FastAPI vs Flask: Which Is Better for Beginners to Develop Quickly?
This article compares the Python web frameworks Flask and FastAPI, with the core content as follows: Flask, an established lightweight framework born in 2010, is renowned for its "flexibility". It is easy to install (`pip install flask`), with a core focused solely on routing and view functions. It has a gentle learning curve, making it suitable for rapid prototyping, but requires manual handling of JSON and parameter validation. FastAPI (2018), on the other hand, emphasizes high performance. Built on Starlette and Pydantic, it comes with automatic API documentation and data validation. Installation requires Uvicorn (`pip install fastapi uvicorn`), and it has a slightly steeper learning curve (needing an understanding of type hints and Pydantic models). However, it offers long-term efficiency, automatic data validation, and asynchronous support, making it ideal for complex scenarios (e.g., high concurrency and automatic documentation). Conclusion: For simple projects or beginners, choose Flask; for those pursuing modern features and long-term scalability, and who have basic Python knowledge, choose FastAPI. Both have their advantages, and the choice depends on specific needs.
Read MoreFlask and Frontend Frameworks: A Practical Guide to Combining Vue.js with Flask
### A Practical Summary of Combining Flask and Vue.js This article introduces the practical process of combining Flask (backend) with Vue.js (frontend) to achieve front-end and back-end separation development. The advantages of choosing both are clear division of labor (backend handles data, frontend is responsible for interaction), efficient collaboration, and flexible data interaction. For the development environment, Python/Flask (with the cross-domain tool flask-cors) and Node.js/Vue-cli (with the Axios data request tool) need to be installed. The project structure is divided into two independent directories: the back-end Flask and the front-end Vue. In the back-end setup, Flask provides API interfaces through `app.py` (e.g., `/api/users` to get user lists), running on port 5000 and returning JSON data. After creating the Vue project, the front-end uses Axios to request back-end data in `App.vue` and renders the user list with `v-for`. During testing, start the Flask back-end (`python app.py`) and the Vue front-end (`npm run serve`), then access `http://localhost:8080` to see the data obtained from the back-end. Common issues include cross-domain configuration, Axios path errors, and Vue rendering problems, which can be resolved through corresponding methods. Summary: This mode enables efficient collaboration between front and back ends. Future extensions could include user CRUD operations and...
Read MoreFlask and Database Connection: SQLAlchemy Basic Operations
SQLAlchemy is a popular ORM tool for Python that allows database operations through Python classes/objects, avoiding direct SQL writing. It supports multiple databases (e.g., MySQL, SQLite) and is well-suited for Flask development. Installation requires `pip install flask flask-sqlalchemy`, with additional drivers needed for databases like MySQL. During initialization, configure the Flask application and database connection (e.g., SQLite path), then initialize the SQLAlchemy instance. Data models are defined via classes, where each class corresponds to a table and class attributes represent fields (e.g., the `User` class includes `id`, `username`, etc., with primary key, unique, and non-null constraints). Use `db.create_all()` to generate tables within the application context. Core operations (CRUD): Create (instantiate → `db.session.add()` → `commit()`); Read (`query.all()`/`filter_by()` etc.); Update (modify object attributes → `commit()`); Delete (`db.session.delete()` → `commit()`). Workflow: Configure connection → Define models → Create tables → CRUD operations. The advantage is no need to write SQL, facilitating rapid development.
Read MoreFlask Request Methods: Practical Handling of GET and POST Requests
This article introduces the lightweight Python Web framework Flask and HTTP request methods GET/POST. Flask is suitable for rapid development of web applications, with the installation command being `pip install flask`. GET is used to retrieve data (data is in the URL and easily leaked), while POST is used to submit data (data is in the request body and more secure). In practice, handling a login form with Flask: define the `/login` route to support GET/POST, where GET renders the form template and POST retrieves the username and password for verification and returns the result. Key knowledge points: the `methods` parameter of the route supports multiple request methods, `request.form` extracts form data, and `render_template` renders templates. Notes: Only POST requires changing `methods=['POST']`, sensitive data should use POST and HTTPS is recommended, and CSRF protection is necessary in production environments.
Read MoreFlask Template Engine Jinja2: From Basic Syntax to Page Rendering
Jinja2 is the default templating engine for Flask, used for dynamically rendering HTML by combining data with static pages. Core features include variables, conditional statements, loops, template inheritance and inclusion, as well as filters for variable processing. In terms of syntax, variables are embedded using `{{ }}`, conditions with `{% if %}`, and loops with `{% for %}` (supporting the loop state variable and empty list handling); templates inherit from parent templates via `extend` (defining blocks) and reuse fragments with `include`; filters use `|` (e.g., `truncate` to truncate text). In Flask, templates are stored in the `templates` folder and rendered by passing data through the `render_template` function. Mastering these core syntaxes can enhance web development efficiency, and it is recommended to consolidate through practice with template inheritance and data processing.
Read MoreData Storage Fundamentals: How Python Web Saves User Information with SQLite
This article introduces the basic method of implementing web data storage using SQLite and Flask. SQLite is lightweight and easy to use, built into Python, and requires no additional server, making it suitable for beginners. First, the Flask environment needs to be installed. The core steps include creating a user table (with auto-incrementing id, unique username, password, and email fields), implementing registration (parameterized data insertion) and user list display (querying and returning dictionary results) through Python operations. During operations, attention should be paid to password encryption (to prevent plaintext storage), SQL injection prevention, and proper connection closure. The article demonstrates the data persistence process with sample code, emphasizing that SQLite is suitable for small projects and serves as an entry-level tool for learning data storage, with potential for future expansion of functions such as login authentication and ORM.
Read MoreJinja2 Template Engine: Dynamically Rendering Data in Web Pages with Flask (with Examples)
This article introduces the role of template engines in web development and the application of Jinja2 in Flask. Template engines solve the cumbersome problem of splicing backend data with frontend HTML, allowing developers to focus on separating data logic from page structure. Jinja2 is Flask's default template engine, with a concise syntax that supports variable substitution, conditional judgment, loops, filters, and other features. The basic process of using Jinja2 involves: first installing Flask, creating an application and defining routes, preparing backend data (such as user information and article lists), and rendering the template through render_template. Template files should be placed in the 'templates' folder, where data is embedded using {{ variables }}, conditional logic and loops are implemented with {% if %} and {% for %}, and filters are applied using the | operator to process data. Template inheritance, through base.html and child templates, promotes code reusability by reusing page structures. The core syntax of Jinja2 includes variable substitution, conditional judgment, loop traversal, and filters, while template inheritance further optimizes project structure. Mastering Jinja2 enables efficient implementation of dynamic page rendering and is a key tool for connecting data and interfaces in web development.
Read More"PaddlePaddle from Beginner to Alchemist" - Part 14: Deploying Prediction Models on Servers
This article introduces the process of building an image recognition interface using Flask. First, a simple Flask program is used to set up the root path and file upload functionality; subsequently, the image prediction API is implemented, which loads the model and performs inference. After uploading an image, users can directly obtain the classification result and confidence. The entire process includes steps such as environment preparation, code writing, and deployment, making it suitable for beginners to learn the development method of image processing services. Key points: 1. **Flask Setup**: Create the root path and file upload functionality. 2. **Model Loading**: Load the model from PaddlePaddle
Read MoreMy Learning Journey with PaddlePaddle - Note 13: Deploying PaddlePaddle to a Website Server
This tutorial provides a detailed introduction to using PaddlePaddle for basic image classification tasks and deploying the resulting model to a web service. Below is a summary of the tutorial content and some improvement suggestions: ### Summary 1. **Environment Preparation**: - Install necessary libraries such as PaddlePaddle and Flask. - Set up the development environment. 2. **Data Preprocessing**: - Read and preprocess images, including converting to grayscale and resizing. 3. **Model Construction and Training**
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