When running email marketing campaigns, one of the biggest challenges is ensuring that your messages reach the inbox rather than the spam folder.
Apache SpamAssassin is a widely used tool for many email clients and email filtering tools to classify messages as spam. In this post, we’ll explore how to leverage SpamAssassin to validate if your email will be marked as spam and why it's marked so.
The logic will be packaged as an API and deployed online, so that it can be integrated into your workflow.
Why Apache SpamAssassin?
Apache SpamAssassin is an open-source spam detection platform maintained by the Apache Software Foundation. It uses a multitude of rules, Bayesian filtering, and network tests to assign a spam “score” to a given email. Generally, an email scoring 5 or above is at high risk of being flagged as spam.
Since that SpamAssassin’s scoring is transparent and well-documented, you can also use it to identify exactly which aspects of your email are causing high spam scores and improve your writing.
Getting Started with SpamAssassin
SpamAssassin is designed to run on Linux systems. You'll need a Linux OS or create a Docker VM to install and run it.
On Debian or Ubuntu systems, install SpamAssassin with:
apt-get update && apt-get install -y spamassassin sa-update
The sa-update command ensures that SpamAssassin’s rules are up-to-date.
Once installed, you can pipe an email message into SpamAssassin’s command-line tool. The output includes an annotated version of the email with spam scores and explains which rules are triggered.
A typical usage might look like this:
spamassassin -t < input_email.txt > results.txt
results.txt will then contain the processed email with SpamAssassin’s headers and scores.
Use FastAPI to Wrap SpamAssassin as an API
Next, let’s create a simple API that accepts two email fields: subject and html_body. It will pass the fields to SpamAssassin and return the validation result.
Example FastAPI Code
from fastapi import FastAPI from datetime import datetime, timezone from email.utils import format_datetime from pydantic import BaseModel import subprocess import re def extract_analysis_details(text): rules_section = re.search(r"Content analysis details:.*?(pts rule name.*?description.*?)\n\n", text, re.DOTALL) if not rules_section: return [] rules_text = rules_section.group(1) pattern = r"^\s*([-\d.]+)\s+(\S+)\s+(.+)$" rules = [] for line in rules_text.splitlines()[1:]: match = re.match(pattern, line) if match: score, rule, description = match.groups() rules.append({ "rule": rule, "score": float(score), "description": description.strip() }) return rules app = FastAPI() class Email(BaseModel): subject: str html_body: str @app.post("/spam_check") def spam_check(email: Email): # assemble the full email message = f"""From: example@example.com To: recipient@example.com Subject: {email.subject} Date: {format_datetime(datetime.now(timezone.utc))} Content-Type: text/html; charset="UTF-8" {email.html_body}""" # Run SpamAssassin and capture the output directly output = subprocess.run(["spamassassin", "-t"], input=message.encode('utf-8'), capture_output=True) output_str = output.stdout.decode('utf-8', errors='replace') details = extract_analysis_details(output_str) return {"result": details}
The response will contain the analysis details of SpamAssassin’s results.
Let's take this input as an example:
subject: Test Email html_body: <html> <body> <p>This is an <b>HTML</b> test email.</p> </body> </html>
The response would be like this:
[ { "rule": "MISSING_MID", "score": 0.1, "description": "Missing Message-Id: header" }, { "rule": "NO_RECEIVED", "score": -0.0, "description": "Informational: message has no Received headers" }, { "rule": "NO_RELAYS", "score": -0.0, "description": "Informational: message was not relayed via SMTP" }, { "rule": "HTML_MESSAGE", "score": 0.0, "description": "BODY: HTML included in message" }, { "rule": "MIME_HTML_ONLY", "score": 0.1, "description": "BODY: Message only has text/html MIME parts" }, { "rule": "MIME_HEADER_CTYPE_ONLY", "score": 0.1, "description": "'Content-Type' found without required MIME headers" } ]
Deploying the API Online
Running SpamAssassin requires a Linux environment with the software installed. Traditionally, you might need an EC2 instance or a DigitalOcean droplet to deploy, which can be costly and tedious, especially if your usage is low-volume.
As for serverless platforms, they often do not provide a straightforward way to run system packages like SpamAssassin.
Now with Leapcell, you can deploy any system packages like SpamAssassin, meanwhile keep the service serverless - you only pay for invocations, which is usually cheaper.
Deploying the API on Leapcell is very easy. You don't have to worry about how to set up a Linux environment or how to build a Dockerfile. Just select the Python image for deploying, and fill in the "Build Command" field properly.
Once deployed, you’ll have an endpoint you can call on-demand. Whenever your API is invoked, it will run SpamAssassin, score the email, and return the response.
The above is the detailed content of Build an API to Keep Your Marketing Emails Out of Spam. For more information, please follow other related articles on the PHP Chinese website!

Hot AI Tools

Undress AI Tool
Undress images for free

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

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

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

SublimeText3 Chinese version
Chinese version, very easy to use

Zend Studio 13.0.1
Powerful PHP integrated development environment

Dreamweaver CS6
Visual web development tools

SublimeText3 Mac version
God-level code editing software (SublimeText3)

Polymorphism is a core concept in Python object-oriented programming, referring to "one interface, multiple implementations", allowing for unified processing of different types of objects. 1. Polymorphism is implemented through method rewriting. Subclasses can redefine parent class methods. For example, the spoke() method of Animal class has different implementations in Dog and Cat subclasses. 2. The practical uses of polymorphism include simplifying the code structure and enhancing scalability, such as calling the draw() method uniformly in the graphical drawing program, or handling the common behavior of different characters in game development. 3. Python implementation polymorphism needs to satisfy: the parent class defines a method, and the child class overrides the method, but does not require inheritance of the same parent class. As long as the object implements the same method, this is called the "duck type". 4. Things to note include the maintenance

Iterators are objects that implement __iter__() and __next__() methods. The generator is a simplified version of iterators, which automatically implement these methods through the yield keyword. 1. The iterator returns an element every time he calls next() and throws a StopIteration exception when there are no more elements. 2. The generator uses function definition to generate data on demand, saving memory and supporting infinite sequences. 3. Use iterators when processing existing sets, use a generator when dynamically generating big data or lazy evaluation, such as loading line by line when reading large files. Note: Iterable objects such as lists are not iterators. They need to be recreated after the iterator reaches its end, and the generator can only traverse it once.

The key to dealing with API authentication is to understand and use the authentication method correctly. 1. APIKey is the simplest authentication method, usually placed in the request header or URL parameters; 2. BasicAuth uses username and password for Base64 encoding transmission, which is suitable for internal systems; 3. OAuth2 needs to obtain the token first through client_id and client_secret, and then bring the BearerToken in the request header; 4. In order to deal with the token expiration, the token management class can be encapsulated and automatically refreshed the token; in short, selecting the appropriate method according to the document and safely storing the key information is the key.

A common method to traverse two lists simultaneously in Python is to use the zip() function, which will pair multiple lists in order and be the shortest; if the list length is inconsistent, you can use itertools.zip_longest() to be the longest and fill in the missing values; combined with enumerate(), you can get the index at the same time. 1.zip() is concise and practical, suitable for paired data iteration; 2.zip_longest() can fill in the default value when dealing with inconsistent lengths; 3.enumerate(zip()) can obtain indexes during traversal, meeting the needs of a variety of complex scenarios.

InPython,iteratorsareobjectsthatallowloopingthroughcollectionsbyimplementing__iter__()and__next__().1)Iteratorsworkviatheiteratorprotocol,using__iter__()toreturntheiteratorand__next__()toretrievethenextitemuntilStopIterationisraised.2)Aniterable(like

Assert is an assertion tool used in Python for debugging, and throws an AssertionError when the condition is not met. Its syntax is assert condition plus optional error information, which is suitable for internal logic verification such as parameter checking, status confirmation, etc., but cannot be used for security or user input checking, and should be used in conjunction with clear prompt information. It is only available for auxiliary debugging in the development stage rather than substituting exception handling.

TypehintsinPythonsolvetheproblemofambiguityandpotentialbugsindynamicallytypedcodebyallowingdeveloperstospecifyexpectedtypes.Theyenhancereadability,enableearlybugdetection,andimprovetoolingsupport.Typehintsareaddedusingacolon(:)forvariablesandparamete

To create modern and efficient APIs using Python, FastAPI is recommended; it is based on standard Python type prompts and can automatically generate documents, with excellent performance. After installing FastAPI and ASGI server uvicorn, you can write interface code. By defining routes, writing processing functions, and returning data, APIs can be quickly built. FastAPI supports a variety of HTTP methods and provides automatically generated SwaggerUI and ReDoc documentation systems. URL parameters can be captured through path definition, while query parameters can be implemented by setting default values ??for function parameters. The rational use of Pydantic models can help improve development efficiency and accuracy.
