import os
import uuid
import time
import logging  # Import the logging module
import torch
from django.http import FileResponse
from rest_framework import status
from rest_framework.response import Response
from rest_framework.generics import CreateAPIView
from TTS.api import TTS
from rest_framework.authentication import TokenAuthentication
from rest_framework.permissions import IsAuthenticated
from texttovoice.models import TextToSpeech
from .serializers import TextToSpeechSerializer
from rest_framework.parsers import MultiPartParser
from drf_yasg import openapi
from drf_yasg.utils import swagger_auto_schema


# Initialize logger at module level
logger = logging.getLogger(__name__)

class TextToSpeechCreateView(CreateAPIView):
    serializer_class = TextToSpeechSerializer
    authentication_classes = [TokenAuthentication]  # Apply token authentication
    permission_classes = [IsAuthenticated]  # Require authentication for this view

    parser_classes = [MultiPartParser]

    @swagger_auto_schema(
            operation_id='Create a document',
            operation_description='Create a document by providing file and s3_key',
            manual_parameters=[
                openapi.Parameter('file', openapi.IN_FORM, type=openapi.TYPE_FILE, description='Document to be uploaded'),
                openapi.Parameter('s3_key', openapi.IN_FORM, type=openapi.TYPE_STRING, description='S3 Key of the Document '
                                                                                                   '(folders along with name)')
            ],
            responses={
                status.HTTP_200_OK: openapi.Response(
                    'Success', schema=openapi.Schema(type=openapi.TYPE_OBJECT, properties={
                        'doc_id': openapi.Schema(type=openapi.TYPE_STRING, description='Document ID'),
                        'mime_type': openapi.Schema(type=openapi.TYPE_STRING, description='Mime Type of the Document'),
                        'version_id': openapi.Schema(type=openapi.TYPE_STRING, description='S3 version ID of the document')
                    })
                )
            }
        )

    def create(self, request, *args, **kwargs):
        serializer = self.get_serializer(data=request.data)
        if serializer.is_valid():
            gpu_available = torch.cuda.is_available()
            text = serializer.validated_data.get("text")
            speaker_wav = serializer.validated_data.get("speaker_wav")
            language = serializer.validated_data.get("language")
            output_filename = f"output_{uuid.uuid4()}.wav"

            # Log the start time
            start_time = time.time()
            # Save the uploaded speaker file to a temporary location
            speaker_file_path = os.path.join('/tmp', speaker_wav.name)
            with open(speaker_file_path, "wb") as destination:
                for chunk in speaker_wav.chunks():
                    destination.write(chunk)
            
            # Generate speech using tts.tts_to_file
            tts = TTS("tts_models/multilingual/multi-dataset/xtts_v2", gpu=gpu_available)
            tts.tts_to_file(text=text, file_path=output_filename, speaker_wav=speaker_file_path, language=language)

            # Log the end time
            end_time = time.time()

            # Calculate the processing time
            processing_time = end_time - start_time

            # Define a function to delete the output file
            def file_iterator(file_name):
                with open(file_name, 'rb') as f:
                    yield from f

                # Delete the file after sending it
                try:
                    os.remove(file_name)
                except Exception as e:
                    # You might want to log this error
                    pass

            # Use the file_iterator to create a FileResponse
            
            TextToSpeech.objects.create(
            text=text,
            speaker_wav=speaker_wav,
            output_wav=output_filename,
            language=language,
            created_by=request.user  # Assign the authenticated user here
            )
            response = FileResponse(file_iterator(output_filename), as_attachment=True, content_type='audio/wav')

            # Log the processing time using the logger
            logger.info(f"start time: {start_time} , end time: {end_time} and Processing time: {processing_time} seconds")
            
            return response

            # except Exception as e:
            #     return Response({"error": str(e)}, status=status.HTTP_500_INTERNAL_SERVER_ERROR)
        return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)