#!/usr/bin/env python3

"""
CASL Voice Bot - Speech Pathology Assistant
Using LiveKit agents with OpenAI's real-time capabilities
"""

import os
import asyncio
import gradio as gr
import logging
import sys
import time
from dotenv import load_dotenv
from livekit import agents
from openai import AsyncOpenAI

# Add parent directory to path to import common utilities
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
from implementations.common.casl_utils import CASLAssessment, save_session_data, CASL_PROMPT

# Load environment variables
load_dotenv()

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Initialize OpenAI client
openai_client = AsyncOpenAI(api_key=os.getenv("OPENAI_API_KEY"))

class GradioInputDevice(agents.InputDevice):
    """Custom input device that works with Gradio"""
    
    def __init__(self):
        super().__init__()
        self.audio_queue = asyncio.Queue()
        self.is_active = True
    
    async def receive(self) -> agents.AudioChunk:
        """Receive audio data from the queue"""
        try:
            audio_data = await asyncio.wait_for(self.audio_queue.get(), timeout=0.1)
            return audio_data
        except asyncio.TimeoutError:
            return None
    
    async def add_audio(self, audio_data):
        """Add audio data to the queue"""
        if audio_data is None:
            return
            
        # Convert gradio audio format to AudioChunk
        sample_rate, audio_array = audio_data
        audio_chunk = agents.AudioChunk(
            samples=audio_array,
            sample_rate=sample_rate,
            is_last=False
        )
        await self.audio_queue.put(audio_chunk)
    
    def stop(self):
        """Stop the input device"""
        self.is_active = False


class GradioOutputDevice(agents.OutputDevice):
    """Custom output device that works with Gradio"""
    
    def __init__(self):
        super().__init__()
        self.output_queue = asyncio.Queue()
    
    async def transmit(self, audio_chunk: agents.AudioChunk) -> None:
        """Transmit audio chunk to the queue"""
        if audio_chunk is not None:
            await self.output_queue.put((audio_chunk.samples, audio_chunk.sample_rate))
    
    async def get_latest_audio(self):
        """Get the latest audio from the queue"""
        try:
            return await asyncio.wait_for(self.output_queue.get(), timeout=0.1)
        except asyncio.TimeoutError:
            return None


class SpeechPathologistAssistant:
    """Speech pathologist assistant using LiveKit agents"""
    
    def __init__(self):
        self.input_device = GradioInputDevice()
        self.output_device = GradioOutputDevice()
        self.assistant = None
        self.assistant_task = None
        self.transcript = []
        self.is_running = False
        self.assessment = CASLAssessment()
    
    async def initialize_assistant(self, voice="shimmer"):
        """Initialize the speech assistant"""
        self.assistant = agents.VoiceAssistant(
            openai_client=openai_client,
            model="gpt-4o",
            voice=voice,
            input_device=self.input_device,
            output_device=self.output_device,
            initial_message=CASL_PROMPT,
            real_time=True,  # Enable real-time processing
        )
        
        # Add transcript and response callbacks
        self.assistant.on_transcript = self.on_transcript
        self.assistant.on_response = self.on_response
    
    def on_transcript(self, transcript):
        """Handle transcript from user"""
        self.transcript.append(f"Student: {transcript.text}")
        
        # Basic analysis of speech for CASL-2 categories
        self.assessment.analyze_speech(transcript.text)
        
        return True
    
    def on_response(self, response):
        """Handle response from assistant"""
        self.transcript.append(f"Speech Pathologist: {response.text}")
        return True
    
    async def start_assistant(self, voice_model, student_id):
        """Start the assistant in a background task"""
        await self.initialize_assistant(voice_model)
        
        self.is_running = True
        
        # Add student info to transcript
        student_info = f" for {student_id}" if student_id else ""
        self.transcript.append(f"Session started{student_info}. The AI Speech Pathologist will speak first.")
        
        # Run the assistant in a background task
        self.assistant_task = asyncio.create_task(self.assistant.run())
        
        return "Session active. The AI will introduce itself."
    
    def stop_assistant(self):
        """Stop the assistant"""
        if self.assistant_task and not self.assistant_task.done():
            self.assistant_task.cancel()
        
        self.input_device.stop()
        self.is_running = False
        
        # Add ending to transcript
        self.transcript.append("Session ended.")
        return "Session stopped."
    
    async def process_audio(self, audio):
        """Process audio from Gradio interface"""
        if not self.is_running or audio is None:
            return None, self.get_transcript(), self.assessment.get_assessment_html()
        
        # Add audio to input device
        await self.input_device.add_audio(audio)
        
        # Check for assistant output
        output_audio = await self.output_device.get_latest_audio()
        
        return output_audio, self.get_transcript(), self.assessment.get_assessment_html()
    
    def get_transcript(self):
        """Get the current transcript"""
        return "\n".join(self.transcript)
    
    def add_note(self, note):
        """Add a custom note"""
        result = self.assessment.add_note(note)
        return "", result, self.assessment.get_assessment_html()
    
    def save_session(self, student_id):
        """Save session to file"""
        return save_session_data(self.transcript, self.assessment, student_id)


# Create the speech pathology assistant
speech_assistant = SpeechPathologistAssistant()


async def start_session(voice_model, student_id):
    """Start the speech pathology session"""
    status = await speech_assistant.start_assistant(voice_model, student_id)
    return status, None, speech_assistant.get_transcript(), speech_assistant.assessment.get_assessment_html()


def stop_session():
    """Stop the speech pathology session"""
    return speech_assistant.stop_assistant(), None, speech_assistant.get_transcript(), speech_assistant.assessment.get_assessment_html()


async def process_mic_input(audio, progress=gr.Progress()):
    """Process microphone input"""
    progress(0, desc="Processing speech...")
    audio_output, transcript, assessment = await speech_assistant.process_audio(audio)
    progress(1, desc="Done")
    return audio_output, transcript, assessment


def add_note(note):
    """Add a note to the session"""
    return speech_assistant.add_note(note)


def save_session(student_id):
    """Save the current session"""
    return speech_assistant.save_session(student_id)


# Create Gradio Interface
with gr.Blocks(title="CASL-2 Speech Pathology Assistant") as app:
    gr.Markdown("# CASL-2 Speech Pathology Assistant")
    gr.Markdown("### AI-powered speech therapy assessment based on the CASL-2 framework")
    
    with gr.Row():
        with gr.Column(scale=1):
            student_id = gr.Textbox(label="Student ID (optional)", placeholder="Enter student ID")
            voice_select = gr.Dropdown(
                ["alloy", "echo", "fable", "onyx", "nova", "shimmer"], 
                value="shimmer", 
                label="Assistant Voice"
            )
            start_button = gr.Button("Start Session", variant="primary")
            stop_button = gr.Button("Stop Session", variant="stop")
            status = gr.Textbox(label="Status", value="Ready to start")
            
            with gr.Accordion("SLP Tools", open=True):
                note_input = gr.Textbox(
                    label="Add Assessment Note", 
                    placeholder="Enter observation or assessment note here..."
                )
                note_button = gr.Button("Add Note")
                note_status = gr.Textbox(label="Note Status")
                save_button = gr.Button("Save Session")
                save_status = gr.Textbox(label="Save Status")
        
        with gr.Column(scale=2):
            audio_output = gr.Audio(label="AI Speech", autoplay=True)
            audio_input = gr.Audio(
                label="Speak to the AI",
                type="microphone",
                source="microphone", 
                streaming=True
            )
    
    with gr.Row():
        with gr.Column(scale=1):
            assessment_html = gr.HTML(label="Assessment Progress")
        with gr.Column(scale=1):
            transcript = gr.Textbox(label="Transcript", lines=10)
    
    with gr.Accordion("About This Application", open=False):
        gr.Markdown("""
        ### About CASL-2 Speech Pathology Assistant
        
        This application provides an AI speech pathologist that can assess students using the CASL-2 framework. It focuses on:
        
        - **Lexical/Semantic Skills**: Vocabulary knowledge and word usage
        - **Syntactic Skills**: Grammar and sentence structure
        - **Supralinguistic Skills**: Higher-level language beyond literal meanings
        - **Pragmatic Skills**: Social use of language (less emphasis for younger students)
        
        The AI will provide structured assessments and exercises to help evaluate speech patterns.
        
        ### How to Use
        
        1. Optionally enter a Student ID to track sessions
        2. Select the AI voice you prefer
        3. Click "Start Session" to begin
        4. The AI will introduce itself and begin the assessment
        5. Speak into your microphone when it's your turn
        6. View the transcript to track the conversation
        7. SLPs can add notes throughout the session
        8. Save the session when finished
        9. Click "Stop Session" when done
        
        ### For Speech-Language Pathologists
        
        This tool is designed to supplement, not replace, professional SLP services. SLPs can:
        
        - Add custom notes during the session
        - Save session data for later reference
        - Track progress across multiple sessions
        - Use the AI as a consistent assessment tool
        """)
    
    # Setup event handlers
    start_button.click(
        fn=lambda voice, student: asyncio.run(start_session(voice, student)), 
        inputs=[voice_select, student_id],
        outputs=[status, audio_output, transcript, assessment_html]
    )
    stop_button.click(
        fn=stop_session, 
        outputs=[status, audio_output, transcript, assessment_html]
    )
    note_button.click(
        fn=add_note,
        inputs=note_input,
        outputs=[note_input, note_status, assessment_html]
    )
    save_button.click(
        fn=save_session,
        inputs=student_id,
        outputs=save_status
    )
    
    # Setup audio processing
    audio_input.stream(
        fn=lambda audio: asyncio.run(process_mic_input(audio)),
        inputs=audio_input,
        outputs=[audio_output, transcript, assessment_html]
    )


def main(share=True):
    """Main function to launch the app"""
    app.launch(share=share)


# Entry point for the application
if __name__ == "__main__":
    main()