How I Built 10 Python Micro-Tools That Generate Passive Income Daily

From automation scripts to micro SaaS — small projects, real revenue

A few years ago, I realized I was spending more time chasing big projects than actually building useful tools. I decided to switch strategies: small, high-impact Python scripts that solve real problems, scale quickly, and — most importantly — could generate income even when I wasn’t actively working.

Over time, I built 10 micro-tools. Some took a single weekend. Others were polished over a couple of evenings. Combined, they now earn me recurring revenue while automating tedious tasks I used to hate. Here’s the human story behind each one.

1. Flask + Jinja2: My First $50 Tool in a Weekend I built a micro web app for freelancers to generate invoice PDFs. No database, no login, just form input → styled HTML → PDF. Stack: Flask + WeasyPrint

pip install flask weasyprint
from flask import Flask, render_template, request
from weasyprint import HTML
app = Flask(__name__)
@app.route("/", methods=["GET", "POST"])
def index():
    if request.method == "POST":
        html = render_template("invoice.html", data=request.form)
        HTML(string=html).write_pdf("invoice.pdf")
        return "Invoice created!"
    return render_template("form.html")

Shared it in two Reddit communities — $50 by Day 2.

2. Streamlit: Turning Python Scripts Into Income-Generating Web Apps

I turned a CSV cleaner into a web app that small Etsy sellers now use. Users upload their CSVs, the script removes duplicates, and outputs a clean version ready for upload.

Stack: Streamlit + pandas

pip install streamlit pandas
import streamlit as st
import pandas as pd
uploaded_file = st.file_uploader("Upload your CSV")
if uploaded_file:
    df = pd.read_csv(uploaded_file)
    df_cleaned = df.dropna().drop_duplicates()
    st.write(df_cleaned)
    df_cleaned.to_csv("cleaned.csv", index=False)

I deployed it on Streamlit Cloud with a simple checkout → $300 in passive downloads in a month.

3. FastAPI + Uvicorn: PDF-to-Text API Writers and researchers wanted a fast way to extract text from PDFs. I built an API that accepts PDFs and returns JSON.

pip install fastapi uvicorn PyMuPDF
from fastapi import FastAPI, UploadFile
import fitz  # PyMuPDF
app = FastAPI()
@app.post("/extract/")
async def extract_text(file: UploadFile):
    doc = fitz.open(stream=await file.read(), filetype="pdf")
    text = "".join([page.get_text() for page in doc])
    return {"text": text}

Added Stripe + rate limiting → $10/month subscriptions from SEO agencies.

4. Selenium + Twilio: Stock Alert Bot I set up an alert system that watches stock prices and sends a text when anything drops >5%.

pip install selenium twilio
from selenium import webdriver
from twilio.rest import Client
client = Client("TWILIO_SID", "TWILIO_TOKEN")
driver = webdriver.Chrome()
driver.get("https://finance.yahoo.com/quote/TSLA")
price = float(driver.find_element("xpath", '//*[@data-field="regularMarketPrice"]').text)
if price < 600:
    client.messages.create(
        body=f"TSLA below $600: ${price}",
        from_="+123456789",
        to="+987654321"
    )

Friends requested custom versions → $180 selling bots on Gumroad.

5. OpenAI + Gradio: Resume Review App Built a simple app where users upload resumes → GPT-4 critiques → feedback displays instantly.

pip install openai gradio
import openai, gradio as gr
def review_resume(text):
    prompt = f"Review this resume and give actionable feedback:\n{text}"
    res = openai.ChatCompletion.create(
        model="gpt-4",
        messages=[{"role":"user","content":prompt}]
    )
    return res['choices'][0]['message']['content']
gr.Interface(fn=review_resume, inputs="textbox", outputs="text").launch()

$5 one-time payments → $400 in three weeks from social media marketing.

6. Pandas + Plotly + PDFKit: Auto Monthly Reports Clients wanted weekly analytics reports. Automated using pandas for data, plotly for charts, pdfkit for PDF generation.

import pandas as pd, plotly.express as px, pdfkit
df = pd.read_csv("sales.csv")
fig = px.bar(df, x="week", y="revenue", title="Weekly Revenue")
fig.write_html("report.html")
pdfkit.from_file("report.html", "report.pdf")

Cron-job → hands-off revenue: $200/month.

7. Python + Notion API: Automation Scripts Automated dashboards, habit logs, task resets. Bundled scripts into .py files → $10 products.

pip install notion-client
from notion_client import Client
notion = Client(auth="your-secret")
page = notion.pages.retrieve("page_id")
print(page["properties"])

Earned $850 via Gumroad and Reddit marketing.

8. BeautifulSoup + Flask: Niche Web Monitors Scraped websites for updates (e.g., government job listings). Flask backend + Google Sheets → email alerts.

pip install beautifulsoup4 flask requests

Charged $5/month → 20 clients.

9. PyPDF2 + OCR + Email Automation: Document Parser Automated scanned PDFs → extract key info → email summary. Clients: lawyers, admins → $600/month recurring.

10. Full Python SaaS (No JavaScript!) Flask + Stripe + SQLite + Bootstrap → built “InvoiceMailer.”

Users sign up → generate/send invoices → Stripe handles billing.
Scaled to $100 MRR.

Key Lessons From Building Micro-Tools

Start with small, repeatable tasks.
Modular, maintainable code makes scaling easier.
Automation amplifies your value, doesn’t replace humans.
Micro SaaS can generate recurring revenue without heavy upfront investment.
Social media and small communities can kickstart marketing with almost zero budget.

Read the full article here: https://python.plainenglish.io/how-i-built-10-python-micro-tools-that-generate-passive-income-daily-803e9563d247