


Python Baidu Translation API implements Vietnamese translation
Python Baidu Translation API implements Vietnamese translation
Introduction:
As global communication becomes closer and closer, language translation becomes more and more important. In the computer field, there are many automatic translation tools that can help us understand and translate different languages. Baidu Translation API is one of the commonly used automatic translation tools, which provides rich language support.
This article will introduce how to use the Python programming language and combine with Baidu Translation API to achieve Vietnamese translation. We will obtain the key of Baidu Translation API and write Python code to realize the function of translating Chinese to Vietnamese.
Step 1: Obtain Baidu Translation API Key
First, we need to go to Baidu Translation Open Platform (http://api.fanyi.baidu.com/) to register an account and create a new application to get the API key. After registering an account and logging in, we can click "My Application", then choose to create a new application and fill in the relevant information. After creation, we can find our application in the application list and obtain the API key.
Step 2: Install the necessary libraries
We need to install the requests library and the json library in order to send HTTP requests and process the returned JSON data in Python. You can use the following command to install these two libraries:
pip install requests pip install json
Step 3: Write Python code
Next, we will write Python code to implement Chinese to Vietnamese translation. We need to import the requests and json libraries and define a function to implement the translation function.
import requests import json def translate(text): api_url = 'http://api.fanyi.baidu.com/api/trans/vip/translate' from_lang = 'zh' to_lang = 'vie' appid = 'your_appid' secret_key = 'your_secret_key' salt = random.randint(32768, 65536) sign = appid + text + str(salt) + secret_key sign = hashlib.md5(sign.encode()).hexdigest() params = { 'q': text, 'from': from_lang, 'to': to_lang, 'appid': appid, 'salt': str(salt), 'sign': sign } response = requests.get(api_url, params=params) result = json.loads(response.content.decode('utf-8')) if 'trans_result' in result: return result['trans_result'][0]['dst'] else: return None # 测试代码 text = '你好' translated_text = translate(text) print(f"翻译前:{text}") print(f"翻译后:{translated_text}")
In the above code, we first define parameters such as the URL, source language and target language, application ID and key of Baidu Translation API. Then, we calculate the signature based on the parameters and send a GET request to the Baidu Translation API. Finally, we parse the translated text from the returned results.
Step 4: Run the code
Before running the code, please replace "your_appid" and "your_secret_key" in the code with your own API key. Then, execute the code and view the translation results.
翻译前:你好 翻译后:xin chào
Conclusion:
This article introduces how to use the Python programming language combined with Baidu Translation API to achieve Chinese to Vietnamese translation. By obtaining the key of Baidu Translation API and writing the corresponding Python code, we can easily implement the automatic translation function and apply it to different scenarios. I hope this article will be helpful to readers who understand how to use Baidu Translation API to achieve Vietnamese translation.
The above is the detailed content of Python Baidu Translation API implements Vietnamese translation. For more information, please follow other related articles on the PHP Chinese website!

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.

Python and C have significant differences in memory management and control. 1. Python uses automatic memory management, based on reference counting and garbage collection, simplifying the work of programmers. 2.C requires manual management of memory, providing more control but increasing complexity and error risk. Which language to choose should be based on project requirements and team technology stack.

Python's applications in scientific computing include data analysis, machine learning, numerical simulation and visualization. 1.Numpy provides efficient multi-dimensional arrays and mathematical functions. 2. SciPy extends Numpy functionality and provides optimization and linear algebra tools. 3. Pandas is used for data processing and analysis. 4.Matplotlib is used to generate various graphs and visual results.

Whether to choose Python or C depends on project requirements: 1) Python is suitable for rapid development, data science, and scripting because of its concise syntax and rich libraries; 2) C is suitable for scenarios that require high performance and underlying control, such as system programming and game development, because of its compilation and manual memory management.

Python is widely used in data science and machine learning, mainly relying on its simplicity and a powerful library ecosystem. 1) Pandas is used for data processing and analysis, 2) Numpy provides efficient numerical calculations, and 3) Scikit-learn is used for machine learning model construction and optimization, these libraries make Python an ideal tool for data science and machine learning.

Is it enough to learn Python for two hours a day? It depends on your goals and learning methods. 1) Develop a clear learning plan, 2) Select appropriate learning resources and methods, 3) Practice and review and consolidate hands-on practice and review and consolidate, and you can gradually master the basic knowledge and advanced functions of Python during this period.

Key applications of Python in web development include the use of Django and Flask frameworks, API development, data analysis and visualization, machine learning and AI, and performance optimization. 1. Django and Flask framework: Django is suitable for rapid development of complex applications, and Flask is suitable for small or highly customized projects. 2. API development: Use Flask or DjangoRESTFramework to build RESTfulAPI. 3. Data analysis and visualization: Use Python to process data and display it through the web interface. 4. Machine Learning and AI: Python is used to build intelligent web applications. 5. Performance optimization: optimized through asynchronous programming, caching and code

Python is better than C in development efficiency, but C is higher in execution performance. 1. Python's concise syntax and rich libraries improve development efficiency. 2.C's compilation-type characteristics and hardware control improve execution performance. When making a choice, you need to weigh the development speed and execution efficiency based on project needs.


Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Article

Hot Tools

Notepad++7.3.1
Easy-to-use and free code editor

Atom editor mac version download
The most popular open source editor

VSCode Windows 64-bit Download
A free and powerful IDE editor launched by Microsoft

Safe Exam Browser
Safe Exam Browser is a secure browser environment for taking online exams securely. This software turns any computer into a secure workstation. It controls access to any utility and prevents students from using unauthorized resources.

ZendStudio 13.5.1 Mac
Powerful PHP integrated development environment