"""
Database models for voiceprint tracking.
"""
from sqlalchemy import create_engine, Column, String, Float, Integer, DateTime, Boolean, ForeignKey, LargeBinary
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker, relationship
from datetime import datetime
import os

Base = declarative_base()

class Voiceprint(Base):
    """Unique voice identity."""
    __tablename__ = 'voiceprints'

    id = Column(String(20), primary_key=True)  # vp_xxxxxxxx
    embedding = Column(LargeBinary, nullable=False)  # 192-dim vector as bytes
    first_seen = Column(DateTime, default=datetime.utcnow)
    times_seen = Column(Integer, default=1)
    total_audio_seconds = Column(Float, default=0.0)
    is_flagged = Column(Boolean, default=False)
    flag_reason = Column(String(200), nullable=True)

    # User-editable fields
    label = Column(String(100), nullable=True)  # Human-friendly name (e.g., "Juan Pérez")
    notes = Column(String(1000), nullable=True)  # User comments/notes

    # Relationships
    appearances = relationship("VoiceprintAppearance", back_populates="voiceprint")


class VoiceprintAppearance(Base):
    """Track where a voiceprint appears."""
    __tablename__ = 'voiceprint_appearances'
    
    id = Column(Integer, primary_key=True, autoincrement=True)
    voiceprint_id = Column(String(20), ForeignKey('voiceprints.id'), nullable=False)
    test_id = Column(String(50), nullable=False)
    test_filename = Column(String(200), nullable=False)
    role = Column(String(20), nullable=False)  # 'main' or 'additional'
    duration_seconds = Column(Float, nullable=False)
    detected_at = Column(DateTime, default=datetime.utcnow)
    clip_path = Column(String(500), nullable=True)  # Path to extracted audio clip
    
    # Relationships
    voiceprint = relationship("Voiceprint", back_populates="appearances")


class TestAnalysis(Base):
    """Store analysis results per test."""
    __tablename__ = 'test_analyses'
    
    id = Column(Integer, primary_key=True, autoincrement=True)
    test_id = Column(String(50), unique=True, nullable=False)
    filename = Column(String(200), nullable=False)
    duration_seconds = Column(Float, nullable=False)
    analyzed_at = Column(DateTime, default=datetime.utcnow)
    
    # Main speaker
    main_voiceprint_id = Column(String(20), ForeignKey('voiceprints.id'), nullable=True)
    main_speech_seconds = Column(Float, default=0.0)
    main_quality = Column(String(20), nullable=True)
    
    # Detection counts
    additional_speakers_count = Column(Integer, default=0)
    background_anomalies_count = Column(Integer, default=0)
    wake_words_count = Column(Integer, default=0)
    
    # Synthetic detection
    synthetic_score = Column(Float, default=0.0)
    is_synthetic = Column(Boolean, default=False)
    
    # JSON results (full analysis)
    results_json = Column(String, nullable=True)


class Database:
    """Database manager."""
    
    def __init__(self, db_path: str = None):
        if db_path is None:
            data_dir = os.environ.get("DATA_DIR", "data")
            db_path = os.path.join(data_dir, "db", "voiceprints.db")
        self.db_path = db_path
        os.makedirs(os.path.dirname(db_path), exist_ok=True)
        self.engine = create_engine(f'sqlite:///{db_path}')
        Base.metadata.create_all(self.engine)
        self.Session = sessionmaker(bind=self.engine)
    
    def get_session(self):
        return self.Session()
    
    def add_voiceprint(self, vp_id: str, embedding: bytes, 
                       test_id: str, filename: str, role: str, 
                       duration: float, clip_path: str = None):
        """Add or update voiceprint and record appearance."""
        session = self.get_session()
        try:
            # Check if voiceprint exists
            vp = session.query(Voiceprint).filter_by(id=vp_id).first()
            
            if vp:
                # Update existing
                vp.times_seen += 1
                vp.total_audio_seconds += duration
                
                # Check for flag conditions
                if vp.times_seen >= 4:
                    vp.is_flagged = True
                    vp.flag_reason = f"Seen in {vp.times_seen} tests"
            else:
                # Create new
                vp = Voiceprint(
                    id=vp_id,
                    embedding=embedding,
                    total_audio_seconds=duration
                )
                session.add(vp)
            
            # Record appearance
            appearance = VoiceprintAppearance(
                voiceprint_id=vp_id,
                test_id=test_id,
                test_filename=filename,
                role=role,
                duration_seconds=duration,
                clip_path=clip_path
            )
            session.add(appearance)
            
            session.commit()
            return vp
        except Exception as e:
            session.rollback()
            raise e
        finally:
            session.close()
    
    def get_voiceprint(self, vp_id: str):
        """Get voiceprint by ID."""
        session = self.get_session()
        try:
            return session.query(Voiceprint).filter_by(id=vp_id).first()
        finally:
            session.close()
    
    def get_all_voiceprints(self):
        """Get all voiceprints."""
        session = self.get_session()
        try:
            return session.query(Voiceprint).order_by(Voiceprint.times_seen.desc()).all()
        finally:
            session.close()
    
    def get_flagged_voiceprints(self):
        """Get flagged voiceprints."""
        session = self.get_session()
        try:
            return session.query(Voiceprint).filter_by(is_flagged=True).all()
        finally:
            session.close()
    
    def get_multi_appearance_voiceprints(self, min_appearances: int = 2):
        """Get voiceprints seen in multiple tests."""
        session = self.get_session()
        try:
            return session.query(Voiceprint).filter(
                Voiceprint.times_seen >= min_appearances
            ).order_by(Voiceprint.times_seen.desc()).all()
        finally:
            session.close()
    
    def get_voiceprint_appearances(self, vp_id: str):
        """Get all appearances of a voiceprint."""
        session = self.get_session()
        try:
            return session.query(VoiceprintAppearance).filter_by(
                voiceprint_id=vp_id
            ).order_by(VoiceprintAppearance.detected_at.desc()).all()
        finally:
            session.close()
    
    def find_matching_voiceprint(self, embedding: bytes, threshold: float = 0.80):
        """Find existing voiceprint matching the embedding."""
        import numpy as np

        session = self.get_session()
        try:
            new_emb = np.frombuffer(bytes(embedding), dtype=np.float32)

            for vp in session.query(Voiceprint).all():
                stored_emb = np.frombuffer(bytes(vp.embedding), dtype=np.float32)
                
                # Cosine similarity
                similarity = np.dot(new_emb, stored_emb) / (
                    np.linalg.norm(new_emb) * np.linalg.norm(stored_emb)
                )
                
                if similarity >= threshold:
                    return vp, similarity
            
            return None, 0.0
        finally:
            session.close()
    
    def save_test_analysis(self, test_id: str, filename: str, 
                          duration: float, results: dict):
        """Save full test analysis."""
        import json
        
        session = self.get_session()
        try:
            analysis = TestAnalysis(
                test_id=test_id,
                filename=filename,
                duration_seconds=duration,
                main_voiceprint_id=results.get('main_voiceprint_id'),
                main_speech_seconds=results.get('main_speech_seconds', 0),
                main_quality=results.get('main_quality'),
                additional_speakers_count=len(results.get('additional_speakers', [])),
                background_anomalies_count=len(results.get('background_anomalies', [])),
                wake_words_count=len(results.get('wake_words', [])),
                synthetic_score=results.get('synthetic_score', 0),
                is_synthetic=results.get('is_synthetic', False),
                results_json=json.dumps(results)
            )
            session.add(analysis)
            session.commit()
            return analysis
        except Exception as e:
            session.rollback()
            raise e
        finally:
            session.close()
    
    def get_stats(self):
        """Get database statistics."""
        session = self.get_session()
        try:
            return {
                'total_tests': session.query(TestAnalysis).count(),
                'total_voiceprints': session.query(Voiceprint).count(),
                'flagged_voiceprints': session.query(Voiceprint).filter_by(is_flagged=True).count(),
                'multi_appearance': session.query(Voiceprint).filter(Voiceprint.times_seen >= 2).count()
            }
        finally:
            session.close()

    def get_analyzer_dashboard_stats(self):
        """Get extended stats for the Analyzer tab dashboard."""
        import json as _json
        from datetime import datetime as _dt

        session = self.get_session()
        try:
            total_tests = session.query(TestAnalysis).count()
            total_vp = session.query(Voiceprint).count()

            high_risk = 0
            for a in session.query(TestAnalysis).all():
                if a.results_json:
                    try:
                        r = _json.loads(a.results_json)
                        if r.get('risk_score', 0) > 60:
                            high_risk += 1
                    except Exception:
                        pass
            fraud_rate = (high_risk / total_tests * 100) if total_tests > 0 else 0.0

            today_start = _dt.utcnow().replace(hour=0, minute=0, second=0, microsecond=0)
            today_analyses = session.query(TestAnalysis).filter(
                TestAnalysis.analyzed_at >= today_start
            ).all()
            alerts_today = 0
            for a in today_analyses:
                if a.results_json:
                    try:
                        r = _json.loads(a.results_json)
                        if r.get('risk_score', 0) > 30:
                            alerts_today += 1
                    except Exception:
                        pass

            return {
                'total_tests': total_tests,
                'fraud_rate': round(fraud_rate, 1),
                'unique_voices': total_vp,
                'alerts_today': alerts_today,
            }
        finally:
            session.close()

    def get_all_tests(self):
        """Get all test analyses ordered by date descending."""
        session = self.get_session()
        try:
            return session.query(TestAnalysis).order_by(
                TestAnalysis.analyzed_at.desc()
            ).all()
        finally:
            session.close()

    def get_test_results(self, test_id: str) -> dict:
        """Get full results JSON for a test."""
        import json as _json
        session = self.get_session()
        try:
            t = session.query(TestAnalysis).filter_by(test_id=test_id).first()
            if t and t.results_json:
                return _json.loads(t.results_json)
            return None
        finally:
            session.close()

    def update_voiceprint_label(self, vp_id: str, label: str):
        """Update voiceprint label/name."""
        session = self.get_session()
        try:
            vp = session.query(Voiceprint).filter_by(id=vp_id).first()
            if vp:
                vp.label = label
                session.commit()
                return True
            return False
        except Exception as e:
            session.rollback()
            raise e
        finally:
            session.close()

    def update_voiceprint_notes(self, vp_id: str, notes: str):
        """Update voiceprint notes/comments."""
        session = self.get_session()
        try:
            vp = session.query(Voiceprint).filter_by(id=vp_id).first()
            if vp:
                vp.notes = notes
                session.commit()
                return True
            return False
        except Exception as e:
            session.rollback()
            raise e
        finally:
            session.close()

    def toggle_voiceprint_flag(self, vp_id: str, flagged: bool, reason: str = None):
        """Manually flag/unflag a voiceprint."""
        session = self.get_session()
        try:
            vp = session.query(Voiceprint).filter_by(id=vp_id).first()
            if vp:
                vp.is_flagged = flagged
                vp.flag_reason = reason if flagged else None
                session.commit()
                return True
            return False
        except Exception as e:
            session.rollback()
            raise e
        finally:
            session.close()

    def get_similarity_threshold(self):
        """Get current similarity threshold (default 0.80)."""
        # Could be stored in a settings table, for now return default
        return 0.80

    def get_appearance_timeline(self, vp_id: str = None):
        """Get appearances over time for timeline chart."""
        session = self.get_session()
        try:
            query = session.query(VoiceprintAppearance)
            if vp_id:
                query = query.filter_by(voiceprint_id=vp_id)
            appearances = query.order_by(VoiceprintAppearance.detected_at).all()

            return [
                {
                    'date': a.detected_at,
                    'voiceprint_id': a.voiceprint_id,
                    'test_id': a.test_id,
                    'role': a.role,
                    'duration': a.duration_seconds
                }
                for a in appearances
            ]
        finally:
            session.close()

    def get_trend_data(self):
        """Get aggregated trend data for charts."""
        import json as _json
        from collections import defaultdict

        session = self.get_session()
        try:
            all_tests = session.query(TestAnalysis).order_by(
                TestAnalysis.analyzed_at
            ).all()

            daily_scores = defaultdict(lambda: {'scores': [], 'count': 0, 'high_risk': 0})
            daily_flags = defaultdict(lambda: {
                'synthetic': 0, 'playback': 0, 'reading': 0,
                'whispers': 0, 'pauses': 0, 'wake_words': 0
            })

            total_risk = 0.0
            high_risk_count = 0

            for t in all_tests:
                day = t.analyzed_at.strftime('%Y-%m-%d') if t.analyzed_at else 'unknown'
                risk = 0
                if t.results_json:
                    try:
                        r = _json.loads(t.results_json)
                        risk = r.get('risk_score', 0)
                        daily_scores[day]['scores'].append(risk)
                        daily_scores[day]['count'] += 1
                        if risk > 60:
                            daily_scores[day]['high_risk'] += 1
                            high_risk_count += 1
                        total_risk += risk

                        if r.get('main_speaker', {}).get('is_synthetic', False):
                            daily_flags[day]['synthetic'] += 1
                        if r.get('playback_detected', False):
                            daily_flags[day]['playback'] += 1
                        if r.get('reading_pattern_detected', False):
                            daily_flags[day]['reading'] += 1
                        if r.get('whisper_detected', False):
                            daily_flags[day]['whispers'] += 1
                        if r.get('suspicious_pauses_detected', False):
                            daily_flags[day]['pauses'] += 1
                        if len(r.get('wake_words', [])) > 0:
                            daily_flags[day]['wake_words'] += 1
                    except Exception:
                        pass

            total = len(all_tests)
            avg_risk = (total_risk / total) if total > 0 else 0
            high_risk_pct = (high_risk_count / total * 100) if total > 0 else 0

            scores_list = []
            for day in sorted(daily_scores.keys()):
                d = daily_scores[day]
                scores_list.append({
                    'date': day,
                    'avg_score': round(sum(d['scores']) / len(d['scores']), 1),
                    'count': d['count'],
                    'high_risk': d['high_risk'],
                })

            flags_list = []
            for day in sorted(daily_flags.keys()):
                entry = {'date': day}
                entry.update(daily_flags[day])
                flags_list.append(entry)

            # Top voices
            all_vps = session.query(Voiceprint).order_by(
                Voiceprint.times_seen.desc()
            ).limit(10).all()
            top_voices = [
                {
                    'id': vp.id,
                    'label': vp.label or vp.id,
                    'times_seen': vp.times_seen,
                    'flagged': vp.is_flagged,
                }
                for vp in all_vps
            ]

            recurring = session.query(Voiceprint).filter(
                Voiceprint.times_seen >= 2
            ).count()

            return {
                'daily_scores': scores_list,
                'daily_flags': flags_list,
                'top_voices': top_voices,
                'summary': {
                    'total': total,
                    'avg_risk': round(avg_risk, 1),
                    'high_risk_pct': round(high_risk_pct, 1),
                    'recurring': recurring,
                },
            }
        finally:
            session.close()