QPrint
Print Intelligence Platform

Every print.
Counted.
Understood.

QPrint Analytics is the intelligence layer for Q-Print campus shop networks — tracking every print job, revenue stream, and failure in real time across all campuses.

View Dashboard How it works
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print jobs processed

Campuses

Success rate

Active shops

Revenue total

Scroll

§ Why QPrint

Print shops operating
completely blind.

University print shops process thousands of jobs every week — but without a centralised data layer, operators cannot identify peak demand, catch failures early, or understand which campuses are underserved. Revenue leaks. Opportunities vanish.

QPrint Analytics is the cloud dashboard Q-Print shops sync to — giving campus administrators a single source of truth for every job, across every shop, in real time.

Campuses Monitored

Multi-university deployment

5m

Sync Frequency

Data never more than 5 minutes stale

Job Success Rate

Reliability tracked across all active shops

90d

Trend History

Analyse up to 90 days of print trend data

§ Data Flow

How it works

Q-Print is an end-to-end print queue management system. Every job a student submits flows through a transparent pipeline — and the aggregated intelligence surfaces here.

Student Prints

1

Upload PDF at campus shop

qprint-<slug>.local:3000
2

Picks settings — colour, copies, paper size

3

Job submitted

POST /api/jobs/upload
4

File stored + DB record created

qprint.db (SQLite) + file storage
5

FastAPI (port 8000) confirms job queued

Admin Dispatches

1

QueuePanel auto-refreshes (QFileSystemWatcher)

2

Admin double-clicks job → JobDetailDialog

3

Selects printer and clicks Print

4

PyMuPDF renders PDF pages at printer DPI

5

win32print sends to physical printer (Windows)

printer_manager.py

Analytics Sync

1

analytics_sync.py runs every 5 minutes

analytics_sync.py
2

Aggregates daily job stats per shop

3

Pushes to cloud analytics service

POST /api/analytics/sync
4

QPrint Analytics processes and stores data

5

Dashboard reflects latest insights in real time

Tracked per Shop

Jobs CompletedJobs ErroredFiles PrintedColor PagesB&W PagesRevenue (₹)Peak HoursCampus RankingDaily TrendsSuccess Rate30 / 90d History

§ Get Running

Install & Run Q-Print

Q-Print ships a single setup script that handles everything — Python virtualenv, npm install, config files, and directory creation. One command gets you from clone to running.

Prerequisites

Python 3.12 or higher
Node.js 18 LTS or higher
npm (bundled with Node.js)
Windows — required for physical printing via win32print. Web UI and admin panel run on any OS.

Windows only: After setup, install pywin32 manually — it is excluded from requirements.txt for cross-platform compatibility.

Installation

1

Clone the Q-Print repository

$ git clone https://github.com/Vivekv634/q-print.git
$ cd q-print
2

Run the setup script — handles everything

# Verifies Python ≥3.12 + Node ≥18, creates venv, runs npm install, copies config files
$ python setup.py
3

Windows only — install pywin32 for printing

$ server\.venv\Scripts\pip install pywin32

Running

4

Activate the virtual environment

$ source server/.venv/bin/activate # Linux / macOS
$ server\.venv\Scripts\activate # Windows
5

Start everything — FastAPI + Next.js + Admin UI

# Starts: FastAPI (port 8000) · Next.js (port 3000) · PySide6 admin window
$ python main.py

Ports

Next.js web app (student UI):3000
Python FastAPI server:8000

Students open qprint-<slug>.local:3000 in their browser.

§ The Creator

Vivek
Vaish

Developer & Creator

Built Q-Print and QPrint Analytics from scratch to solve a real visibility problem observed across university campus print shop networks.

Every print shop I visited was flying blind. No numbers, no patterns, no decisions. I built Q-Print and QPrint Analytics because the data was always there — it just needed a way to speak.

Vivek Vaish

Creator, Q-Print & QPrint Analytics

§ Open Source

Contribute
or request features

Both Q-Print and QPrint Analytics are open source. If your university has a different setup, or you need a feature — open an issue or send a pull request. Shop owners with technical feedback are especially welcome.

New Campus Integration

Add support for your university's print shop infrastructure or data formats.

Hardware Support

Integrate additional printer models or campus point-of-sale systems.

Analytics Features

Propose new metrics, visualisations, or reporting dimensions.

Performance

Optimise sync speed, API latency, or dashboard load time.

Bug Reports

Found an issue? Open a detailed report with reproduction steps.

Pull Requests

Fork, improve, and submit. All contributions are reviewed and welcome.

Q-Print on GitHubQPrint Analytics