from io import BytesIO

from langchain_openai import ChatOpenAI
from openai import OpenAI
import tiktoken
import os
from dotenv import load_dotenv
import os
from langchain.text_splitter import MarkdownHeaderTextSplitter

# Load environment variables from .env file
load_dotenv()

# IBM Connection Parameters (using loaded env variables)
openai_api_key = os.getenv("OPENAI_API_KEY")

def n_of_questions():
    n_of_questions = 5
    return n_of_questions

#openai_api_key = os.environ.get("openai_api_key")

model = "gpt-3.5-turbo-1106"

def load_model(openai_api_key):
    return ChatOpenAI(
        model_name=model,
        openai_api_key=openai_api_key,
        temperature=0.5
    )

# Initialize the OpenAI client with the API key
client = OpenAI(api_key=openai_api_key)


def convert_text_to_speech(text, output, voice):
    try:
        # Convert the final text to speech
        response = client.audio.speech.create(model="tts-1-hd", voice=voice, input=text)

        if isinstance(output, BytesIO):
            # If output is a BytesIO object, write directly to it
            for chunk in response.iter_bytes():
                output.write(chunk)
        else:
            # If output is a file path, open and write to it
            with open(output, 'wb') as f:
                for chunk in response.iter_bytes():
                    f.write(chunk)

    except Exception as e:
        print(f"An error occurred: {e}")
        # Fallback in case of error
        response = client.audio.speech.create(model="tts-1-hd", voice=voice, input='Here is my Report.')

        if isinstance(output, BytesIO):
            for chunk in response.iter_bytes():
                output.write(chunk)
        else:
            with open(output, 'wb') as f:
                for chunk in response.iter_bytes():
                    f.write(chunk)


def transcribe_audio(audio):
    try:
        audio_file = open(audio, "rb")
        transcription = client.audio.transcriptions.create(
            model="whisper-1",
            file=audio_file
        )
        return transcription.text
    except Exception as e:
        return "Audio transcription failed. Please try again."
    
    
def split_text_with_langchain(text, headers_to_split_on):
    markdown_splitter = MarkdownHeaderTextSplitter(headers_to_split_on=headers_to_split_on)
    docs = markdown_splitter.create_documents([text])
    return docs