Generates Webs from Type Hints The Problem I Was Solving I run a laser cutting business. Every day I need to: Process images for engraving Generate reports from production data Share tools with my team \(who don't code\) I wanted something insane: Just type hints → complete web UI So I built it. After a month, I shipped FuncToWeb - a framework that auto-generates production-ready web interfaces from Python type hints. Zero HTML. Zero CSS. Zero JavaScript. from typing import Annotated from func\_to\_web import run from func\_to\_web.types import ImageFile from pydantic import Field def process\_image\( image: ImageFile, brightness: Annotated\[int, Field\(ge=0, le=100\)\] = 50, mode: Literal\["grayscale", "sepia", "blur"\] = "grayscale" \): # Your image processing logic here processed = apply\_filter\(image, brightness, mode\) return processed # PIL Image auto-displayed run\(process\_image\) What you get: ✅ Professional file upload with drag-and-drop ✅ Slider for brightness \(0-100, validated client + server\) https://lnkd.in/gTZejntw

🌤️ Just Built a Real-Time Weather App! 🚀 I'm excited to share my latest project - a Real-Time Weather Application built from scratch using Python Flask and OpenWeatherMap API! ☀️🌧️❄️ ✨ What it does: • Get real-time weather updates for 8 global cities • 5-day detailed weather forecasts • Temperature conversion (Celsius/Fahrenheit) • Beautiful city comparison with interactive graphs • Responsive web interface with stunning visuals 🛠️ Tech Stack: • Backend: Python, Flask • Frontend: HTML5, CSS3 • API Integration: OpenWeatherMap • Data Visualization: Matplotlib • Data Processing: Pandas 🌟 Key Features I Implemented: ✅ Real-time API integration ✅ Multi-city weather comparison ✅ Error handling & validation ✅ Responsive web design ✅ Data visualization charts ✅ Temperature unit conversion This project challenged me to work with real-time data, API integration, and creating user-friendly interfaces. I'm particularly proud of the city comparison feature that visually represents weather patterns across different locations! 📊 💡 The best part? It's completely functional and ready for deployment! Check out the complete project on GitHub: https://lnkd.in/dW2jhUsW #Python #Flask #WebDevelopment #WeatherApp #APIintegration #DataVisualization #Programming #Coding #TechProjects #OpenSource #DeveloperJourney #FullStack #RealTimeData #WebApp

I’m excited to share our recent team project “Database Design Studio”, a web application, developed as part of our 4th Semester course, Database System, under the supervision of Dr. Rudra Pratap Deb Nath Sir. Team Members: Nilanjana Das Jui Subha Shesgin Sumaiya Nazneen ▪️ Overview: Database Design Studio is a full-stack web application built with Flask (backend) and React (frontend) that automates and visualizes database design tasks - including normalization, functional dependency detection, and ER diagram generation. It provides an interactive workflow interface, Excel-like table viewer, and auto-generated ER diagrams to make complex database design processes intuitive and visual. ▪️ Key Features: - File Upload & Cleaning: Upload and preprocess datasets (CSV/Excel). - Normalization (1NF → 3NF): Automated decomposition with key detection. - Functional Dependency Detection: Identifies dependencies, including compound attributes. - Dependency Preservation & Lossless Join: Ensures correctness of decomposition. - Excel-like Table Viewer: Dynamically browse normalized tables. - Code Panel: Displays backend logic for each workflow step. - ER Diagram Generator: Visualizes entities, relationships, and keys using Graphviz. - Interactive Workflow UI: Drag-and-drop flow visualization for database operations. ▪️ Tech Stack: React, Flask, Pandas, Graphviz, Context API, Axios, and Python utilities for data logic and visualization. Live Website: https://lnkd.in/gs9gPTGE Explore the Project: https://lnkd.in/ee9ydxqe Demo Video: https://lnkd.in/eXt_5Csw This project represents our effort to bridge database theory with real-world application - turning normalization and schema design into an interactive learning experience. #DatabaseSystem #DatabaseDesign #React #Flask #Normalization #ERDiagram #FunctionalDependencies #TeamProject #webapp

Plotly Studio - create your own Data Cards component - with 3 cards 5 minutes work!!! - no coding, no point-and-click, one natural language prompt only required this is my prompt: 'Card 1: add a card to show AC (actual sales) for the whole year to this card add a bar chart at the bottom to show AC across all months use blues colour map for the bars Card 2: add a card to show FC (forecast sales) for the whole year to this card add a line chart at the bottom to show FC across all months Card3: add a card to show delta AC/FC for the whole year colour-code the delta add an up or down arrow for the delta All Cards: arrange the cards horizontally use dark theme for all the cards abbreviate each month to three letters in bold white font show month at 315 degrees Tooltip: show Month and metric' what you see in the screenshot is not a chart, or three charts arranged horizontally it is a single data cards component containing three cards, as my prompt states add a card three times there are some differences between a chart and a data card component the latter has no Edit button in App Summary view and the AI-generated Python is different and uses different objects from Plotly and Dash and HTML of course, you can also use a similar prompt to add the cards to the default Data Cards component, if you prefer Eszter Kovacs Brian Julius #plotlystudio #python

🚀 Excited to share my latest project — an Inventory Management System built with Python, Streamlit, and SQLite. It features: 🔐 Role-based login (Admin & Staff) 🧾 Product management (Add, Update, Delete) 💰 Sales tracking & automatic stock updates 📊 Dashboard with charts and total sales summary This project helped me strengthen my skills in database design, Streamlit UI, and data visualization. 🔗 Project Link : https://lnkd.in/g-R9eSfH #Python #Streamlit #SQLite #DataVisualization #ProjectShowcase #WebApp

🚀 Just Launched: PSD Extraction Tool (FastAPI + Render) I recently built and deployed a PSD Extraction Tool that helps design teams easily extract layers from Photoshop (.PSD) files — directly from the browser! 💡 Tech Stack: FastAPI (Python) for backend psd-tools + Pillow for layer extraction Jinja2 for the web interface GitHub for version control Render for deployment 🔁 How it works: 1️⃣ Upload your .psd file (even large ones up to 70 MB) 2️⃣ The tool automatically extracts all layers 3️⃣ Download them instantly as a ZIP — no Photoshop required 🌐 Try it live here → https://lnkd.in/d9wmeS_m 💻 Source code → https://lnkd.in/dCyK78yW This project was a great learning experience — from handling large file uploads in FastAPI to deploying on Render with a smooth CI/CD pipeline. Would love your feedback & ideas for the next version — thinking of adding login + extraction history next 👩💻 #FastAPI #Render #Python #Automation #DesignTools #WebApp #GitHubProjects #WomenInTech

🔥 Day 44 / #100Daysfrontendchallenge — JSON Tree Visualization Today’s challenge was all about turning raw JSON data into a beautiful, structured visual tree — just like you see in advanced developer tools! 💡 Goal: Build an interactive JSON Tree Visualizer where you can paste any JSON data, and it automatically displays a connected hierarchy of nodes — parents, children, arrays, and values — all structured visually. What I Built: A recursive React component system (Root → Parent → Node) Automatic detection for Objects and Arrays Hierarchical rendering with clear parent-child depth Dynamic color coding for nodes and values Smooth visualization layout using flex and spacing logic ⚙️ Tech Used: React + TailwindCSS (no external libraries — built fully from scratch 🚀) 🧩 Next Steps: Add search functionality that finds and auto-scrolls to any key/value in the tree Draw connector lines between nodes for even better visualization Add zoom-in/out transitions for large JSON structures 🎯 Key Learning: Recursion is the heart of tree visualizations — once you understand how to render components recursively, you can create anything from file explorers to org charts to visual data mappers. 💬 “Visualization isn’t about colors — it’s about clarity.” Super fun challenge, and probably one of the most visually satisfying builds so far in this 100-day journey! Source code : https://lnkd.in/gKQnyF5T #100DaysOfCode #FrontendDevelopment #ReactJS #WebDevelopment #LearningInPublic #JSON #Visualization #JavaScript

🚀 DataViz — a smart, full-stack Sales Analytics Dashboard that lets users upload datasets (CSV, Excel, JSON) to instantly visualize revenue, profit, and performance insights. 💡 Built with React.js, Tailwind CSS, Node.js, Express, Python (Pandas/NumPy), Chart.js, and Docker, DataViz bridges the MERN stack with Python analytics for seamless data processing. ⚙️ Architecture: React uploads files → Express (via Multer) stores them → Python cleans & analyzes → Results sent back to server → React visualizes insights through interactive charts. 📊 Designed for exploring trends, top products, and growth metrics — making data visualization intuitive and insightful. 🔗 Live Demo: https://lnkd.in/dhsXV9V9 #WebDevelopment #MERNStack #Python #DataVisualization #SalesAnalytics #FullStack #ReactJS

Why I Prefer Saving Plotly Graphs as HTML? Saving Plotly visuals as HTML keeps them interactive, shareable, and standalone. Unlike static images, HTML files let you zoom, hover, and explore data right in your browser — no Python setup needed. They’re perfect for sharing insights with clients or teammates, embedding in dashboards, or showcasing projects online. Note: if you use include_plotlyjs='cdn' instead of 'inline', the viewer needs internet access to load the Plotly library. If you want full offline capability, use 'inline'.

Plotly Studio - template for those who prefer the dark no coding, no formulas, no GUI point-and-click nothing required except pointing Plotly Studio to your data source it uses AI to generate prompts based on your data each prompt generated creates a written specification and writes quite a bit of Python - all powered by the in-built AI and the Python generates all your visuals - just sit and watch your dashboard appear not even time to grab a coffee as it takes about 2 or 3 minutes, and you end up with maybe 8 to 10 visuals arranged in a dashboard you can tweak the generated prompts - that's how I got dark mode (prompt below) and/or create your own prompts in natural language - each prompt you write results in a new visual I chose the Theme object and looked at its prompt - it was one line I added a second line to request dark mode everywhere Here is the finished prompt: 'Professional business analytics theme emphasizing data clarity and insights, clean modern typography, and subtle grid lines for precise data reading Use the plotly_dark template' the first line had been written by AI - I added the second line (not a lot of work!) there are other named templates apart from plotly_dark ("plotly", "plotly_white", "ggplot2", "seaborn", "simple_white", "none") plotly is the default, unless you name a different template or you can create your own templates PS I also added some words to the the AI-generated prompt for the Layout object, specifically to change the Hero section, which is the very top of the dashboard here is one line I added: 'Header Hero background with a gradient of black, blue, light blue, very light blue, light blue, blue, and black colors' Brian Julius Eszter Kovacs #plotlystudio #python