I built a Code Architecture Generator that automatically visualizes Python code structure. 🏗️ THE PROBLEM: When you join a new project or revisit old code, understanding the structure is painful. You spend hours reading through hundreds of lines just to find functions and classes. THE SOLUTION: A tool that does this automatically. Upload any Python file, and instantly see: - All functions and classes - Methods inside each class - Code metrics dashboard - Visual architecture diagram HOW IT WORKS (Technical Deep Dive): 🔹 AST PARSING Python's Abstract Syntax Tree (AST) converts code into a tree structure. My tool walks through this tree and extracts every function, class, and method without executing the code. 🔹 CODE METRICS DASHBOARD Automatically calculates: - Total lines of code - Number of functions - Number of classes - Number of methods - Longest function (lines) - Maximum parameters per function 🔹 ARCHITECTURE DIAGRAM Converts the extracted structure into a visual flowchart using Mermaid.js. Shows parent-child relationships between classes and methods. 🔹 EXPORT OPTIONS - Download as Mermaid code (for further editing) - Download as HTML (opens in any browser) - Download as text report (for documentation) 🔹 FILE SUPPORT - Single Python files (.py) - ZIP folders with multiple files (analyzes first 10 files) TECH STACK: - Python (core logic) - AST (code parsing) - Streamlit (web interface) - Mermaid.js (diagram generation) WHAT I LEARNED: - How Python internally understands code (AST) - Tree traversal algorithms - Converting code structure to visual graphs - Building full-stack web apps with Streamlit - Professional Git/GitHub practices #Python #Streamlit #SoftwareEngineering #Coding #AST #BackendDeveloper #PythonDeveloper #OpenToWork

"Project Showcase: Personal Finance Dashboard using Python" I’m excited to share my recent project — a "Personal Finance Dashboard" developed using "HTML, CSS, and Python (Flask)". About the Project: Managing personal finances can be challenging. This dashboard helps users track their income, monitor expenses, and analyze savings in a simple and interactive way. Key Features: Income and expense tracking Savings calculation and budget analysis Category-wise expense breakdown Smart budget status (Good / Needs Improvement) Clean and user-friendly interface What I Learned: Backend development using Flask Handling user input and data processing in Python Connecting frontend with backend Applying concepts like modular programming and data abstraction "Tech Stack" HTML | CSS | Python (Flask) This project strengthened my understanding of full-stack development and real-world problem solving. Looking forward to building more impactful projects! #Python #Flask #WebDevelopment #StudentProject #FinanceDashboard #LearningJourney

Day 33 of #60DaysOfMiniProjects Today I built a more structured and real-world Python project — an Advanced Expense Tracker (CLI-Based System) Instead of a basic input-output program, I designed a system that manages, analyzes, and stores financial data, making it feel like a real application. What this project does: • Allows users to add and manage daily expenses • Categorizes spending (Food, Travel, etc.) • Calculates total and category-wise spending • Stores data using JSON for persistence • Loads previous data automatically for continuity • Runs interactively in the terminal with a menu-driven system What it generates: • Organized expense records • Spending summaries and insights • A complete command-line financial tracking experience Concepts I worked with: • Object-Oriented Programming (Classes & Objects) • File Handling (JSON) • Data structures and aggregation • Menu-driven CLI design • Real-world problem solving This project helped me understand how to structure larger programs and build systems that feel closer to real-world applications. Next step: Adding search, delete features + upgrading to GUI Learning step by step. Building consistently. Improving every day. #Python #MiniProjects #BuildInPublic #CodingJourney #DeveloperGrowth #LearningInPublic #60DaysOfCode

Complete GUI Project with Database Integration This project demonstrates how to build a full-featured Python GUI application from scratch with real-world functionalities, including: ✔️ Login & Registration System with Validation✔️ Password Recovery using Security Questions✔️ Multi-Window GUI Design✔️ Course & Student Management System✔️ Result Management with Database Integration✔️ CRUD Operations (Create, Read, Update, Delete)✔️ Search, Filter & Dashboard Features Designed as a complete end-to-end solution, this project is ideal for mid-term or final year submissions and for anyone looking to gain practical experience in Python development. Focus is on understanding the full workflow—from UI design to backend database handling and real-time data operations.  #GUI #Database #FinalYearProject #StudentProject #Coding #Programming #SoftwareDevelopment #Vidyawan

Python Series – Day 25: APIs in Python (Get Live Data Easily!) Yesterday, we learned Web Scraping🌐 Today, let’s learn a smarter and cleaner way to get data from websites/apps: 👉 APIs 🧠 What is an API? 👉 API stands for Application Programming Interface It allows one application to communicate with another and exchange data. 📌 Simple meaning: API is a bridge between two systems. 🔹 Real-Life Example 🍔 You order food using an app. 👉 App sends request to restaurant system 👉 Restaurant sends response with order details That communication works like an API 🔥 💻 Why Use APIs Instead of Scraping? ✔️ Structured data (JSON format) ✔️ Faster & reliable ✔️ Official way to access data ✔️ Real-time updates 💻 Example: Get Data from API import requests url = "https://lnkd.in/dwJmappU" response = requests.get(url) data = response.json() print(data) 🔍 Output: { 'userId': 1, 'id': 1, 'title': '...', 'body': '...' } 💻 Example: Access Specific Value print(data["title"]) 👉 Prints title from API response. 🎯 Why APIs are Important? ✔️ Used in weather apps 🌦️ ✔️ Payment gateways 💳 ✔️ Social media apps 📱 ✔️ Stock / Crypto live prices 📈 ✔️ Real-world software development ⚠️ Pro Tip 👉 Learn JSON format well, because most APIs return JSON data. 🔥 One-Line Summary API = A bridge to get live data from another system 📌 Tomorrow: JSON in Python (Handle API Data Like a Pro!) Follow me to master Python step-by-step 🚀 #Python #API #JSON #WebDevelopment #DataScience #Automation #Coding #Programming #LearnPython #MustaqeemSiddiqui

🚀 From Static Data to an Interactive Web App! 🚀 I recently decided to take my programming skills to the next level by heavily upgrading one of my data analysis projects. What started as a simple Python script to process the famous Superstore dataset is now a fully interactive, live web dashboard! 📊 Here is what I accomplished:  🧠 Learned how to structure data using Python and Pandas (specifically using .groupby() to manipulate data dynamically). 🛠️ Rebuilt static charts into interactive ones using Streamlit. ⚙️ Mastered version control by resolving conflicts via Git terminal commands. 🌐 Deployed the final application to the cloud so anyone in the world can interact with the data! I am incredibly proud of how much I learned about environments, dependencies, and taking a project from code to the cloud. 🔗 Try the live dashboard here: https://lnkd.in/dapceAK5  💻 Check out the code on my GitHub: https://lnkd.in/det-RQjc #Python #DataAnalysis #DataScience #Streamlit #Pandas #CodingJourney #BuildInPublic

🚀 Just shipped my second open-source Python library: jsondiffhtml Diff two JSON files and get a modern, self-contained HTML report — themeable, no runtime dependencies, no CDN calls. Why I built it: At work, I kept needing to compare large JSON payloads (API responses, configs, fixtures) and explain the differences to non-engineers. Existing tools either dumped raw text diffs or required spinning up a web service. I wanted a single HTML file I could attach to a Jira ticket or Slack message — open it, see what changed, done. What's in v0.1.0: 🔍 Native diff engine — zero runtime dependencies 📍 JSON path + line-number tracking back to the source files 🎨 Themeable (custom primary color, logo, title) 📦 Self-contained HTML output — no network calls, works offline 🏷️ Per-item ignore + comment annotations, persisted in localStorage 📊 Auto-splits huge diffs across multiple files 🖥️ CLI included: json-diff a.json b.json -o report.html Install: pip install jsondiffhtml Links: 📦 PyPI: https://lnkd.in/dqWcyW3N 💻 GitHub: https://lnkd.in/dWEcbEer Built with Python 3.9+, MIT licensed. Feedback, issues, and PRs are very welcome 🙏 #Python #OpenSource #JSON #DeveloperTools #PyPI

🎯 Uncover the magic of APIs with Python! Learn how to retrieve data from online sources and incorporate it into your projects seamlessly. APIs serve as a bridge between different software applications, empowering developers to access and manipulate external data efficiently. 🚀 For developers, mastering APIs opens up endless possibilities for creating dynamic and data-rich applications. It allows seamless integration of diverse functionalities and services, providing a competitive edge in the tech industry. 🔍 Here's a step-by-step breakdown: 1️⃣ Choose the API and obtain your unique access key. 2️⃣ Make a request to the API endpoint using Python's requests library. 3️⃣ Parse the JSON response to extract the data you need. 4️⃣ Implement the retrieved data in your project to enhance its functionality. 🖥️ Full code example: ```python import requests url = 'https://lnkd.in/gc8PxW6P' params = {'key': 'YOUR_API_KEY'} response = requests.get(url, params=params).json() # Process the response data accordingly ``` 💡 Pro tip: Always review the API documentation thoroughly to understand its endpoints and parameters effectively. ❌ Common mistake to avoid: Forgetting to handle potential errors in the API response, leading to unexpected bugs in your application. 🌟 Ready to dive into the world of APIs? What interesting projects are you planning to build using API integrations? 💬 🌐 View my full portfolio and more dev resources at tharindunipun.lk 🚀 #APIs #PythonDevelopment #CodingTips #WebDevelopment #DataIntegration #DeveloperCommunity #TechProjects #LearnToCode #CodingJourney

Supply Chains this, Supply Chains that, we all know the deal lately 🫠 I pulled together a new post on hardening the software supply chain with good practices, sprinkled in with some recent and cited write-ups (including public reporting and analysis around incidents like LiteLLM on PyPI, Shai-Hulud, etc etc) I also lined that up with what I’ve actually done locking down npm / JavaScript, Python, and Go in my own pipelines: hash-aware lockfiles (specifically with Python), attestations, cooldowns on brand-new releases, continuous dependency scanning across the full tree, why SBOMs matter once they’re saved as build artifacts and why one measure isn't a silver bullet, all the good stuff. If you’re tired of “pin the version and hope" (spoiler alert, it's not enough) then it’s a practical read. 😆 Read it here: https://lnkd.in/gXuB57Ah #SoftwareSupplyChain #DevSecOps #AppSec #npm #go #pip #uv #python #SCA

Every Python Developer Must Know These 30 Concepts 👇 1. Variables & Data Types (int, float, list, tuple, dict, set) 2. Mutable vs Immutable Objects 3. List Comprehensions 4. Generators & yield 5. Functions & Lambda Expressions 6. *args and **kwargs 7. Decorators 8. Closures in Python 9. Recursion 10. Exception Handling (try, except, finally, custom exceptions) 11. File Handling (read, write, context managers) 12. Context Managers (with statement) 13. Object Oriented Programming (Classes, Objects) 14. Inheritance & Multiple Inheritance 15. Magic Methods (dunder methods like __init__, __str__) 16. Dataclasses 17. Modules & Packages 18. Virtual Environments (venv) 19. Package Management (pip) 20. Iterators & Iterable Protocol 21. Multithreading vs Multiprocessing 22. Async Programming (asyncio, async/await) 23. GIL (Global Interpreter Lock) 24. Memory Management & Garbage Collection 25. Logging in Python 26. Testing (unittest, pytest) 27. Working with APIs (requests, JSON handling) 28. Serialization (pickle, JSON) 29. Pythonic Coding (PEP 8, idiomatic Python) 30. Performance Optimization (profiling, caching, time complexity)