Last Part of my Weather App Building a Feature-Rich Weather Dashboard with Python & Flask 🚀 I’m excited to share my latest project: a Dynamic Weather Dashboard. This isn't just a basic weather app; it’s a full-stack tool that provides deep insights into the environment. Key Features: Dynamic Backgrounds: The UI intelligently changes based on weather conditions (Rainy, Sunny, Cloudy, etc.) using CSS-to-JS binding. Air Quality Index (AQI) 🍃: Integrated a pollution tracking system to show real-time air quality levels. Comprehensive Forecasts: Includes a detailed 24-hour hourly ribbon and a 5-day outlook. Smart Recent Searches: Uses Browser LocalStorage to remember your previous searches for quick access. Robust Error Handling: A custom-designed, dark-themed "City Not Found" experience that keeps users engaged even when searches fail. Technical Highlights: Backend: Python & Flask Production Server: Waitress WSGI Frontend: HTML5, CSS3, JavaScript Data Source: OpenWeatherMap API This project taught me a lot about API integration, asynchronous data flow, and creating a responsive, "living" UI. #Python #Flask #WebDev #OpenWeatherMap #SoftwareEngineering #ProjectShowcase

🚀 Just built a full-stack To-Do List web application with Flask! ✅ Features: User authentication & session management Complete CRUD operations (Create, Read, Update, Delete) Task filtering (All/Active/Completed) Responsive design for all devices Real-time task statistics dashboard 💻 Tech Stack: Flask backend SQLite database HTML/CSS/JavaScript frontend SQLAlchemy ORM Flask-Login for authentication This project demonstrates core backend development skills and clean architecture. Perfect for beginners looking to understand full-stack development! #Flask #Python #WebDevelopment #CRUD #Backend #Programming #FullStack #Projects #ToDoApp

🚀 Excited to share my latest project: A Full-Stack Inventory Management System! Managing inventory isn’t just about storing data—it’s about predicting demand, preventing stockouts, and scaling with confidence. That’s exactly what I focused on while building this system from scratch using Python & Flask. This isn’t a typical CRUD app. I implemented a custom demand simulation engine to stress-test inventory behavior under real-world scenarios like demand spikes and stock fluctuations. 🔧 Key Technical Highlights 🔹 Backend: Python (Flask), SQLite, SQLAlchemy 🔹 Frontend: Jinja2, Bootstrap 5, Vanilla JavaScript 🔹 Architecture: Modular Flask Blueprints for clean scalability 🔹 Simulation Engine: Real-time demand forecasting & stock movement analytics 📊 The system provides live inventory insights, helping simulate decision-making before real losses occur. 🎥 Check out the demo video below to see real-time analytics in action! #Python #Flask #FullStackDevelopment #InventoryManagement #SoftwareEngineering #WebDevelopment #Backend #Projects

Building a Financial React Frontend for a Render-Deployed FastAPI (yfinance) API n my last post I showed you how to make a data API backend using Python FastAPI, deploy it to Render, and how to make API calls. This follow-up demonstrates how to build a small React frontend that consumes our FastAPI backend deployed on Render. I demonstrate how to make one API call, process the returned JSON, and present the result as a clean “Financials” table view. Backend (Render): https://lnkd.in/eXh8kHhJ Endpoint used: GET https://lnkd.in/e3sTemrr What the frontend demonstrates (in a single component): Environment-based API configuration via .env (VITE_API_BASE_URL=...) A tiny apiGet() helper for URL composition + consistent error handling (FastAPI detail) Data shaping for presentation: extract fiscal-date columns, sort newest-to-oldest, render metrics as rows Live example of the same approach in production: Equity Explorer https://lnkd.in/dd2bwgz6 #FullStack #SoftwareEngineering #FrontendDevelopment #BackendDevelopment #React #TypeScript #FastAPI #Python #RESTAPI #CloudDeployment #SystemDesign #DataEngineering #FinTech #QuantDeveloper

Bridging Flutter & Flask: Building a Full-Stack Regex Engine I’m thrilled to share my latest project—a Regex Matching Web App that combines a sleek Flutter UI with a powerful Flask backend. Inspired by Regex101, this project was a deep dive into building a seamless communication bridge between a cross-platform frontend and Python’s robust logic. 🛠️ The Tech Stack: Frontend (Flutter): Crafted a responsive, intuitive UI to handle user inputs and display matches dynamically. Backend (Flask): Built a RESTful logic layer to process regular expressions using Python’s re module. The Bridge: Managed the data flow between the two to ensure real-time feedback and instant pattern validation. Key Features: 🔹 Dynamic match highlighting 🔹 Robust error handling for invalid regex patterns 🔹 Clean, modular architecture Special thanks to Innomatics Research Labs for the guidance and the opportunity to sharpen my full-stack skills! github link -> https://lnkd.in/gQ7fpggM #FullStack #Flutter #Flask #Python #WebDevelopment #Regex #InnomaticsResearchLabs

I Built a Search Engine from Scratch 🔍 I wanted to know exactly how Google handles billions of pages. So, I built a custom search engine from scratch using Python. What’s inside: ✅ Custom Crawler: Asynchronous web navigation and data extraction. ✅ Inverted Index: Optimized data structures for sub-second document retrieval. ✅ Query Engine: Smart ranking logic to deliver relevant results. ✅ Interface: A clean, functional interface for real-time searching. The Workflow: ✅Performance: Managed dependencies with uv for a lightning-fast dev environment. ✅Reliability: Integrated GitHub Actions for automated CI and code formatting. Key takeaway: Building the fundamentals manually is the best way to understand complex systems. Tools used: Python, BeautifulSoup, uv, Flask, GitHub Actions. Please do check it out and let me know what you think! Git Repo: https://lnkd.in/ggERQ95Z #SearchEngine #PythonDeveloper #SoftwareDeveloper

🚀 Excited to share my latest project: a URL Shortener Web Application! 🔗 What does it do? - Shortens long URLs into simple, shareable links - Stores both original and shortened URLs in a database - Provides a history page to view all saved links - Built with a clean frontend using HTML, CSS, and Bootstrap ⚙️ Tech Stack: - Backend: Flask (Python) - Database: SQLAlchemy with SQLite - Frontend: HTML, CSS, Bootstrap 💡 Key Learnings: - Building and connecting Flask routes with a database - Using SQLAlchemy ORM for managing data - Validating user input with Python libraries - Creating a user‑friendly interface with Bootstrap A big thank you to Innomatics Research Labs and Mayank Ghai sir for guiding me through this journey! 🙌 #Python #Flask #SQLAlchemy #Bootstrap #WebDevelopment #InnomaticsResearchLabs

🚀 Built a fully local PDF-chat RAG system using Ollama + LangChain Over the last few weeks, I’ve been working on a local-first RAG (Retrieval Augmented Generation) project that lets you chat with your PDFs without sending a single byte to the cloud. The goal was simple: understand how real RAG systems are built end-to-end, not just follow a notebook tutorial. This project turns PDFs into a searchable knowledge base and lets you ask grounded questions with source references, all running on your own machine. 🔹 What this system does 1️⃣ Upload one or multiple PDFs The pipeline: Parses and chunks PDFs Generates embeddings locally Stores vectors in ChromaDB 2️⃣ Query with RAG Questions are answered strictly from retrieved document chunks Responses include source citations Multi-query retrieval improves answer quality 3️⃣ Multiple ways to use it Next.js web app for a clean, modern chat experience Streamlit interface for quick testing and demos Jupyter notebooks for experimentation and learning 4️⃣ API-first design FastAPI backend with clean REST endpoints Upload PDFs, query documents, manage collections programmatically 🧠 Why I built this I wanted to go beyond “hello world” RAG demos and actually understand: How chunking and retrieval affect answer quality How embeddings, vector stores, and LLMs work together How to expose RAG systems as real products with APIs and UIs Everything runs 100% locally using Ollama, so privacy is preserved and the system works offline. 🛠️ Tech stack Python · LangChain · ChromaDB Ollama (LLMs + embeddings) FastAPI backend Next.js & Streamlit frontends 📁 Full project, setup steps, and code are on 👉 GitHub: https://lnkd.in/g5atC2kE I’m still iterating on this, so feedback, ideas, and PRs are very welcome. #RAG #GenAI #LangChain #Ollama #BuildInPublic #AIEngineering #LLM #OpenSource #FastAPI #NextJS #ScaleDown #Education #BuildInPublic

I recently came across a post from a user on Reddit who was frustrated with tracking ski trails and lift conditions. They wanted a way to pull real-time data from Stratton Mountain’s report (grooming status and lift/trail openings) without manually clicking through a website everyday. I took the challenge and built a web scraping solution using Python. Technical Hurdle: The project was more than just a simple scrape. The resort’s website uses dynamic elements, meaning the desired data was only accessible after certain JavaScript functions were executed. A standard "static" scrape would return empty results. Solution: To deliver the functionality the user needed, I implemented a headless browser session using Selenium. This allowed the script to: - Simulate a real user visit to trigger the necessary JavaScript - "Wait" for the dynamic trail elements to render completely before extraction - Capture and transform raw web data into a clean, actionable format Result: By automating this workflow, I provided the user with a reliable way to access mountain data. Instead of being locked behind a browser, the information is now structured and ready for any future needs from custom dashboards to automated notifications. Check out the code on GitHub: https://lnkd.in/gHQ3JtAd What’s one manual task in your hobby or business that you wish you could automate?

𝗧𝗵𝗲 𝟮𝟬𝟮𝟲 𝗣𝗼𝘄𝗲𝗿 𝗖𝗼𝘂𝗽𝗹𝗲: 𝗣𝘆𝘁𝗵𝗼𝗻 & 𝗡𝗲𝘅𝘁.𝗷𝘀. In the world of full-stack development, everyone has a favorite combo. At CodBeyón, we have placed our bets on this specific architecture: 🧠 𝗧𝗵𝗲 𝗕𝗿𝗮𝗶𝗻 (𝗕𝗮𝗰𝗸𝗲𝗻𝗱): 𝗣𝘆𝘁𝗵𝗼𝗻. Whether it's Django or FastAPI, Python allows us to integrate AI Agents and complex data processing natively. ⚡ 𝗧𝗵𝗲 𝗙𝗮𝗰𝗲 (𝗙𝗿𝗼𝗻𝘁𝗲𝗻𝗱): 𝗡𝗲𝘅𝘁.𝗷𝘀. It gives the user an instant, app-like experience with best-in-class SEO. Why this matters for your business: You get the raw power of AI on the back, and the speed of a modern web app on the front. 𝗦𝘁𝗼𝗽 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗺𝗼𝗻𝗼𝗹𝗶𝘁𝗵𝗶𝗰 𝗮𝗽𝗽𝘀 𝘁𝗵𝗮𝘁 𝗰𝗮𝗻'𝘁 𝘀𝗰𝗮𝗹𝗲. 𝗕𝘂𝗶𝗹𝗱 𝗱𝗲𝗰𝗼𝘂𝗽𝗹𝗲𝗱 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝘁𝗵𝗮𝘁 𝗮𝗿𝗲 𝗿𝗲𝗮𝗱𝘆 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗔𝗜 𝗲𝗿𝗮. #Python #NextJS #FullStack #TechStack #WebArchitecture #CodBeyon