import os
import time
import subprocess
import whisper
from cog import BasePredictor, Input, Path
import torch
import tempfile

class Predictor(BasePredictor):
    def setup(self):
        """Load the model into memory to make inference faster"""
        print("Loading models...")
        
        # Load whisper for audio transcription
        print("Loading Whisper model...")
        self.whisper_model = whisper.load_model("large-v3", download_root="models/speech_encoder/")
        
        # In a real implementation, this would load the LLaMA-Omni model
        print("Note: In a real deployment, the LLaMA-Omni model would be loaded here")
        
        # Start the controller
        print("Starting controller...")
        self.controller_process = subprocess.Popen([
            "python", "-m", "omni_speech.serve.controller",
            "--host", "0.0.0.0",
            "--port", "10000"
        ])
        time.sleep(5)  # Wait for controller to start
        
        # Start model worker
        print("Starting model worker...")
        self.model_worker_process = subprocess.Popen([
            "python", "-m", "omni_speech.serve.model_worker",
            "--host", "0.0.0.0",
            "--controller", "http://localhost:10000",
            "--port", "40000",
            "--worker", "http://localhost:40000",
            "--model-path", "Llama-3.1-8B-Omni",
            "--model-name", "Llama-3.1-8B-Omni",
            "--s2s"
        ])
        time.sleep(10)  # Wait for model worker to start
        
        print("Setup complete")
    
    def predict(
        self,
        audio: Path = Input(description="Audio file for speech input", default=None),
        text: str = Input(description="Text input (used if no audio is provided)", default=None),
    ) -> str:
        """Run inference on the model"""
        if audio is None and not text:
            return "Error: Please provide either an audio file or text input."
        
        if audio is not None:
            # Process audio input
            print(f"Transcribing audio from {audio}...")
            
            # Transcribe audio using Whisper
            result = self.whisper_model.transcribe(str(audio))
            transcription = result["text"]
            
            print(f"Transcription: {transcription}")
            
            # In a real implementation, this would process the transcription through LLaMA-Omni
            # For this placeholder, we'll just return the transcription with a simulated response
            response = f"Transcription: {transcription}\n\nResponse: This is a simulated response to your audio. In a real deployment, this would be processed through the LLaMA-Omni model."
            
            return response
        else:
            # Process text input
            print(f"Processing text: {text}")
            
            # In a real implementation, this would process the text through LLaMA-Omni
            # For this placeholder, we'll just return the text with a simulated response
            response = f"Input: {text}\n\nResponse: This is a simulated response to your text. In a real deployment, this would be processed through the LLaMA-Omni model."
            
            return response
    
    def __del__(self):
        """Clean up processes on shutdown"""
        if hasattr(self, 'controller_process'):
            self.controller_process.terminate()
        
        if hasattr(self, 'model_worker_process'):
            self.model_worker_process.terminate()