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Pratyush Panda
Pratyush Panda

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SlothFridge: The Couch-Potato Culinary Copilot (TabPFN + Gemma 2 + ElevenLabs)

#devchallenge #hf26challenge

Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady Submission 🦥

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady

What I Built

It is 10:45 PM. You are lying completely flat on your bed, scrolling on your phone, starving.

You drag yourself to the kitchen, pull open the fridge door, and stare into the void: two cold eggs, a dying heel of stale bread, half a bowl of curd (dahi), and a stray slice of cheese.

The traditional internet recipe requires chopping three shallots, preheating an oven, using four prep bowls, and dirtying a cutting board. Opening a food delivery app means spending â‚ą250 and waiting 45 minutes while fighting decision fatigue. Most people just give up and go to sleep hungry.

SlothFridge is the couch-potato culinary copilot built to honor International Sloth Day:

  1. The Couch Ingestion Check: Tap whatever random items are dying in your fridge without leaving bed or typing out complex measurements.
  2. Tabular Fatigue & Standing Regression (TabPFN): Evaluates kitchen variables against 130 empirical lazy-cooking observations to predict your active standing minutes, classify cleanup fatigue tiers (Zero Effort (Mug), Low Effort (1 Pan), High Effort (Avoid)), and calculate your sink debt penalty.
  3. Lazy Recipe Synthesis (Gemma 2 via Groq): gemma2-9b-it crafts an unapologetically low-effort 3-step meal designed around one non-negotiable rule: zero cutting boards, zero knives, and you eat directly from the vessel so you never look at the sink.
  4. Sleepy Audio Walkthrough (ElevenLabs): Streams hands-free audio instructions using eleven_turbo_v2_5 in a slow, deadpan voice, guiding you through low-heat cooking so you can retreat back to bed while it cooks.

Demo

  • Local / Self-Hosted Backend: FastAPI + Uvicorn service running TabPFN CPU inference and ElevenLabs streaming.

The interface is built with a mid-century warm stationery aesthetic (#EFE9DD ground, #141C2B carbon ink, and Newsreader + Courier Prime typography), featuring self-drawing effort curves and zero-border-radius precision.

Code

The entire open-source repository—including the reference dataset, TabPFN dual inference engine, Gemma 2 prompt templates, and multi-page editorial frontend—is public on GitHub:

GitHub logo Pratyush-Panda-2006 / SlothFridge

The couch-potato culinary copilot. Predicts cleanup fatigue and active standing minutes via TabPFN, crafts ultra-lazy 1-pan/mug recipes with Gemma 2, and streams deadpan hands-free cooking audio with ElevenLabs for Hacktoberfest Week 3.

SlothFridge: The Couch Potato Culinary Copilot 🦥🍳

Built for the Hacktoberfest Open-Source AI Challenge Week 3
Theme: Slow and Steady / International Sloth Day

SlothFridge takes expiring groceries entered by exhausted users lying on their beds and outputs an uncompromisingly lazy, 1-pan or microwave recipe. It uses TabPFN to predict cleanup fatigue and continuous standing minutes, Gemma 2 (via Groq) to synthesize lazy recipes, and ElevenLabs to stream slow, deadpan audio walkthroughs.

🏛 Architecture & Open Stack

  1. TabPFN Fatigue & Standing Time Engine (services/tabpfn_service.py):

    • Trained on data/sloth_meals.csv (130 realistic kitchen observations).
    • Predicts active standing minutes (continuous regression) and cleanup fatigue tier (Zero Effort (Mug), Low Effort (1 Pan), High Effort (Avoid)).
    • Computes utensil penalty and inverted Sloth Approval Score (0–100, where higher is lazier).

  2. Gemma 2 Couch Potato Chef (services/gemma_service.py):

    • Synthesizes lazy recipes using gemma2-9b-it on Groq with strict JSON…
View on GitHub

How I Built It

SlothFridge connects tabular foundation inference with open-weight language synthesis and streaming speech:

1. TabPFN Fatigue & Standing Time Engine (services/tabpfn_service.py)

Rather than relying on vague heuristics or heavyweight neural training loops, SlothFridge utilizes Prior Labs' TabPFN fitted on in-memory tabular data (data/sloth_meals.csv):

  • Continuous Regression: Estimates exact standing time down to the decimal (e.g., 3.8 minutes).
  • Multi-Class Probabilities: Computes calibrated posterior distributions across Zero Effort (Mug), Low Effort (1 Pan), and High Effort (Avoid).
  • Cognitive Anomaly Detection: Flags an alert if a user inputs complex ingredients requiring extensive knife work while selecting "Couch Comatose" commitment.

2. Gemma 2 Couch Potato Chef (services/gemma_service.py)

Using Google's open-weight gemma2-9b-it served through Groq, the engine acts as an exhausted, realistic chef. It strips out all culinary pretense:

  • Enforces hand-tearing over knife-chopping.
  • Mandates eating straight from the skillet or mug with a fork.
  • Generates deadpan, 3-step instructions tailored to your available heat source.

3. ElevenLabs Sleepy Voice Narration (services/voice_service.py)

Using eleven_turbo_v2_5 with the calm, deadpan "George" voice profile, the app synthesizes low-latency audio walkthroughs. Users start the audio, dump ingredients in, set the heat to low, and return to bed until the timer sounds—touching neither a cutting board nor their phone screen with messy hands.

Why Does Open Innovation Matter?

  1. Zero-Toll Everyday Utilities: A consumer app for lazy home cooking cannot survive behind expensive proprietary API paywalls or â‚ą800/month subscription gates. Open models like Gemma 2 and CPU-efficient tabular models like TabPFN run at near-zero inference cost.
  2. Local Sovereignty: Your kitchen habits, pantry ingredients, and late-night routines stay private.
  3. In-Context Tabular Intelligence: TabPFN's transformer-based approach allows developers to evaluate small, curated tabular datasets without managing hyperparameter tuning pipelines or cloud database clusters.

The Lazy Payoff

To test the real-world payoff, I simulated a late-night cooking scenario using a typical hostel desk stash: 2 eggs, half an onion, two slices of stale bread, and a slice of processed cheese.

  • The Traditional Recipe: 22 minutes of standing, 1 chef's knife, 1 cutting board, 1 whisk, 1 prep bowl, 1 frying pan, and 1 plate. Total sink debt: 7 items.
  • The SlothFridge Payoff:
    • TabPFN Predicted Standing Time: 4.2 minutes.
    • Cleanup Fatigue Tier: Low Effort (1 Pan) (94.2% posterior confidence).
    • Gemma 2 Generated Recipe: "The 1-Skillet Bread-Egg Slump" — Tear the stale bread by hand into a cold pan, crack both eggs over the bread, crush the onion half into rough chunks with your palm, drop the cheese on top, cover with a lid, and walk back to bed for 5 minutes. Eat directly from the pan with a single fork.
    • Actual Utensils Washed: 1 skillet, 1 fork.
    • Effort Saved: ~18 minutes of standing avoided and an 85% reduction in dirty dishes.

Prize Categories

  • Best Use of TabPFN
  • Best Use of Gemma
  • Best Use of ElevenLabs
  • Best Use of Vercel

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