translatorai 1.0.1

easy using AI, more texting models!

AI Translator

A powerful Python library for accessing multiple translation services through a unified API.

Table of Contents

• Installation
• Quick Start
• Core Features
• Translation Services
• Language Detection
• API Reference
• Examples
• Error Handling
• Limitations

Installation

pip install translatorai

Quick Start

from translatorai import Translator

# Initialize the translator
translator = Translator()

# Translate text from auto-detected language to English
result = translator.google("Привет, мир!", "en")
print(result['response']['translated_text'])  # Output: "Hello, world!"

Core Features

• Access to 5 different translation services via a unified API
• Automatic language detection
• Support for specific source and target languages
• Error handling with appropriate HTTP status codes
• JSON response format for easy integration

Translation Services

The library provides access to the following translation engines:

Method	Service	Description
google()	Google Translate	Google's neural machine translation service
deepl()	DeepL	Highly accurate translation service known for natural-sounding results
amazon()	Amazon Translate	Amazon Web Services' neural machine translation service
modern_mt()	ModernMT	Adaptive neural machine translation
libre()	LibreTranslate	Open-source machine translation

Language Detection

The library includes automatic language detection which can be used both as a standalone feature and as part of the translation process.

# Detect language
detection = translator.detect("гЃ“г‚“гЃ«гЃЎгЃЇ")
print(detection["data"]["language_probability"]["code"])  # Output: "ja"

API Reference

Translator Class

Translator()

Initializes a new translator instance.

Translation Methods

Each translation method follows the same signature:

def method_name(text, target, source='auto')

• text (str): The text to translate
• target (str): Target language code (e.g., "en", "fr", "de")
• source (str, optional): Source language code. Defaults to 'auto' for automatic detection.

Returns a JSON object with translation results.

Available Methods

Google Translate

translator.google(text, target, source='auto')

DeepL

translator.deepl(text, target, source='auto')

Amazon Translate

translator.amazon(text, target, source='auto')

ModernMT

translator.modern_mt(text, target, source='auto')

LibreTranslate

translator.libre(text, target, source='auto')

Language Detection

translator.detect(text)

• text (str): The text to analyze (only first 100 characters are used)

Returns a JSON object with detected language information.

Examples

Translating with Different Services

translator = Translator()

# Original text
text = "La vie est belle"

# Translate with different services
google_result = translator.google(text, "en", "fr")
deepl_result = translator.deepl(text, "en", "fr") 
amazon_result = translator.amazon(text, "en", "fr")

print(f"Google: {google_result['response']['translated_text']}")
print(f"DeepL: {deepl_result['response']['translated_text']}")
print(f"Amazon: {amazon_result['response']['translated_text']}")

Using Auto-Detection

# The source language will be automatically detected
result = translator.google("Guten Tag", "en")
print(result['response']['translated_text'])  # Output: "Good day"

Error Handling

The library uses the raise_for_status() method from the requests library to handle HTTP errors. If the translation service returns a non-200 response, an exception will be raised.

try:
    result = translator.google("Hello world", "invalid_language_code")
except requests.exceptions.HTTPError as e:
    print(f"An error occurred: {e}")