main.py

from fastapi import FastAPI, HTTPException, UploadFile, File
from fastapi.responses import FileResponse
from fastapi.staticfiles import StaticFiles
from pydantic import BaseModel
from typing import Optional
import azure.cognitiveservices.speech as speechsdk
from openai import OpenAI
import pyaudio
import wave
import tempfile
import os
from dotenv import load_dotenv
from functools import wraps

# Load environment variables
load_dotenv()

# Initialize FastAPI app
app = FastAPI(
    title="Voice Assistant API",
    description="A voice assistant that converts speech to text, processes it, and returns synthesized speech",
    version="1.0.0"
)

# Initialize API clients
openai_client = OpenAI(api_key=os.getenv('OPENAI_API_KEY'))
azure_speech_key = os.getenv('AZURE_SPEECH_KEY')
azure_service_region = os.getenv('AZURE_SPEECH_REGION')


class VoiceAssistantError(Exception):
    """Custom exception for Voice Assistant errors"""
    pass


def handle_errors(func):
    """Decorator for error handling"""
    @wraps(func)
    async def wrapper(*args, **kwargs):
        try:
            return await func(*args, **kwargs)
        except VoiceAssistantError as e:
            raise HTTPException(status_code=400, detail=str(e))
        except Exception as e:
            raise HTTPException(
                status_code=500, detail=f"Internal server error: {str(e)}")
    return wrapper


class VoiceAssistant:
    def __init__(self):
        self.conversation_history = []
        self.temp_dir = tempfile.mkdtemp()
        self.openai_client = OpenAI(api_key=os.getenv('OPENAI_API_KEY'))
        self.speech_config = speechsdk.SpeechConfig(
            subscription=azure_speech_key,
            region=azure_service_region
        )

        # Audio recording config
        self.CHUNK = 1024
        self.FORMAT = pyaudio.paInt16
        self.CHANNELS = 1
        self.RATE = 16000
        self.RECORD_SECONDS = 5

    async def record_audio(self) -> bytes:
        """Record audio from microphone"""
        p = pyaudio.PyAudio()

        try:
            stream = p.open(
                format=self.FORMAT,
                channels=self.CHANNELS,
                rate=self.RATE,
                input=True,
                frames_per_buffer=self.CHUNK
            )

            frames = []
            for _ in range(0, int(self.RATE / self.CHUNK * self.RECORD_SECONDS)):
                data = stream.read(self.CHUNK)
                frames.append(data)

            temp_path = os.path.join(self.temp_dir, "temp_recording.wav")
            wf = wave.open(temp_path, 'wb')
            wf.setnchannels(self.CHANNELS)
            wf.setsampwidth(p.get_sample_size(self.FORMAT))
            wf.setframerate(self.RATE)
            wf.writeframes(b''.join(frames))
            wf.close()

            with open(temp_path, 'rb') as audio_file:
                audio_bytes = audio_file.read()

            return audio_bytes

        finally:
            stream.stop_stream()
            stream.close()
            p.terminate()

    async def transcribe_audio(self, audio_bytes: bytes) -> str:
        """Convert speech to text using OpenAI Whisper"""
        try:
            response = self.openai_client.audio.transcriptions.create(
                model="whisper-1",
                file=("audio.wav", audio_bytes),
            )
            return response.text
        except Exception as e:
            raise VoiceAssistantError(f"Transcription failed: {str(e)}")

    async def get_chat_response(self, text: str) -> str:
        """Get response from ChatGPT"""
        try:
            self.conversation_history.append({"role": "user", "content": text})

            response = self.openai_client.chat.completions.create(
                model="gpt-4",
                messages=self.conversation_history,
                max_tokens=150
            )

            assistant_response = response.choices[0].message.content
            self.conversation_history.append(
                {"role": "assistant", "content": assistant_response})

            return assistant_response
        except Exception as e:
            raise VoiceAssistantError(f"Chat processing failed: {str(e)}")

    async def synthesize_speech(self, text: str) -> str:
        """Convert text to speech using Azure"""
        try:
            output_path = os.path.join(self.temp_dir, "response.wav")
            audio_config = speechsdk.audio.AudioOutputConfig(
                filename=output_path)

            synthesizer = speechsdk.SpeechSynthesizer(
                speech_config=self.speech_config,
                audio_config=audio_config
            )

            result = synthesizer.speak_text_async(text).get()

            if result.reason == speechsdk.ResultReason.SynthesizingAudioCompleted:
                return output_path
            else:
                raise VoiceAssistantError("Speech synthesis failed")

        except Exception as e:
            raise VoiceAssistantError(f"Speech synthesis failed: {str(e)}")

    async def process_voice_input(self, audio_data: bytes = None) -> tuple[str, str]:
        """Process voice input and return response text and audio file path"""
        try:
            if audio_data is None:
                audio_data = await self.record_audio()

            transcript = await self.transcribe_audio(audio_data)
            response_text = await self.get_chat_response(transcript)
            audio_path = await self.synthesize_speech(response_text)

            return response_text, audio_path

        except Exception as e:
            raise VoiceAssistantError(f"Voice processing failed: {str(e)}")

    def cleanup(self):
        """Clean up temporary files"""
        import shutil
        try:
            shutil.rmtree(self.temp_dir)
        except Exception:
            pass


class ChatResponse(BaseModel):
    text: str
    audio_path: str


@app.get("/health")
async def root():
    """Health check endpoint"""
    return {"status": "ok", "message": "Voice Assistant API is running"}


@app.post("/chat", response_model=ChatResponse)
@handle_errors
async def chat_endpoint(audio_file: UploadFile = File(None)):
    """Process voice input and return response"""
    assistant = VoiceAssistant()
    try:
        audio_data = None
        if audio_file:
            audio_data = await audio_file.read()

        response_text, audio_path = await assistant.process_voice_input(audio_data)

        with open(audio_path, 'rb') as f:
            audio_content = f.read()

        assistant.cleanup()

        temp_response_path = tempfile.mktemp(suffix='.wav')
        with open(temp_response_path, 'wb') as f:
            f.write(audio_content)

        return FileResponse(
            path=temp_response_path,
            media_type="audio/wav",
            headers={"text-response": response_text},
            filename="response.wav"
        )
    except Exception as e:
        if assistant:
            assistant.cleanup()
        raise VoiceAssistantError(f"Chat processing failed: {str(e)}")


def get_static_directory(name: str):
    return os.path.join(os.getcwd(), name)


def try_mount_static_html(app, name: str, prefix: str = "/"):
    directory = get_static_directory(name)
    if os.path.exists(directory):
        app.mount(prefix, StaticFiles(
            directory=directory, html=True), name=name)
        print(f"Mounted {name} at {prefix}")
    else:
        print(f"Directory not found: {directory}")


try_mount_static_html(app, "frontend")

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)