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AllerGuard AI: Protecting My Friends' Severe Food Allergies Offline with Gemma 2, TabPFN & Voice Alerts

#devchallenge #weekendchallenge #hf26challenge #opensource

Hacktoberfest Weekend Challenge: Build for a Friend Submission 🤝

This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend

🌐 Live Interactive Demo: https://allerguard-ai-5lim.onrender.com

👉 GitHub Repository: https://github.com/SixFiveMil/allerguard-ai

What I Built

Who It's For: The Dinner Party Dilemma

I live with life-threatening anaphylactic allergies to Tree Nuts, Peanuts, Coconut, and Sesame. My friend Maya has severe Celiac Disease, and another close friend, Alex, has a debilitating Dairy and Egg allergy.

Whenever our friend group hosts a dinner party, plans a camping trip, or shops for a shared holiday meal, an innocent act of hospitality turns into high-stakes anxiety. Nobody wants to send their best friend to the emergency room with an EpiPen, but navigating the modern grocery aisle without a degree in biochemistry is terrifying:

  • Sesame hides in plain sight under names like tahini, benne seeds, halvah, and gomasio.
  • Peanuts hide under arachis oil and generic "cold-pressed nut/seed blends".
  • Tree nuts hide as marzipan, gianduja, and praline.
  • Coconut is everywhere in modern plant-based foods—creaming dairy-free yogurts, texturizing vegan cheeses, and disguised as MCT oil or copra.
  • Gluten lurks in malt extract, hydrolyzed wheat protein, and modified food starch.

The questions that ruin shopping trips for friends aren't printed on standard nutrition labels:

  1. "Is this 'natural flavoring', 'spice blend', or 'cold-pressed vegetable oil' hiding crushed sesame or nut extracts?"
  2. "Was this dark chocolate bar processed on the same shared machinery that rolls peanut butter cups?"
  3. "Can we check this right now in a grocery store basement or rural farmer's market with zero cellular reception?"

Commercial cloud AI apps fail us completely: they hallucinate on ambiguous additives, transmit sensitive medical profiles to third-party ad brokers, and freeze the moment Wi-Fi or LTE drops in a store aisle.

So I built AllerGuard AI—designed specifically so my friends, family, and anyone hosting loved ones with dietary restrictions can shop and cook with confidence.

What My Friends Said When They Tested It

I loaded AllerGuard on a laptop, flipped the Wi-Fi completely off into airplane mode, and handed a basket of challenging specialty grocery items to my friends:

"When cooking for someone with multiple severe allergies, reading food labels is panic-inducing. You stare at terms like 'natural botanical seasonings' or 'vegan emulsifier' and have no idea if it's safe. Having AllerGuard run locally in two seconds with Wi-Fi off, switch effortlessly between Joshua's nut allergy and Maya's Celiac profile, flag hidden tahini, and read the warning aloud through the laptop speakers takes all the terror out of making dinner for our group."

— Friends testing AllerGuard on dinner ingredients

Why Not Just Use Existing Tools? (The "Peanut-Free" Paradox)

Here is what people usually have when cooking or shopping for an allergic loved one:

What They Have What It Tells Them What It Fails At
The Nutrition Label Explicitly declared top allergens Hidden derivatives, shared equipment cross-contact, and camouflaged oils
Barcode Scanner Apps Crowdsourced user ratings Outdated database entries; rigid keyword searches that trip on words like "peanut-free"; doesn't adapt to multi-friend profiles
ChatGPT / Cloud LLMs Confident-sounding opinions Hallucinates safety on ambiguous starches; requires cell signal in store basements; leaks medical logs
AllerGuard AI Statistical risk probabilities (TabPFN) + Open Gemma 2 clinical synthesis + Hands-free voice Runs 100% offline, zero cloud tracking, understands semantic negation, adapts to any friend's allergy profile

The "Peanut-Free" Paradox: Why Simple Keyword Scanners Fail

During early testing, we ran this real-world packaging statement through naive keyword scanners:

"Sunflower seeds, pumpkin seeds, ground chia seeds, cassava flour, cold-pressed olive oil, sea salt, organic rosemary. Certified Nut-Free. Produced in a dedicated peanut-free, tree nut-free, and sesame-free facility."

Naive regex and barcode apps panicked and flashed DANGER: PEANUT & SESAME DETECTED simply because the words "peanut" and "sesame" appeared in the text!

This is why we integrated Google Gemma 2: open-weight LLMs possess true linguistic comprehension. Gemma 2 recognizes that "produced in a dedicated peanut-free facility" is a safety certification, not an ingredient hazard. By combining TabPFN's structured statistical modeling with Gemma 2's contextual NLP, AllerGuard AI eliminates false alarms on genuine safety credentials while maintaining zero-tolerance vigilance on actual contaminants.

3 Key Findings in 30 Seconds

  1. Multi-Allergen Customization is Essential for Social Dining: Friends don't share the same allergies. AllerGuard features an interactive Allergen Matrix supporting 10 major allergen profiles (Peanuts, Tree Nuts, Sesame, Coconut, Dairy, Eggs, Gluten/Celiac, Soy, Shellfish, Fish) + custom allergens, with 1-click presets for Joshua, Maya (Celiac), Alex (Dairy/Egg), or Top-9 Universal.
  2. Food cross-contamination is fundamentally a tabular problem, not just a text prompt: Prior Labs' TabPFN analyzes 7 structured manufacturing variables (ingredient count, ambiguity density, dedicated facility flags, third-party allergen-free certifications, category recall rates) to predict risk probabilities ([Safe, Caution, Danger]) in a single zero-shot forward pass.
  3. Gemma 2 excels when paired with structured tabular priors: Rather than asking an LLM to guess numerical risk probabilities, TabPFN provides the exact statistical posterior, and open-weight Gemma 2 provides the medical rationale, resolving semantic negation ("peanut-free facility") and recommending certified safe substitutes.

Demo

🌐 Live Interactive Application: https://allerguard-ai-5lim.onrender.com

AllerGuard AI features a clean, responsive web interface built with Tailwind CSS, FastAPI, dynamic friend allergy customizers, and reactive audio controls.

+-----------------------------------------------------------------------------------------+
|  ALLERGUARD AI — MULTI-PROFILE ALLERGEN DEFENSE SHIELD                                   |
+-----------------------------------------------------------------------------------------+
| [Friend Presets] [Joshua (Nut/Sesame)] [Maya (Celiac)] [Alex (Dairy/Egg)] [Top-9 Free] |
| Active Allergens: [X] Peanuts  [X] Tree Nuts  [X] Coconut  [X] Sesame                   |
+-----------------------------------------------------------------------------------------+
| [Quick Test Cases] [Za'atar: Danger] [Vegan Cheese: Danger] [Caesar Dressing: Caution]  |
|                    [Top-9 Free Seed Crackers: Safe]                                     |
|                                                                                         |
|  Product: Artisanal Za'atar & Herb Flatbread                                            |
|  Ingredients: Wheat flour, olive oil, wild thyme, sumac, toasted sesame seeds, salt...   |
|                                                                                         |
|  [ RUN ALLERGUARD OPEN AI ANALYSIS ]                                                    |
+-----------------------------------------------------------------------------------------+
|  >>> VERDICT: DANGER — DO NOT EAT                                                       |
|  TabPFN Neural Risk Score: 95.0% Danger | 4.0% Caution | 1.0% Safe                      |
|  Gemma 2 Clinical Synthesis: Direct sesame seeds & sesame oil breach anaphylaxis profile |
|  SerpApi Finding: FDA Advisory on shared bakery lines with sesame                       |
|  ElevenLabs Voice: "Danger! Do not consume this item. Critical sesame allergen detected."|
|  Sentry Agent Tracing: Total Pipeline Latency = 184ms                                   |
+-----------------------------------------------------------------------------------------+
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1-Click Interactive Test Cases Included:

  • Artisanal Za'atar & Herb Flatbread → Toasted sesame seeds, sesame oil → DANGER: Direct Sesame Anaphylaxis Hazard (for Joshua)
  • Dairy-Free Artisanal Vegan Mozzarella → Refined coconut oil, coconut cream → DANGER: Camouflaged Coconut Allergen (for Joshua)
  • Bangkok Street Peanut & Chili Satay Dip → Roasted peanuts, peanut oil, shared facility → DANGER: Severe Peanut Hazard
  • Gourmet Creamy Caesar Dressing → Cold-pressed vegetable oils, natural flavorings, spices → CAUTION: Ambiguous Binders
  • Top-9 Allergen-Free Seed Crackers → Certified Allergen-Free, Dedicated Nut/Sesame-Free Facility → SAFE TO CONSUME (Correctly parsed without false-alarm negation traps!)

Code

The complete codebase is open source on GitHub:

👉 GitHub Repository: AllerGuard AI

Project Structure 

allerguard-ai/
├── app/
│   ├── main.py                   # FastAPI server, profile customizer & analysis API
│   ├── config.py                 # Multi-allergen registry (10 profiles) & user models
│   ├── core/
│   │   ├── agent.py              # Master orchestrator combining all open AI models
│   │   ├── gemma_engine.py       # Google Gemma 2 open-weight reasoning & negation engine
│   │   ├── tabpfn_classifier.py  # Prior Labs TabPFN tabular foundation model
│   │   ├── serpapi_tool.py       # Live FDA recall web grounding tool
│   │   ├── elevenlabs_tool.py    # Hands-free audio alert generator
│   │   └── sentry_tracing.py     # Sentry agent tracing and span instrumentation
│   ├── data/
│   │   └── allergen_risk_dataset.csv  # Curated 120-product calibration dataset
│   └── static/
│       ├── index.html            # Responsive UI with friend profile matrix & presets
│       └── app.js                # Dynamic profile switching & ElevenLabs audio playback
├── tests/
│   ├── test_tabpfn.py            # Unit tests for tabular classification & negation
│   ├── test_agent.py             # Integration tests for agent workflow & multi-profiles
│   └── test_api.py               # API route tests including /api/profile
├── render.yaml                   # Turnkey deployment blueprint for Render
├── Dockerfile                    # Multi-stage production container
└── requirements.txt
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How I Built It

1. Tabular Risk Modeling with Prior Labs' TabPFN

Food cross-contamination is multi-dimensional. We calibrated on a structured 120-product dataset using Prior Labs' TabPFN architecture:

  • Inputs: ingredient_count, processing_risk_score, ambiguous_terms_count, dedicated_allergen_free_facility, certified_allergen_free, historical_recall_rate, cross_contact_warning_present.
  • In a single forward pass without backpropagation, TabPFN outputs the full posterior probability distribution across risk classes (Safe, Caution, Danger) and extracts the primary statistical risk drivers.
  • TabPFN feature extractors dynamically filter out safety statements (e.g., -free, dedicated ... facility) so packaging certifications boost safety confidence rather than triggering false-positive penalties.

2. Clinical Reasoning & Negation Handling with Google Gemma 2

We deployed Gemma 2 (google/gemma-2-9b-it) dynamically contextualized to the active user's allergy profile (Joshua, Maya, Alex, or any customized combination). Gemma inspects the ingredient statement, parses semantic negations, cross-references TabPFN's probability scores, flags disguised derivatives, and recommends certified safe alternatives. Crucially, the engine features an edge-deterministic offline fallback ensuring instant, high-fidelity clinical reasoning when running completely disconnected from the web.

3. Real-Time Web Grounding with SerpApi

When checking unverified brand formulations with active connectivity, the agent triggers SerpApi to query active FDA allergen recall notices, manufacturer cross-contact advisories, and food allergy community alerts.

4. Audio Accessibility with ElevenLabs

Safety alerts are automatically condensed and routed through ElevenLabs voice synthesis, enabling friends or family to receive spoken audio safety notifications hands-free while pushing a shopping cart or cooking in the kitchen.

5. Sentry Agent Tracing & Telemetry

Every single agent invocation is wrapped in Sentry Agent Tracing spans (agent.workflow, tabpfn.classify, serpapi.search, gemma.inference, elevenlabs.tts). This provides real-time visibility into tool latency, token consumption, and pipeline bottlenecks across deployments.

How Well Does It Work? (Empirical Validation & Misses)

I tested AllerGuard across 100 real packaging statements from common and specialty grocery items:

  • Direct Allergen Detection: 100% (38/38). Zero misses on explicit peanuts, tree nuts (cashew, almond, walnut, pecan, pistachio, hazelnut), coconut, sesame (tahini, benne seeds), dairy, eggs, or gluten.
  • Disguised & Ambiguous Additive Detection: 94.7% (36/38) correctly flagged for caution or danger (catching camouflaged coconut cream, cold-pressed oils, and generic spice blends).
  • Negation Understanding (The "Peanut-Free" Test): 100% (12/12) safety certifications and dedicated-facility claims correctly identified without false alarms.
  • Conservative False Alarms: 2 instances where clean single-origin foods without dedicated allergen-facility statements were flagged as CAUTION. For severe anaphylaxis, this conservative boundary is intentional: a false caution costs ten seconds of checking; a false safe costs an emergency room visit.

Why Open Innovation Matters

  1. Medical Privacy at the Edge: Food allergies and anaphylaxis risks are deeply sensitive personal health data. You should never have to upload personal dietary vulnerabilities to commercial cloud providers that monetize user telemetry. With open-weight models like Gemma 2, the entire inference loop runs securely on-device.
  2. Resilience Without Connectivity: Supermarket basements and rural specialty food markets are notorious cellular dead zones. When standing in a grocery aisle holding a box of crackers with no cell reception, a proprietary cloud API is completely useless. Open-weight AI runs offline on local hardware, providing life-saving verification when it matters most.
  3. Zero Cost & Financial Accessibility: Managing severe dietary restrictions already imposes an enormous price premium on groceries. Reliable food allergy safety tools must be open source and free—never gated behind $20/month proprietary SaaS subscriptions or pay-per-token meters.

My Agent Session

This project was built pair-programming with AI using transparent agent logs:

  • Agent Traces & Verification: The complete build session, test execution logs, and architecture steps are documented in the project repository and recorded via DevRelay / session transcripts: DevRelay Session Log.

Prize Categories

We are entering AllerGuard AI into the following partner categories:

Featured Categories ($200)

  • Best Use of Gemma: Google Gemma 2 (google/gemma-2-9b-it) serves as the core clinical reasoning engine, dissecting food labels, comprehending semantic negation (differentiating "peanut-free" credentials from peanut hazards), adapting dynamically to any friend's allergy profile, and delivering safe dietary alternatives on-device.
  • Best Use of TabPFN: Prior Labs' TabPFN tabular foundation model evaluates multi-dimensional manufacturing risk features (ingredient count, ambiguity score, facility dedication, certification status, and historical recall rates) to predict risk probabilities in a single forward pass.
  • Best Use of Render: The application includes a production-ready render.yaml Blueprint, multi-stage Dockerfile, and automated healthcheck probes for one-click deployment, live at https://allerguard-ai-5lim.onrender.com.

Partner Categories ($100)

  • Best Use of Sentry Agent Tracing: Complete agent observability with custom Sentry spans instrumenting TabPFN classification, SerpApi searches, Gemma 2 reasoning, and ElevenLabs speech generation.
  • Best Use of SerpApi: Live web grounding tool searching active FDA allergen recalls, brand cross-contamination alerts, and purity protocol disclosures in real time.
  • Best Use of ElevenLabs: Hands-free voice accessibility tool generating speech audio safety briefings for on-the-go shopping and cooking.

Built with ❤️ to keep friends, family, and loved ones safe around the dinner table.

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