Introducing BricksFlowUI: Open-Source Python UI Framework for Databricks

Streamline your data collection with a universal Python scraper🚀. Writing custom scraping logic for each e-commerce site can be frustrating, time-consuming, and difficult to maintain. I have developed and released the "Ultimate" Universal Scraper on GitHub. This Python script is designed to reliably extract product data, including names, prices, images, and descriptions, from a variety of website structures with minimal configuration. Key benefits for developers and businesses include: - Robust & Reliable: Built to handle common scraping challenges and edge cases. - Highly Adaptable: Works effectively on many different e-commerce and product listing pages. - Time-Saving: Eliminates the need to reinvent the wheel for every new data extraction project. - Clean Output: Provides structured data ready for analysis in CSV or JSON formats. - Open Source: Available for viewing, forking, and contributing to its development. Whether your focus is on price comparison, market research, or data-driven insights, this tool can significantly enhance your efficiency. Check out the documentation and code on my official repository: 👉 https://lnkd.in/dzmprBhQ #Python #WebScraping #DataScience #DataAutomation #ECommerceData #GitHub #PythonDeveloper #OpenSourceContribution #DataEfficiency

#Wine_Quality_Prediction_using_Machine_Learning I built a full-stack ML project that predicts whether a wine is *Good* or *Bad* based on its chemical properties.  Tech Stack:  #Frontend: React (Vite)  #Backend: Node.js (Express)  #ML Model: Python (Flask + Scikit-learn) What I did:  # Trained a Random Forest model on wine dataset  # Converted raw input data into predictions using a Flask API  # Connected React → Node → Flask for real-time prediction  # Designed a UI form to input wine parameters and display results Features:  #Real-time prediction  #Clean UI with popup result (Good / Bad )  #Full API integration Challenges I faced: #Connecting multiple servers (React, Node, Flask) #Handling data format mismatches between frontend & backend #Fixing API errors like ECONNREFUSED Outcome: Successfully built an end-to-end ML web app where users can input wine features and instantly get quality prediction. #GitHub_Repository: https://lnkd.in/givVPBfv

“Day 7 – I built a dashboard using Python + Flask " Today I worked on: web_app.py : It will <<>> Convert DataFrame → HTML table Generate chart image Send both to frontend WHAT BUILT TODAY A real report system Exactly what SaaS tools do Facing challenge: Problems: “UnboundLocalError: cannot access local variable 'file'. Solved: Indent issues solved. TypeError: 'method' object is not subscriptable. Solved: Using square brackets [ ] on function (method). FileNotFoundError: static//chart.png Solved: put HTML and templates path into app initializer that easily get file location. import os BASE_DIR = os.path.dirname(os.path.abspath(__file__)) app = Flask( __name__, template_folder=os.path.join(BASE_DIR, '..', 'templates'), static_folder=os.path.join(BASE_DIR, '..', 'static') ) What I learned : Show full student table Show topper + insights Display chart inside browser I am documenting my journey to becoming a Data Scientist while building real-world projects. #DataScience #Python #SaaS #Automation #Analytics #BuildInPublic

🚀 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 deployed my first data app and honestly it felt like a whole journey 🚀 I built a Product Information Quality Dashboard using Python and Streamlit that analyzes how complete product data is and flags items that might confuse customers. Some of the skills I got to work with: 🐍 Python 🗄️ SQLAlchemy & SQLite for database management 📊 Pandas for data handling and exports ⚙️ Streamlit for building the interactive dashboard 🔧 Git & GitHub for version control ☁️ Deployed live on Streamlit Community Cloud Still learning and building every day — but seeing something you built actually live on the internet hits different 💪 Feedback always welcome! Check it out 👇 🔗 Live App: https://lnkd.in/gaiu6Src 💻 GitHub: https://lnkd.in/gm-TeghV #Python #DataScience #Streamlit #buildinpublic #DataAnalytics #LearningInPublic

I'm often asked how to handle edge cases when building data layers with MongoDB and Python. Simple CRUD is great, but real-world apps need robust query patterns and clean architecture. Working in VS Code on this project, I focused on layering logic. Instead of calling the database directly from the application layer, I used a modular service pattern (like user_service.py calling db_utils.py). A few key practices I implemented: ✅ Robust Error Handling: Ensuring a clean return for cases like invalid ObjectIds, which prevents app crashes. ✅ Modular Query Logic: Abstracting queries into specific, reusable functions (e.g., get_users_by_college) makes the main logic much easier to read and test. ✅ Automated Postman-Free Testing: In my terminal, you can see I'm using curl and echo to script a "Full CRUD Test Cycle." This is a fast, reproducible way to verify APIs during development. What's your go-to pattern for structuring database interactions in your applications? Do you stick with raw queries, ORMs, or custom data access objects? Let me know in the comments! GitHub link - > https://lnkd.in/dASzkj7T #mongodb #python #development #dataservices #vscode #backend #programming #softwareengineering

🚀 Day 4 of my 7 Days Django Challenge Today I built CurioLog — Daily Curiosity Journal 🧠✨ It’s a Django-based journaling web app where users can store and organize: ideas questions observations experiments learning notes Instead of making a very basic project, I wanted to build something that feels more practical and meaningful. ✅ Features: User authentication Add / edit / delete entries Categories and tags Search and filters Dashboard analytics Monthly and category charts CSV export Responsive UI 🛠️ Tech Stack: Python, Django, Bootstrap 5, Chart.js, Pandas, SQLite 📚 What I learned: Django authentication CRUD operations Model relationships Search & filtering Dashboard logic Data visualization Exporting data This project gave me a much better understanding of how real-world Django apps can be structured beyond just forms and models. 🔗 GitHub: https://lnkd.in/ggTm7Hyc #Django #Python #WebDevelopment #FullStackDevelopment #BackendDevelopment #StudentDeveloper #Projects #LearningInPublic #GitHub #SoftwareDevelopment

Ever tried explaining a complex database schema to a teammate or client using just code? It can be a headache. I found a way to automate the entire process. Using Django Extensions, you can generate a full visual diagram of your project’s models directly from your terminal. Here’s how to do it in 3 easy steps: 1- Install the tools: pip install django-extensions pygraphviz (Note: You can also use pydotplus if you prefer!) 2-Add to your apps: Don't forget to add 'django_extensions' to your INSTALLED_APPS in settings.py. 3- Run the command : "python manage.py graph_models -a -o my_project_schema.png" Boom! You have a high-quality visual representation of your database architecture ready for documentation, presentations, or just to admire your hard work. It’s a lifesaver for onboarding new developers like me or debugging complex relationships. #Django #Python #WebDevelopment #Backend #CodingTips #SoftwareArchitecture #DatabaseDesign

Built a Customer Churn Prediction Dashboard from scratch. FastAPI backend · SQLite · Vanilla JS · No frameworks. Features: → RFM-based churn risk scoring → High / Medium / Low / Safe segmentation → Suggested actions per customer → Bulk actions, CSV import/export, Action log No tutorials. Just built it. https://lnkd.in/dpUCQq3j #Python #FastAPI #BuildInPublic #OpenToWork

I built the same chatbot 4 times in Python. Same FastAPI backend. Same SQLite. Same UI spec. 4 different Python UI frameworks. Here's the honest tier list PANEL - S tier The grown-up choice. Bootstrap grid, true reactivity, custom CSS without hacks. Built on Bokeh, born in HoloViz. Pick this when the app has to live in production. PyShiny - S tier 20 years of Shiny wisdom, finally in Python. Built on Starlette + asyncio. The reactive graph means only what depends on a changed input recomputes. Stupid fast. Streamlit - A tier Still the POC speedrun champ. Notebook → web in an hour. But every interaction reruns the entire script; that snappy 5-widget app becomes painful at 50. Dash - A tier Plotly + Flask + React under the hood. Massive community, full HTML control, enterprise-ready. The id-based callback system is loved and hated equally. My picks: ⭐️ Quick POC or ML demo: Streamlit ⭐️ Production reactive app: PyShiny ⭐️ Heavy analytics dashboard: Dash ⭐️ Multi-page bootstrap UI: Panel Python UI is no longer "just for ML demos". It's becoming a real production option for scalable UI, pick the framework that matches the lifecycle of your app, not the hype. Which one are you using? 👇 #Python #DataScience #FastAPI #Streamlit #Panel #Dash #Shiny #MachineLearning #SoftwareEngineering Note: Built with Lovable! I used this AI platform to rapidly prototype, iterate, and deploy this site in just a few hours. The interactive publishing feature and seamless GitHub integration turned weeks of work into a few hours of productive 'vibe coding'. Github: https://lnkd.in/dBNrPFfe Lovable App for more details: https://lnkd.in/dST5da4S