# index.py
import os  # Used for setting Python environment
from gtts import gTTS  # Converts text to speech
from flask import Flask, send_file, request  # Python web framework
import openai  # The parent of ChatGPT

os.environ["OPEN_API_KEY"] = "YOUR_API_KEY"  # Set Python environment variable (API key: https://platform.openai.com/account/api-keys)
openai.api_key = os.environ.get("OPEN_API_KEY")  # Configure OpenAI with the API key
app = Flask(__name__)  # Initialize Flask app

@app.route('/tts')  # Similar to route in any framework
def comtor():
    text = request.args.get('text', default='', type=str)  # Get parameters (e.g., tts?lang=vi&text=Đoan is a good older brother, even though he doesn't have a younger sister)
    lang = request.args.get('lang', default='', type=str)  # Get parameters (e.g., tts?lang=vi&text=Đoan is a good older brother, even though he doesn't have a younger sister)
    prompt = f"translate Vietnamese to Japanese: \"{text}\"" if lang == "vi" else f"translate Japanese to Vietnamese: \"{text}\""  # Input for ChatGPT training; here, we set two languages: vi or ja
    langTrans = "ja" if lang == "vi" else 'vi'  # Output language for speech synthesis
    response = openai.Completion.create(
        engine="text-davinci-003",  # Model evaluated by ChatGPT for the best translation results
        prompt=prompt,
        max_tokens=100,  # Maximum characters in the response
        temperature=0.0,  # Higher temperature results in more diverse but less accurate output; for translation, we set it to 0.0
        top_p=1.0,  # Determines the proportion of considered words; lower values result in fewer words, so we set it to 1.0
        n=1,  # Length of the generated text (n); here, we set it to 1 for a 1:1 translation
        stop=None,  # Ensure the model stops after generating a complete word or sentence
    )
    translated_text = response['choices'][0]['text'].strip()  # Get ChatGPT's translation result
    tts = gTTS(text=translated_text, lang=langTrans, slow='false')  # Convert the result to speech
    tts.save('/tmp/hello.mp3')  # Save to storage (using /tmp so Vercel has permission to save audio files in a temporary directory)
    return send_file("/tmp/hello.mp3", as_attachment=False)  # Send the speech result

if __name__ == '__main__':
    app.run(host='0.0.0.0')

{
    "version": 2,
    "builds": [
        {
            "src": "./index.py",
            "use": "@vercel/python"
        }
    ],
    "routes": [
        {
            "src": "/(.*)",
            "dest": "/"
        }
    ]
}