Build an API to Keep Your Marketing Emails Out of Spam
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
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
AI Hentai Generator
Generate AI Hentai for free.
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)
Hot Topics
1359
52
How to solve the permissions problem encountered when viewing Python version in Linux terminal?
Apr 01, 2025 pm 05:09 PM
Solution to permission issues when viewing Python version in Linux terminal When you try to view Python version in Linux terminal, enter python...
How Do I Use Beautiful Soup to Parse HTML?
Mar 10, 2025 pm 06:54 PM
This article explains how to use Beautiful Soup, a Python library, to parse HTML. It details common methods like find(), find_all(), select(), and get_text() for data extraction, handling of diverse HTML structures and errors, and alternatives (Sel
How to Perform Deep Learning with TensorFlow or PyTorch?
Mar 10, 2025 pm 06:52 PM
This article compares TensorFlow and PyTorch for deep learning. It details the steps involved: data preparation, model building, training, evaluation, and deployment. Key differences between the frameworks, particularly regarding computational grap
Mathematical Modules in Python: Statistics
Mar 09, 2025 am 11:40 AM
Python's statistics module provides powerful data statistical analysis capabilities to help us quickly understand the overall characteristics of data, such as biostatistics and business analysis. Instead of looking at data points one by one, just look at statistics such as mean or variance to discover trends and features in the original data that may be ignored, and compare large datasets more easily and effectively. This tutorial will explain how to calculate the mean and measure the degree of dispersion of the dataset. Unless otherwise stated, all functions in this module support the calculation of the mean() function instead of simply summing the average. Floating point numbers can also be used. import random import statistics from fracti
What are some popular Python libraries and their uses?
Mar 21, 2025 pm 06:46 PM
The article discusses popular Python libraries like NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, Django, Flask, and Requests, detailing their uses in scientific computing, data analysis, visualization, machine learning, web development, and H
How to Create Command-Line Interfaces (CLIs) with Python?
Mar 10, 2025 pm 06:48 PM
This article guides Python developers on building command-line interfaces (CLIs). It details using libraries like typer, click, and argparse, emphasizing input/output handling, and promoting user-friendly design patterns for improved CLI usability.
How to efficiently copy the entire column of one DataFrame into another DataFrame with different structures in Python?
Apr 01, 2025 pm 11:15 PM
When using Python's pandas library, how to copy whole columns between two DataFrames with different structures is a common problem. Suppose we have two Dats...
Explain the purpose of virtual environments in Python.
Mar 19, 2025 pm 02:27 PM
The article discusses the role of virtual environments in Python, focusing on managing project dependencies and avoiding conflicts. It details their creation, activation, and benefits in improving project management and reducing dependency issues.
