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

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.

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

• Google AI (Gemini) - Scores each take's sentiment (positive/negative/mixed/neutral)
• Snowflake - Stores every take and computes the Heat Index / leaderboard views

Repository structure

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.