"""
Practice session and analysis models
"""
from sqlalchemy import Column, Integer, String, ForeignKey, DateTime, Float, JSON, Text, Boolean
from sqlalchemy.orm import relationship
from sqlalchemy.sql import func
from config.database import Base

class PracticeSession(Base):
    __tablename__ = "practice_sessions"
    
    id = Column(Integer, primary_key=True)
    student_id = Column(Integer, ForeignKey("students.id"), nullable=False)
    assignment_id = Column(Integer, ForeignKey("assignments.id"), nullable=True)
    
    # Session info
    practice_type = Column(String, nullable=False)  # conversation, reading, articulation
    technique = Column(String, nullable=False)  # normal, prolonged_speech, easy_onset
    duration_seconds = Column(Float)
    
    # Content
    prompt_text = Column(Text)  # What they were supposed to say/read
    transcribed_text = Column(Text)  # What they actually said
    audio_file_path = Column(JSON)  # Array of audio file paths (for multi-turn sessions) or single path
    # Format: ["sessionID_date_studentID_therapistID_turn1.wav", "sessionID_date_studentID_therapistID_turn2.wav", ...]
    # For reading practice: single string or array with one element
    
    # Quick metrics
    total_words = Column(Integer)
    words_per_minute = Column(Float)
    
    # Status
    is_completed = Column(Boolean, default=False)
    completed_at = Column(DateTime(timezone=True))
    created_at = Column(DateTime(timezone=True), server_default=func.now())
    
    # Relationships
    student = relationship("Student", back_populates="sessions")
    assignment = relationship("Assignment", back_populates="sessions")
    analysis = relationship("SessionAnalysis", back_populates="session", uselist=False)
    
    def __repr__(self):
        return f"<PracticeSession {self.id} - {self.practice_type}>"


class SessionAnalysis(Base):
    __tablename__ = "session_analyses"
    
    id = Column(Integer, primary_key=True)
    session_id = Column(Integer, ForeignKey("practice_sessions.id"), unique=True, nullable=False)
    
    # Analysis results (stored as JSON for flexibility)
    stutter_analysis = Column(JSON)  # Detailed stutter types, counts, locations
    fluency_analysis = Column(JSON)  # Prolongation %, consistency scores
    rushed_speech_analysis = Column(JSON)  # Burst patterns, pause analysis
    articulation_analysis = Column(JSON)  # Accuracy scores per sound
    
    # Summary metrics
    total_stutters = Column(Integer, default=0)
    stutter_frequency_percent = Column(Float, default=0.0)
    fluency_score = Column(Float)  # 0-100
    rushed_speech_severity = Column(String)  # minimal, mild, moderate, severe
    articulation_accuracy = Column(Float)  # 0-100
    
    # Affected phonemes/patterns
    struggled_phonemes = Column(JSON)  # List of problem sounds
    stutter_patterns = Column(JSON)  # Common patterns identified
    
    # Feedback
    patient_feedback = Column(Text)  # Generated patient-friendly feedback
    therapist_notes = Column(Text)  # Detailed clinical notes
    
    # Timestamps
    analyzed_at = Column(DateTime(timezone=True), server_default=func.now())
    
    # Relationships
    session = relationship("PracticeSession", back_populates="analysis")
    
    def __repr__(self):
        return f"<SessionAnalysis for session {self.session_id}>"