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How to Create a Multilingual Translation Bot with ChatGPT and Python and deploy it to Vercel
After extensive research and some sleepless nights (approximately one hour), I’ve discovered a way to create a multilingual translation bot using ChatGPT, Python, and Vercel. In this article, I’ll guide you step-by-step, providing code comments to make it easy to understand.
Step 1: Required Libraries (Python)
First, let’s set up the necessary Python libraries:
# 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
Step 2: Set Up OPEN_API_KEY and Initialize Flask App
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
Step 3: Main Function
@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
Step 4: Run the App
if __name__ == '__main__':
app.run(host='0.0.0.0')
Note: I used PyCharm and Python 3.10 for this example.
Deploying to Vercel
To deploy this locally developed bot to Vercel for free:
- Create a
vercel.json
file (as shown below). - Push your code to Git.
- Link Git with Vercel.
- Voilà! You can check the deployed bot here.
{
"version": 2,
"builds": [
{
"src": "./index.py",
"use": "@vercel/python"
}
],
"routes": [
{
"src": "/(.*)",
"dest": "/"
}
]
}
And there you have it! With just 30 lines of code, you’ve created an impressive translation bot. If you have any questions or need further clarification, feel free to ask! 🚀👍
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