DEV Community: Nandhini_T

Rivalry-Radar-World-Cup-passion-engine-with-Snowflake-Google-AI

Nandhini_T — Mon, 13 Jul 2026 03:54:23 +0000

This is a submission for Weekend Challenge: Passion Edition
(https://dev.to/challenges/weekend-2026-07-09)

What I Built

Rivalry Radar — a live "Heat Index" for World Cup rivalries. Fans drop 280-character Terrace Takes on any matchup (Brazil vs Argentina, England vs France, whatever's got you shouting at the TV), rate how much the moment hurt or thrilled them from 1–10, and the app does the rest:

Google AI (Gemini) scores every take's sentiment the instant it lands — positive, negative, mixed, or neutral — and separately writes a short "Hype Verdict" in the voice of a stadium announcer, based on the latest takes for a matchup.
That sentiment score feeds a Heat Index, computed and ranked in Snowflake with RANK() OVER (ORDER BY heat_index DESC), combining take volume, sentiment intensity, and self-rated passion into one live number per rivalry.
Two leaderboards: which rivalry is hottest right now, and which fanbase is bringing the most passion overall.

Demo

frontend/index.html is fully self-contained: opening it in a browser lets
anyone submit takes, watch the Heat Index flip digit-by-digit like an
airport departure board, and see the leaderboards re-rank in real time. It
ships with seed takes from eight classic rivalries so it's not empty on
first load.

Code

NandhuTee / Rivalry-Radar-World-Cup-passion-engine-with-Snowflake-Google-AI

🔥 Rivalry Radar — World Cup Passion Engine

Fans drop 280-character Terrace Takes on any World Cup matchup. Google AI (Gemini) scores the emotion behind every word and writes a stadium-announcer Hype Verdict; Snowflake stores every take and computes a live Heat Index that ranks exactly which rivalry is boiling hottest right now.

Built for the DEV Weekend Challenge: Passion Edition 🏆 Best Use of Google AI and Best Use of Snowflake

Why this exists

Passion is easy to feel and hard to measure. Every World Cup rivalry generates an ocean of unstructured text — chants, rants, one-line hot takes — that traditionally just... disappears into group chats. Rivalry Radar treats that text as data: Gemini reads the emotion in it the moment it's written, and Snowflake turns that into a live, rankable leaderboard.

How the work is split

Does what
Google AI (Gemini)	Scores each take's sentiment (positive/negative/mixed/neutral)

…

View on GitHub

rivalry-radar/
├── frontend/index.html # self-contained demo UI
├── backend/app.py # FastAPI service — real Gemini + Snowflake calls, with a demo-mode fallback
├── backend/requirements.txt
└── sql/schema.sql # Snowflake DDL and the Heat Index / leaderboard views

How I Built It

The build started from the Heat Index formula, since that's the number the
whole app orbits around: avg_passion * 0.5 + avg_sentiment_intensity * 3 + log2(take_count + 1) * 2. Volume matters (a rivalry with one take isn't
"hot"), but so does how emotionally loaded the language is — and that's
where Google AI comes in.

Gemini reads each take and classifies its sentiment:

prompt = (
    "Classify the overall emotional sentiment of this football fan "
    "comment as exactly one word — positive, negative, mixed, or "
    f"neutral. Reply with only that one word.\n\nComment: {text}"
)
response = client.models.generate_content(model="gemini-2.5-flash", contents=prompt)

That categorical result gets mapped to a numeric intensity — fury counts
exactly as much as joy, both are passion — so it drops straight into the
Heat Index math.

For the fun part, Gemini also turns the most recent takes for a rivalry into
a punchy one-liner:

prompt = (
    "You are a stadium hype announcer. In under 40 words, deliver a "
    f"punchy verdict on the {team_a_name} vs {team_b_name} World Cup "
    f"rivalry based on these fan takes: {joined}"
)

Snowflake handles the other half of the job: storing every take and
computing the leaderboards with real SQL — aggregation, a derived metric,
and a RANK() window function per rivalry and per fanbase. It's a clean
split: Gemini reads the emotion, Snowflake turns it into a ranking.

The backend is a small FastAPI service with two independent fallbacks,
keeping the whole flow explorable without handing out API keys for a
weekend project: no GEMINI_API_KEY → sentiment scoring falls back to a
keyword heuristic; no SNOWFLAKE_ACCOUNT → the whole API runs in demo mode
with seed data.

The frontend leaned into the subject: a split-flap "departure board" digit
animation for the Heat Index, a scrolling terrace-chant ticker, and a
submission form styled like a stadium chalkboard — an attempt to make the
data feel like the thing it's measuring.

Prize Categories

Submitting for Best Use of Google AI and Best Use of Snowflake — Gemini does the real intelligence work in this project: reading the emotion behind every fan take and writing the Hype Verdict. Snowflake plays an honest supporting role as the data warehouse, storing every take and doing the ranking analytics that turn Gemini's scores into a live leaderboard.

Thank you.

QuickPortfolio — Turning an Incomplete Portfolio into a Full-Stack Portfolio CMS

Nandhini_T — Sun, 07 Jun 2026 18:09:34 +0000

This is a submission for the GitHub Finish-Up-A-Thon Challenge

What I Built

QuickPortfolio is a full-stack portfolio CMS that allows developers to create and manage their own professional portfolio website through a custom admin dashboard.

The project started as a partially completed portfolio idea with basic authentication and minimal UI. During the Finish-Up-A-Thon Challenge, I transformed it into a much more complete and scalable application with:

JWT Authentication
Portfolio management
Project CRUD system
Experience CRUD system
Social links management
Public portfolio pages
PostgreSQL + Prisma integration
Responsive dashboard UI
Component-based React architecture

The project uses:

Frontend
React
Vite
Tailwind CSS
React Router DOM
Backend
Node.js
Express.js
Prisma ORM
PostgreSQL.

Demo

GitHub Repositories
Frontend Repo: [https://github.com/NandhuTee/quickportfolio-frontend]
Backend Repo: [https://github.com/NandhuTee/quickportfolio-backend]
Live Demo
Frontend: [https://quickportfolio-frontend-llno.vercel.app/]
Backend API: [https://quickportfolio-backend.onrender.com/]
Video Link :[ https://youtu.be/Bth22bpWtMs]

The Comeback Story

This project originally started as a simple portfolio management idea, but it was incomplete and missing many important features.

At first, the application only had:

Basic authentication
Minimal dashboard UI
Incomplete CRUD functionality
No proper portfolio architecture

During the challenge, I focused on finishing the missing pieces and restructuring the application into a real portfolio CMS.

Major improvements included:

Migrating to PostgreSQL with Prisma
Building reusable React components
Creating modular CRUD systems
Implementing public portfolio routing
Designing a cleaner dashboard experience
Adding experience and social link management
Improving overall frontend architecture

One of the biggest challenges was debugging database connection issues and synchronizing Prisma schema migrations with the deployed PostgreSQL database. Solving those issues helped me better understand backend architecture and production debugging.

This challenge pushed me to move beyond tutorial-style coding and focus on building a more complete full-stack product.

My Experience with GitHub Copilot

GitHub Copilot helped speed up repetitive development tasks and improved my workflow while building CRUD operations and React components.

I used Copilot to:

Generate boilerplate CRUD logic
Speed up React component creation
Improve Express route structure
Refactor repetitive frontend patterns
Debug API integration issues

The most valuable part was how it helped reduce repetitive coding so I could focus more on architecture and feature completion.

Planned future improvements:

Portfolio image uploads
PDF resume export
Blog CMS
Contact form system
Dark mode
Dashboard sidebar navigation
Portfolio analytics This challenge helped me finally turn an unfinished side project into something much closer to a real production-ready application.

_ Thanks You _

Building, Learning, and Growing as a Developer — Hello DEV!

Nandhini_T — Wed, 29 Oct 2025 04:42:51 +0000

Hey everyone! 👋
I’m Nandhini, a passionate frontend web developer from Tamil Nadu, India who loves turning ideas into interactive digital experiences.

💻 About Me

I started my coding journey with a deep curiosity about how websites work — and since then, I’ve been exploring the world of JavaScript, React.js, and Node.js.
Currently, I’m learning full-stack development using the MERN stack (MongoDB, Express.js, React.js, Node.js) and experimenting with tools like Tailwind CSS and TypeScript.

🎯 What I’m Currently Building

📚 Online Learning Platform — allows users to enroll, watch lectures, and get certified

⚙️ My Tech Stack

🌱 What I’m Learning

I’m focusing on improving my full-stack development skills and learning more about APIs, authentication, and responsive UI/UX design.

🤝 Let’s Connect

I’d love to collaborate on exciting web projects, internships, or open-source contributions!
Let’s share knowledge and grow together 🚀

Find me here:

🌍 Portfolio: portfolionandhini.netlify.app/#

💼 GitHub: https://github.com/nandhutee

💌 Email: rtnandhutee@gmail.com

nandhini_portfolio

Nandhini_T — Wed, 07 Feb 2024 06:07:15 +0000

Check out this Pen I made!