        break
            
            if symptom_entity:
                kg.add_relation(disease_id, symptom_entity.id, "causes", confidence)
    
    # Add treatment relations
    _add_treatment_entities(kg)


def _get_disease_symptom_mappings() -> Dict[str, List[Tuple[str, float]]]:
    """
    Get curated disease-symptom mappings.
    
    These are based on medical literature and provide high-quality
    associations that may not be present in the raw ontologies.
    """
    return {
        "DOID:8469": [  # Influenza
            ("fever", 0.95), ("cough", 0.85), ("fatigue", 0.90),
            ("body aches", 0.85), ("headache", 0.80), ("chills", 0.75),
        ],
        "DOID:0080600": [  # COVID-19
            ("fever", 0.80), ("cough", 0.85), ("fatigue", 0.90),
            ("shortness of breath", 0.70), ("headache", 0.60),
            ("loss of taste", 0.50), ("loss of smell", 0.50),
        ],
        "DOID:10459": [  # Common cold
            ("runny nose", 0.95), ("sore throat", 0.80), ("cough", 0.75),
            ("nasal congestion", 0.85), ("sneezing", 0.80),
        ],
        "DOID:552": [  # Pneumonia
            ("fever", 0.90), ("cough", 0.95), ("shortness of breath", 0.85),
            ("chest pain", 0.70), ("fatigue", 0.80),
        ],
        "DOID:6132": [  # Bronchitis
            ("cough", 0.95), ("fatigue", 0.60),
            ("shortness of breath", 0.50),
        ],
        "DOID:10534": [  # Strep throat
            ("sore throat", 0.98), ("fever", 0.80), ("headache", 0.50),
        ],
        "DOID:13084": [  # Sinusitis
            ("headache", 0.85), ("nasal congestion", 0.90),
            ("runny nose", 0.80),
        ],
        "DOID:8893": [  # Migraine
            ("headache", 0.99), ("nausea", 0.70),
        ],
    }


def _add_treatment_entities(kg: KnowledgeGraph):
    """Add treatment entities and relationships."""
    treatments = [
        Entity("tx_rest", "Rest", EntityCategory.TREATMENT,
               "Physical and mental rest", ["bed rest"]),
        Entity("tx_fluids", "Fluid Intake", EntityCategory.TREATMENT,
               "Increased hydration", ["hydration"]),
        Entity("tx_acetaminophen", "Acetaminophen", EntityCategory.MEDICATION,
               "Pain and fever reducer", ["paracetamol", "Tylenol"]),
        Entity("tx_ibuprofen", "Ibuprofen", EntityCategory.MEDICATION,
               "NSAID for pain and inflammation", ["Advil", "Motrin"]),
        Entity("tx_antiviral", "Antiviral Medication", EntityCategory.MEDICATION,
               "Medications for viral infections", ["oseltamivir", "Tamiflu"]),
        Entity("tx_decongestant", "Decongestants", EntityCategory.MEDICATION,
               "Nasal congestion relief", ["pseudoephedrine"]),
    ]
    
    for tx in treatments:
        kg.add_entity(tx)
    
    # Treatment relationships
    treatment_map = {
        "DOID:8469": ["tx_rest", "tx_fluids", "tx_acetaminophen", "tx_antiviral"],
        "DOID:0080600": ["tx_rest", "tx_fluids", "tx_acetaminophen"],
        "DOID:10459": ["tx_rest", "tx_fluids", "tx_decongestant"],
        "DOID:552": ["tx_rest"],
    }
    
    for disease_id, treatment_ids in treatment_map.items():
        if disease_id in kg.entities:
            for tx_id in treatment_ids:
                if tx_id in kg.entities:
                    kg.add_relation(tx_id, disease_id, "treats", 0.8)


def load_knowledge_graph(use_embeddings: bool = True) -> KnowledgeGraph:
    """
    Main entry point: Load datasets and build knowledge graph.
    
    Args:
        use_embeddings: If True, also index entities for semantic search
    """
    loader = DatasetLoader()
    loader.load_all_datasets()
    
    kg = build_knowledge_graph(loader)
    
    if use_embeddings:
        try:
            from .embedding_service import get_embedding_service
            embedding_service = get_embedding_service()
            embedding_service.index_entities(kg.get_entity_dict_for_embedding())
        except Exception as e:
            logger.warning(f"Failed to initialize embeddings: {e}")
    
    return kg