Collection of C++ and python questions:
The following content involves the creation of api
If I use QT in C++ language to write the software interface, and then use python language to write things related to artificial intelligence. For example, some models, so now I want to call the model written in python in the software written by QT, so will it cause incompatibility between C language and python language? If it is compatible, what should I do? If not, how can I modify it?
First of all, QT is usually written in C++, and the question is about how to integrate Python code in QT (C++) application, especially artificial intelligence related models.
- Compatibility issues :
There are no direct compatibility issues between C++ and Python. In fact, many tools and libraries allow interoperability between the two languages.
- How to integrate :
Using the Python C API : Python provides a C API that allows C and C++ code to call Python code directly. You can use this API to embed a Python interpreter in your QT application, and then call Python scripts or modules.
Using Boost.Python : Boost.Python is a C++ library that simplifies interoperability between C++ and Python. Using it, you can easily call Python code in C++ and vice versa.
Using PyQt or PySide : If your main application logic is written in Python, you may consider using PyQt or PySide to create QT applications directly in Python. This way, you can directly call your AI model without embedding Python in C++.
- Note :
Performance : Embedding Python in a C++ application may introduce some additional performance overhead. Make sure your application's performance meets your needs.
Python environment : Make sure your application has access to the correct Python environment and all necessary dependencies.
Thread Safety : If your QT application is multi-threaded, make sure to manage the Python GIL (Global Interpreter Lock) properly.
- If not directly integrated :
Using an API : You can run your Python AI model as a standalone service, for example using Flask or FastAPI to create a REST API. Your QT application can then communicate with this service via HTTP requests.
In conclusion, it is perfectly possible to integrate Python code into a QT (C++) application, just choose the method that best suits your application and needs.
About the fourth point of using api instructions:
The way to use the API is to decouple your Python AI model from your QT application, making them run as two separate services. The benefit of this approach is that it provides more flexibility and can be extended and maintained more easily, but it may add some latency, because your QT application needs to communicate with the Python service through network requests.
Here are the steps on how to implement this method:
- Create a Python REST API :
Choose a framework : There are many Python frameworks that can help you create REST APIs, such as Flask, FastAPI, and Django REST Framework. FastAPI is a modern, fast (high performance) web framework for building APIs based on standard Python type hints.
Define endpoints : Define one or more endpoints for your AI model. For example, you could have a /predict endpoint that takes input data and returns the model's predictions.
Processing the request : When the QT application sends a request, your API needs to receive input data, pass it to your AI model, and return the result to the QT application.
Error handling : Make sure your API can gracefully handle errors, such as malformed input data or problems with your model.
- Call API in QT application :
Sending requests : You can use Qt's network modules (such as QNetworkAccessManager) to send HTTP requests to your Python API.
Handling the response : When your API returns a result, your QT application needs to parse the response and display the result to the user.
Error handling : Make sure your QT application can handle any errors or exceptions returned by the API.
- deployment :
Local deployment : If your QT application and Python API are both running on the same machine, you can run your API on localhost (eg http://localhost:5000 ).
Remote Deployment : If you want to deploy your Python API to a remote server or cloud service, you need to ensure that your QT application can access that server, and consider securing your communication with HTTPS.
Containerization : Consider using Docker or other container technologies to deploy your Python API, which can simplify the deployment process and ensure a consistent environment.
- Security :
Authentication : If your API is public, consider adding an authentication mechanism such as Basic Authentication or Token Authentication to secure your API.
Data protection : Make sure your API uses HTTPS encrypted communication, and consider encrypting sensitive data.
Overall, running your Python AI model as a standalone REST API is a very powerful and flexible approach, allowing you to easily communicate with multiple clients and applications.
How to use FastAPI
FastAPI is a very popular Python framework for creating RESTful APIs . To start using FastAPI , you need to follow these steps:
- Install FastAPI and Uvicorn :
FastAPI itself is a framework, and Uvicorn is an ASGI server for running FastAPI applications.
You can install them with pip:
pip install fastapi[all] uvicorn
Here [all] means to install all optional dependencies of FastAPI, including data authentication, OAuth2 password flow, etc.
- Create a basic FastAPI application:
Create a new Python file, say main.py , and add the following content:
from fastapi import FastAPI
app = FastAPI()
@app.get("/")
def read_root():
return {
"Hello": "World"}
This will create a simple FastAPI application with a root endpoint that returns a JSON response when you access it.
- Run your FastAPI application:
Run your FastAPI application with Uvicorn:
uvicorn main:app --reload
Here main:app refers to the app instance in the main.py file . --reload causes the server to restart automatically when you modify code, which is useful during development.
Once the server is running, you can view your API by visiting http://127.0.0.1:8000/ in your browser .
- Extend your API :
You can easily add more endpoints, integrate databases, use dependency injection, etc. The documentation of FastAPI is very detailed, providing a large number of examples and tutorials.
- Documentation :
FastAPI automatically generates API documentation. Just visit http://127.0.0.1:8000/docs and you can see an interactive API documentation listing all available endpoints, request parameters and response formats.
Overall, FastAPI provides a simple and efficient way to create high-performance RESTful APIs. It was designed with the needs of modern web development in mind, such as data validation, asynchronous support, and type safety.
what is deployment
"Deployment" (Deployment) is a key step in the software development process, which refers to the migration of the developed application or system from the development environment to the production environment, so that it can be accessed and used by end users or other systems. In simple terms, deployment is putting your software in a place where users can access it.
Deployment usually involves the following aspects:
- Environment selection :
Local Deployment : Run the application on a local machine, typically for development and testing.
Server Deployment : Running the application on a dedicated server or virtual machine, making it accessible to external users.
Cloud Deployment : Running an application using resources provided by a cloud service provider such as AWS, Azure, Google Cloud, etc.
- Configuration :
Adjust the configuration of the application, such as database connection, API key, domain name settings, etc., according to the deployment environment.
- Data migration :
If your application uses a database, you may need to migrate data from one environment to another, or perform updates to the database schema.
- Scalability :
Dynamically adjust resources, such as increasing or decreasing server instances, based on application traffic and performance requirements.
- Security :
Make sure the application is secure in production, such as using HTTPS, configuring firewalls, restricting unnecessary port access, etc.
- Monitoring and logging :
Monitor application performance and health in production environments, collecting and analyzing logs for troubleshooting.
- Updates and maintenance :
Over time, new versions of the application may need to be deployed, bugs fixed, or new features added.
For FastAPI applications, deployment usually involves choosing a suitable ASGI server (such as Uvicorn , Daphne , or Hypercorn ), configuring it to run your application, and putting it behind a reverse proxy (such as Nginx or Apache). You can then choose to run your application on a local server, virtual private server, or cloud service, depending on your needs and budget.
ASGI server and deployment location
Choosing a suitable ASGI server and choosing a deployment location (such as a local server or a cloud server) are two different concepts:
- ASGI server :
ASGI (Asynchronous Server Gateway Interface) is a standard that defines how to run Python web applications in an asynchronous environment.
When we talk about choosing a suitable ASGI server, we mean choosing one capable of running your FastAPI application, such as Uvicorn, Daphne or Hypercorn. These are programs that run on your deployment environment (whether on-premises or in the cloud).
- Deployment location :
You can choose to deploy your FastAPI application on a local server . This means that the application runs on your physical machine, such as your personal computer or a company's internal server.
You can also choose to deploy on cloud servers such as AWS EC2, Google Cloud Compute Engine or Azure Virtual Machines. These cloud service providers provide you with virtual server resources on which you can run your applications.
No matter which deployment location you choose, you will need an ASGI server to run your FastAPI application.
In summary, choosing an ASGI server is a decision about how to run your FastAPI application, and choosing a deployment location is a decision about where to run it. Both are separate decisions that can be chosen based on your needs and resources.
Replenish
Uvicorn , Daphne or Hypercorn . These are programs that run on your deployment environment (whether on-premises or in the cloud). What's the meaning?
explain:
Uvicorn, Daphne, and Hypercorn are all ASGI servers. An ASGI server is a special web server capable of running Python web applications that support the ASGI specification, such as FastAPI. These servers can handle requests asynchronously, which means they can handle multiple requests at the same time instead of one after the other, improving performance.
When I say "these are programs that run on your deployment environment (whether on-premises or in the cloud)" I mean:
- Local Deployment : If you run the FastAPI application on your own computer or on your company's internal server, then Uvicorn, Daphne or Hypercorn will also run on the same machine. You would start these servers, and they would listen on some port (say 8000), and wait for and process requests from users.
- Cloud Deployment : If you choose to run your FastAPI application on a cloud service such as AWS, Google Cloud or Azure, then Uvicorn, Daphne or Hypercorn will also run on a virtual machine or container of this cloud service. These servers listen to a port in the cloud environment, waiting for and processing requests from users.
No matter which deployment method you choose, you will need an ASGI server to run your FastAPI application. This is what I mean by " programs that run on your deployment environment " .
Link:
FastAPI Deployment | Geek Tutorial (geek-docs.com)
Detailed tutorial of FastAPI deployment on cloud server - Zhihu (zhihu.com)