Built a Weather App with Python and Streamlit

💡 Project Overview: •The app allows users to:View multipletravel destinations with images •Navigate between tabs (Home, Settings, About) •Click Info buttons to view travel image sets dynamically •Select an age range from the sidebar and view matching users in a clean table format 🎯 Learning Outcomes: •Learned how to design interactive dashboards using Streamlit. •Understood state management and conditional rendering in web apps. •Improved understanding of layout control (columns, tabs, sidebars). •Practiced integrating logic and visuals smoothly in a single Python app. 🔗 Summary: This project was a great hands-on experience to understand how Streamlit can quickly transform Python scripts into beautiful, interactive web applications — ideal for data visualization, filtering, and dynamic content display. Linked:https://lnkd.in/g_7-j4tY A big thank you to Chaitanya Madakasira sir for guiding me through this learning journey. #Python #Streamlit #WebDevelopment #MiniProject #DataApp #LearningByDoing #TravelExplorer #MachineLearningJourney #ProjectShowcase

“O𝐨p𝐬… M𝐲 𝐆P𝐀?” — 𝐍o𝐭 𝐚n𝐲m𝐨r𝐞 😎 After too many calculator tabs and spreadsheet formulas, I finally built something that makes GPA tracking actually fun and effortless. 🎯 Meet Oops My GPA — a clean, interactive web app made with Streamlit to calculate and visualize your semester GPA & CGPA like never before. 💡 What makes it cool?  🧮 𝗦𝗺𝗮𝗿𝘁 𝗚𝗣𝗔 & 𝗖𝗚𝗣𝗔 𝗺𝗼𝗱𝗲𝘀 (𝗻𝗼 𝗺𝗮𝗻𝘂𝗮𝗹 𝗳𝗼𝗿𝗺𝘂𝗹𝗮𝘀 𝗮𝗴𝗮𝗶𝗻!)  📊 𝗔𝘂𝘁𝗼-𝘂𝗽𝗱𝗮𝘁𝗲𝘀 + 𝗹𝗶𝘃𝗲 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗴𝗿𝗮𝗽𝗵  🎓 𝗣𝗿𝗲-𝗹𝗼𝗮𝗱𝗲𝗱 𝗣𝗨 𝗕𝗦-𝗜𝗧 𝗰𝗿𝗲𝗱𝗶𝘁𝘀 — 𝗼𝗿 𝗮𝗱𝗱 𝘆𝗼𝘂𝗿 𝗼𝘄𝗻  🧷 𝗠𝗶𝗻𝗶𝗺𝗮𝗹 𝗨𝗜 + 𝗼𝗻𝗲-𝗰𝗹𝗶𝗰𝗸 𝗿𝗲𝘀𝗲𝘁 𝗮𝗻𝗱 𝘀𝗮𝘃𝗲  ⚡ 𝗙𝗮𝘀𝘁, 𝘀𝗲𝘀𝘀𝗶𝗼𝗻-𝗯𝗮𝘀𝗲𝗱 𝗱𝗲𝘀𝗶𝗴𝗻 (𝗻𝗼 𝗱𝗮𝘁𝗮 𝗹𝗼𝘀𝘀 𝘄𝗵𝗶𝗹𝗲 𝘂𝘀𝗶𝗻𝗴) 💻 Built With: Python · Streamlit · Pandas · Matplotlib · Custom CSS 𝗧𝗿𝘆 𝗶𝘁 𝗹𝗶𝘃𝗲 👉 https://lnkd.in/dduMHhvz 𝐂𝐨𝐝𝐞 𝐢𝐭 𝐲𝐨𝐮𝐫𝐬𝐞𝐥𝐟 👉 https://lnkd.in/d3GFtwUU ✨ This project started as a simple idea — “make GPA calculation less annoying” — and turned into one of my cleanest Streamlit builds so far. Would love your feedback or thoughts — especially if you’re also working on student-centric tools or love Streamlit apps! #Python #Streamlit #OpenSource #StudentProjects #EdTech #GPAcalculator #BSIT #DataApps #OpenSource #StudentLife #BuildInPublic #AIinEducation #StudentSuccess #GPAcalculator #DataVisualization #WebApp #Coding

🤖 My First Ever Machine Learning Web Project is Live! 🚀 I’m thrilled to share my Face Recognition-Based Web Application, now live at: 🔗 https://lnkd.in/gJ-RNd29 This project marks a huge milestone for me — my first complete Machine Learning + Web Integration project. It can recognize faces from images, uploaded videos, or even a live webcam feed directly from your browser! 🧠 How it works: I’ve trained the system with several known faces (stored with their names). When you upload an image/video or use the live webcam, the app detects and identifies faces that match the pretrained data. You can even add new faces through the “Manage Faces” feature to make the system learn and recognize new users in real time. 💻 Tech Stack: Frontend: HTML5, CSS3, JavaScript (Responsive for Desktop & Mobile) Backend: Flask (Python) Libraries: OpenCV, dlib, face_recognition, Pillow, NumPy Deployment: PythonAnywhere This project taught me a lot about integrating Machine Learning and Computer Vision into a full-stack web app — from face detection and training to real-time recognition through a simple and clean interface. 🔗 GitHub Repository: https://lnkd.in/grUPvJTR #MachineLearning #ComputerVision #Python #Flask #FaceRecognition #AI #DeepLearning #OpenCV #WebDevelopment #FullStack #LearningJourney #ResponsiveDesign

🚀 Day 4 of my 100 Days AI & Data Engineering Challenge! ☕ Today, I built a Streamlit app for a coffee shop and explored the power of layout and interactivity features in Streamlit. On this journey, I learned: ✅ How to use Sidebars for easy navigation ✅ Creating Columns and using with blocks for clean layouts ✅ Using Expanders and Tabs to organize content ✅ Encapsulating layouts in functions for reusable components ✅ Choosing the right layout elements to enhance user experience The result is a fully interactive coffee shop app that provides: 1.About Us section 2.FAQs section 3.Customer Feedback form This project helped me understand how thoughtful layouts can drastically improve user experience in Python web apps. 💻 Check out the project on GitHub: https://lnkd.in/gpzhdXvj Link of Deployed app in Streamlit Community : https://lnkd.in/gy4U27Ay #100DaysOfCode #AI #DataEngineering #Python #Streamlit #WebAppDevelopment #LearningJourney

🚀 Version 1.3 — Multi-Model YOLO Video Detection App Update! 🎥🧠 Excited to announce v1.3 of my FastAPI + React YOLO application, with two powerful new models added! ✅ Backend: FastAPI + Python + PyTorch ✅ Frontend: React + Tailwind + NGINX ✅ New in v1.3: 🟢 Instance Segmentation & Masking using YOLOv11 🤸 Workout Pose Analysis — upload a reference workout video and your actual workout video, and get a posture similarity score along with personalized suggestions (powered by Mediapipe) ✅ Existing Models: 🪖 YOLOv11 Helmet Detection 🔥 YOLOv11 Fire & Smoke Detection ✅ Deployment: Docker + Docker Compose ✅ One-command launch: docker-compose up --build ⭐ v1.3 Highlights: Scalable, modular containerized setup Multi-model AI video processing Outputs processed videos + analytics Now supporting both industrial safety and fitness/pose analysis 🛠 Tech Stack: Python • FastAPI • PyTorch React • Tailwind • NGINX Docker • Docker Compose YOLOv11 • Mediapipe • OpenCV 🔥 v1.3 makes the app even more versatile, combining safety monitoring and workout analytics in one containerized platform. More AI models coming soon! 😉 #AI #ComputerVision #SafetyAI #FastAPI #ReactJS #Docker #YOLO #DeepLearning #MLOps #DevOps #PyTorch #OpenCV #FullStackDevelopment #AIProjects #HelmetDetection #FireAndSmokeDetection #YOLOv11 #InstanceSegmentation #WorkoutAnalysis

🌤️ 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

🚀 Project Launch: Game of Thrones Personality Matcher 🧠⚔️ I recently built an interactive Streamlit web app that recommends your Game of Thrones character closest match in GOT Universe — based on character similarity and data-driven analysis 🔍 💡 Project Overview This app lets users select any GOT character and instantly find their personality “twin” from the Seven Kingdoms 👑 using distance-based similarity logic. It integrates live Thrones API data, computes character proximity using NumPy, and displays the results with character images fetched dynamically. ⚙️ Tech Stack 🔹 Python 🔹 Streamlit (for UI) 🔹 NumPy (for similarity computation) 🔹 Requests (for API calls) 🔹 Pickle (for data storage) 🌟 Key Features 🎭 Select a character from GOT and instantly get your closest match in GOT Universe. 📊 Similarity calculation using Euclidean distance. 🖼️ Dynamic character images fetched via the Thrones API. 🧹 Cleaned and standardized character names for better accuracy. 💻 Interactive dual-column layout built using Streamlit for a sleek user experience. 🎯 Outcome ✅ Successfully integrated data preprocessing, API fetching, and machine learning logic in a single web interface. ✅ Strengthened understanding of recommendation systems, frontend-backend integration, and interactive data apps. ✅ A fun and data-driven way to explore GOT personalities 🔥 #Python #Streamlit #MachineLearning #DataScience #APIIntegration #RecommendationSystem #WebApp #GameOfThrones #AIProjects #TechInnovation Github Link: https://lnkd.in/guberv2h Deployed App Link: https://lnkd.in/gWDmTePq

🎉 Project Launch: PDF Chatbot using Flask & LM Studio I’m super excited to share my latest project — a PDF Chatbot built using Flask (Python) and LM Studio! 🚀 This web app allows users to: 📄 Upload any PDF file 💬 Ask questions about its content 🤖 Get instant AI-generated answers — all offline, without cloud APIs! ⸻ 🧠 Tech Stack: • Python (Flask) • PyPDF2 for text extraction • Requests for API communication • HTML, CSS, JS for frontend • LM Studio for local LLM inference ⸻ ⚙️ Features: ✅ Reads & processes PDF documents ✅ Integrates with local AI (LM Studio) ✅ Simple and clean web UI ✅ Runs completely offline — ensuring privacy #Python #Flask #ArtificialIntelligence #MachineLearning #LMStudio #AIProjects #PDFChatbot #WebDevelopment #OpenSource

I came last place for my Fantasy Basketball League last year which inspired me to make a tool that can hopefully help me do better this year! Check out my fun side project I've been working on for the past few weeks: http://bit.ly/4qERPK7 How it works: 1️⃣ Connect your Sleeper account (username-based, no auth required) 2️⃣ Select your fantasy basketball league 3️⃣ Chat with AI about roster help with context specific to your league, waiver wire pickups, trades and get instant analysis with favorability scores (0-100) and trade simulations. The tech behind it: → Backend: FastAPI (async Python), WebSocket for real-time updates → Frontend: React + Vite with Tailwind CSS → Data: PostgreSQL + Redis distributed caching → AI: OpenAI LLMs with AutoGen multi-agent framework → APIs: Sleeper, NBA Stats, ESPN (web scraping) → Deployment: Railway + Vercel with CI/CD I learned a ton about parallel processing with asyncio (cut analysis time from 30s to 3s!), caching strategies, and WebSocket architecture. Shoutout Claude for being the single engineer on this proj and the UI could def use some work 😂 #AI #ProductDevelopment #FantasyBasketball #LLM #Python #React #SideProject