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SugarShield

Live:https://sugarshield.vercel.app/ · Eval Dashboard:https://sugarshield.vercel.app/eval

Food ingredient safety scanner with built-in evaluation infrastructure. The eval isn't an afterthought — it's a first-class feature of the product.

Why I Built This

Food labels use dozens of names for sugar — maltodextrin, dextrose, cane juice, rice syrup, corn syrup solids. A keyword-only system would miss half of them. Most nutrition apps don't tell you how confident they are or what kind of mistakes they make.

The core PM question was: what failure mode is worse — over-warning or missing hidden sugar? For a safety-first product, a false negative (telling someone a product is safe when it isn't) is riskier than a false positive (prompting a double-check). That answer shaped every eval and product decision.

Eval Infrastructure

Live Eval Dashboard →

The Product Decision

We intentionally over-warn rather than miss hidden sugar. Conservative bias is not a bug — it's an explicit product choice documented in the eval page.

Eval Metrics (Strict Mode)

MetricResultWhy It Matters
False Negatives0Intentional. Missing hidden sugar is the riskiest failure mode
Trigger Match Rate87%How often the model identifies the correct sugar ingredient
Conservative BiasIntentionalWarn more, miss less — safety over precision

Strict vs. Lenient Mode

The eval dashboard exposes two evaluation modes so users can see the tradeoff:

  • Strict Mode — flags artificial sweeteners, borderline ingredients, naturally occurring sugars in context
  • Lenient Mode — passes products where sugar is naturally occurring or debated nutritionally

This lets users understand why a product was flagged, not just that it was flagged.

Known Limitation (Documented)

The system may over-flag some products. This is intentional — explicitly stated on the eval page as a product tradeoff, not hidden from users.

PM Insight (From Eval Page)

Several WARN results (e.g., Diet Soda, Coconut Water) are intentional. These products contain sweeteners or naturally occurring sugars that are debated in nutritional science. SugarShield defaults to caution rather than silent pass to preserve user trust.

What This Demonstrates

  • Eval-first product design — the /eval page is built into the product, not a separate doc
  • Conservative bias as a product decision — not an accident, documented and explained to users
  • False negative optimization — 0 missed detections by intentional design
  • Confidence transparency — users see confidence level and which triggers fired
  • Failure mode honesty — known limitations surface in the product UI, not hidden in a README

How It Works

  • Frontend: Next.js 14, React 18, Tailwind CSS
  • AI Core: OpenAI GPT-4o/GPT-3.5-turbo for ingredient classification
  • Eval Data: Ground-truth evalSet.json with 15 test cases across input types (scan, link, upload)
  • Flow: User → scan/upload ingredient label → AI classifies sugar content → confidence score + trigger list → PASS / WARN / FAIL

Run Locally

  1. git clone https://github.com/Ruthwik-Data/sugarshield.git
  2. cd sugarshield && npm install
  3. Create .env.local with your OPENAI_API_KEY
  4. npm run dev → http://localhost:3000
  5. Visit /eval to see the full evaluation dashboard

Status

  • ✅ MVP with AI classification shipped
  • ✅ Eval dashboard with strict/lenient mode
  • ✅ False negative tracking (0 missed detections)
  • ✅ Conservative bias documented as product decision
  • ⏳ Multi-day historical trends
  • ⏳ Browser-based nutritional scanning for online grocery shopping

About

AI-powered ingredient safety scanner that detects harmful additives in food products using computer vision and ML.

sugarshield.vercel.app

Resources

Readme
Activity

Stars

0 stars

Watchers

0 watching

Forks

0 forks
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GitHub - Ruthwik-Data/sugarshield: AI-powered ingredient safety scanner that detects harmful additives in food products using computer vision and ML. · GitHub
Skip to content
BranchesTags
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SugarShield
Live:https://sugarshield.vercel.app/ · Eval Dashboard:https://sugarshield.vercel.app/eval
Food ingredient safety scanner with built-in evaluation infrastructure. The eval isn't an afterthought — it's a first-class feature of the product.
Why I Built This
Food labels use dozens of names for sugar — maltodextrin, dextrose, cane juice, rice syrup, corn syrup solids. A keyword-only system would miss half of them. Most nutrition apps don't tell you how confident they are or what kind of mistakes they make.
The core PM question was: what failure mode is worse — over-warning or missing hidden sugar? For a safety-first product, a false negative (telling someone a product is safe when it isn't) is riskier than a false positive (prompting a double-check). That answer shaped every eval and product decision.
Eval Infrastructure
Live Eval Dashboard →
The Product Decision
We intentionally over-warn rather than miss hidden sugar. Conservative bias is not a bug — it's an explicit product choice documented in the eval page.
Eval Metrics (Strict Mode)
MetricResultWhy It Matters
False Negatives0Intentional. Missing hidden sugar is the riskiest failure mode
Trigger Match Rate87%How often the model identifies the correct sugar ingredient
Conservative BiasIntentionalWarn more, miss less — safety over precision
Strict vs. Lenient Mode
The eval dashboard exposes two evaluation modes so users can see the tradeoff:
  • Strict Mode — flags artificial sweeteners, borderline ingredients, naturally occurring sugars in context
  • Lenient Mode — passes products where sugar is naturally occurring or debated nutritionally
This lets users understand why a product was flagged, not just that it was flagged.
Known Limitation (Documented)
The system may over-flag some products. This is intentional — explicitly stated on the eval page as a product tradeoff, not hidden from users.
PM Insight (From Eval Page)
Several WARN results (e.g., Diet Soda, Coconut Water) are intentional. These products contain sweeteners or naturally occurring sugars that are debated in nutritional science. SugarShield defaults to caution rather than silent pass to preserve user trust.
What This Demonstrates
  • Eval-first product design — the /eval page is built into the product, not a separate doc
  • Conservative bias as a product decision — not an accident, documented and explained to users
  • False negative optimization — 0 missed detections by intentional design
  • Confidence transparency — users see confidence level and which triggers fired
  • Failure mode honesty — known limitations surface in the product UI, not hidden in a README
How It Works
  • Frontend: Next.js 14, React 18, Tailwind CSS
  • AI Core: OpenAI GPT-4o/GPT-3.5-turbo for ingredient classification
  • Eval Data: Ground-truth evalSet.json with 15 test cases across input types (scan, link, upload)
  • Flow: User → scan/upload ingredient label → AI classifies sugar content → confidence score + trigger list → PASS / WARN / FAIL
Run Locally
  1. git clone https://github.com/Ruthwik-Data/sugarshield.git
  2. cd sugarshield && npm install
  3. Create .env.local with your OPENAI_API_KEY
  4. npm run dev → http://localhost:3000
  5. Visit /eval to see the full evaluation dashboard
Status
  • ✅ MVP with AI classification shipped
  • ✅ Eval dashboard with strict/lenient mode
  • ✅ False negative tracking (0 missed detections)
  • ✅ Conservative bias documented as product decision
  • ⏳ Multi-day historical trends
  • ⏳ Browser-based nutritional scanning for online grocery shopping
About
AI-powered ingredient safety scanner that detects harmful additives in food products using computer vision and ML.
sugarshield.vercel.app
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
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, 'i');
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GitHub - Ruthwik-Data/sugarshield: AI-powered ingredient safety scanner that detects harmful additives in food products using computer vision and ML. · GitHub
Skip to content
BranchesTags
Repository files navigation
SugarShield
Live:https://sugarshield.vercel.app/ · Eval Dashboard:https://sugarshield.vercel.app/eval
Food ingredient safety scanner with built-in evaluation infrastructure. The eval isn't an afterthought — it's a first-class feature of the product.
Why I Built This
Food labels use dozens of names for sugar — maltodextrin, dextrose, cane juice, rice syrup, corn syrup solids. A keyword-only system would miss half of them. Most nutrition apps don't tell you how confident they are or what kind of mistakes they make.
The core PM question was: what failure mode is worse — over-warning or missing hidden sugar? For a safety-first product, a false negative (telling someone a product is safe when it isn't) is riskier than a false positive (prompting a double-check). That answer shaped every eval and product decision.
Eval Infrastructure
Live Eval Dashboard →
The Product Decision
We intentionally over-warn rather than miss hidden sugar. Conservative bias is not a bug — it's an explicit product choice documented in the eval page.
Eval Metrics (Strict Mode)
MetricResultWhy It Matters
False Negatives0Intentional. Missing hidden sugar is the riskiest failure mode
Trigger Match Rate87%How often the model identifies the correct sugar ingredient
Conservative BiasIntentionalWarn more, miss less — safety over precision
Strict vs. Lenient Mode
The eval dashboard exposes two evaluation modes so users can see the tradeoff:
  • Strict Mode — flags artificial sweeteners, borderline ingredients, naturally occurring sugars in context
  • Lenient Mode — passes products where sugar is naturally occurring or debated nutritionally
This lets users understand why a product was flagged, not just that it was flagged.
Known Limitation (Documented)
The system may over-flag some products. This is intentional — explicitly stated on the eval page as a product tradeoff, not hidden from users.
PM Insight (From Eval Page)
Several WARN results (e.g., Diet Soda, Coconut Water) are intentional. These products contain sweeteners or naturally occurring sugars that are debated in nutritional science. SugarShield defaults to caution rather than silent pass to preserve user trust.
What This Demonstrates
  • Eval-first product design — the /eval page is built into the product, not a separate doc
  • Conservative bias as a product decision — not an accident, documented and explained to users
  • False negative optimization — 0 missed detections by intentional design
  • Confidence transparency — users see confidence level and which triggers fired
  • Failure mode honesty — known limitations surface in the product UI, not hidden in a README
How It Works
  • Frontend: Next.js 14, React 18, Tailwind CSS
  • AI Core: OpenAI GPT-4o/GPT-3.5-turbo for ingredient classification
  • Eval Data: Ground-truth evalSet.json with 15 test cases across input types (scan, link, upload)
  • Flow: User → scan/upload ingredient label → AI classifies sugar content → confidence score + trigger list → PASS / WARN / FAIL
Run Locally
  1. git clone https://github.com/Ruthwik-Data/sugarshield.git
  2. cd sugarshield && npm install
  3. Create .env.local with your OPENAI_API_KEY
  4. npm run dev → http://localhost:3000
  5. Visit /eval to see the full evaluation dashboard
Status
  • ✅ MVP with AI classification shipped
  • ✅ Eval dashboard with strict/lenient mode
  • ✅ False negative tracking (0 missed detections)
  • ✅ Conservative bias documented as product decision
  • ⏳ Multi-day historical trends
  • ⏳ Browser-based nutritional scanning for online grocery shopping
About
AI-powered ingredient safety scanner that detects harmful additives in food products using computer vision and ML.
sugarshield.vercel.app
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
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var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - Ruthwik-Data/sugarshield: AI-powered ingredient safety scanner that detects harmful additives in food products using computer vision and ML. · GitHub
Skip to content
BranchesTags
Repository files navigation
SugarShield
Live:https://sugarshield.vercel.app/ · Eval Dashboard:https://sugarshield.vercel.app/eval
Food ingredient safety scanner with built-in evaluation infrastructure. The eval isn't an afterthought — it's a first-class feature of the product.
Why I Built This
Food labels use dozens of names for sugar — maltodextrin, dextrose, cane juice, rice syrup, corn syrup solids. A keyword-only system would miss half of them. Most nutrition apps don't tell you how confident they are or what kind of mistakes they make.
The core PM question was: what failure mode is worse — over-warning or missing hidden sugar? For a safety-first product, a false negative (telling someone a product is safe when it isn't) is riskier than a false positive (prompting a double-check). That answer shaped every eval and product decision.
Eval Infrastructure
Live Eval Dashboard →
The Product Decision
We intentionally over-warn rather than miss hidden sugar. Conservative bias is not a bug — it's an explicit product choice documented in the eval page.
Eval Metrics (Strict Mode)
MetricResultWhy It Matters
False Negatives0Intentional. Missing hidden sugar is the riskiest failure mode
Trigger Match Rate87%How often the model identifies the correct sugar ingredient
Conservative BiasIntentionalWarn more, miss less — safety over precision
Strict vs. Lenient Mode
The eval dashboard exposes two evaluation modes so users can see the tradeoff:
  • Strict Mode — flags artificial sweeteners, borderline ingredients, naturally occurring sugars in context
  • Lenient Mode — passes products where sugar is naturally occurring or debated nutritionally
This lets users understand why a product was flagged, not just that it was flagged.
Known Limitation (Documented)
The system may over-flag some products. This is intentional — explicitly stated on the eval page as a product tradeoff, not hidden from users.
PM Insight (From Eval Page)
Several WARN results (e.g., Diet Soda, Coconut Water) are intentional. These products contain sweeteners or naturally occurring sugars that are debated in nutritional science. SugarShield defaults to caution rather than silent pass to preserve user trust.
What This Demonstrates
  • Eval-first product design — the /eval page is built into the product, not a separate doc
  • Conservative bias as a product decision — not an accident, documented and explained to users
  • False negative optimization — 0 missed detections by intentional design
  • Confidence transparency — users see confidence level and which triggers fired
  • Failure mode honesty — known limitations surface in the product UI, not hidden in a README
How It Works
  • Frontend: Next.js 14, React 18, Tailwind CSS
  • AI Core: OpenAI GPT-4o/GPT-3.5-turbo for ingredient classification
  • Eval Data: Ground-truth evalSet.json with 15 test cases across input types (scan, link, upload)
  • Flow: User → scan/upload ingredient label → AI classifies sugar content → confidence score + trigger list → PASS / WARN / FAIL
Run Locally
  1. git clone https://github.com/Ruthwik-Data/sugarshield.git
  2. cd sugarshield && npm install
  3. Create .env.local with your OPENAI_API_KEY
  4. npm run dev → http://localhost:3000
  5. Visit /eval to see the full evaluation dashboard
Status
  • ✅ MVP with AI classification shipped
  • ✅ Eval dashboard with strict/lenient mode
  • ✅ False negative tracking (0 missed detections)
  • ✅ Conservative bias documented as product decision
  • ⏳ Multi-day historical trends
  • ⏳ Browser-based nutritional scanning for online grocery shopping
About
AI-powered ingredient safety scanner that detects harmful additives in food products using computer vision and ML.
sugarshield.vercel.app
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

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var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '
GitHub - Ruthwik-Data/sugarshield: AI-powered ingredient safety scanner that detects harmful additives in food products using computer vision and ML. · GitHub
Skip to content
BranchesTags
Repository files navigation
SugarShield
Live:https://sugarshield.vercel.app/ · Eval Dashboard:https://sugarshield.vercel.app/eval
Food ingredient safety scanner with built-in evaluation infrastructure. The eval isn't an afterthought — it's a first-class feature of the product.
Why I Built This
Food labels use dozens of names for sugar — maltodextrin, dextrose, cane juice, rice syrup, corn syrup solids. A keyword-only system would miss half of them. Most nutrition apps don't tell you how confident they are or what kind of mistakes they make.
The core PM question was: what failure mode is worse — over-warning or missing hidden sugar? For a safety-first product, a false negative (telling someone a product is safe when it isn't) is riskier than a false positive (prompting a double-check). That answer shaped every eval and product decision.
Eval Infrastructure
Live Eval Dashboard →
The Product Decision
We intentionally over-warn rather than miss hidden sugar. Conservative bias is not a bug — it's an explicit product choice documented in the eval page.
Eval Metrics (Strict Mode)
MetricResultWhy It Matters
False Negatives0Intentional. Missing hidden sugar is the riskiest failure mode
Trigger Match Rate87%How often the model identifies the correct sugar ingredient
Conservative BiasIntentionalWarn more, miss less — safety over precision
Strict vs. Lenient Mode
The eval dashboard exposes two evaluation modes so users can see the tradeoff:
  • Strict Mode — flags artificial sweeteners, borderline ingredients, naturally occurring sugars in context
  • Lenient Mode — passes products where sugar is naturally occurring or debated nutritionally
This lets users understand why a product was flagged, not just that it was flagged.
Known Limitation (Documented)
The system may over-flag some products. This is intentional — explicitly stated on the eval page as a product tradeoff, not hidden from users.
PM Insight (From Eval Page)
Several WARN results (e.g., Diet Soda, Coconut Water) are intentional. These products contain sweeteners or naturally occurring sugars that are debated in nutritional science. SugarShield defaults to caution rather than silent pass to preserve user trust.
What This Demonstrates
  • Eval-first product design — the /eval page is built into the product, not a separate doc
  • Conservative bias as a product decision — not an accident, documented and explained to users
  • False negative optimization — 0 missed detections by intentional design
  • Confidence transparency — users see confidence level and which triggers fired
  • Failure mode honesty — known limitations surface in the product UI, not hidden in a README
How It Works
  • Frontend: Next.js 14, React 18, Tailwind CSS
  • AI Core: OpenAI GPT-4o/GPT-3.5-turbo for ingredient classification
  • Eval Data: Ground-truth evalSet.json with 15 test cases across input types (scan, link, upload)
  • Flow: User → scan/upload ingredient label → AI classifies sugar content → confidence score + trigger list → PASS / WARN / FAIL
Run Locally
  1. git clone https://github.com/Ruthwik-Data/sugarshield.git
  2. cd sugarshield && npm install
  3. Create .env.local with your OPENAI_API_KEY
  4. npm run dev → http://localhost:3000
  5. Visit /eval to see the full evaluation dashboard
Status
  • ✅ MVP with AI classification shipped
  • ✅ Eval dashboard with strict/lenient mode
  • ✅ False negative tracking (0 missed detections)
  • ✅ Conservative bias documented as product decision
  • ⏳ Multi-day historical trends
  • ⏳ Browser-based nutritional scanning for online grocery shopping
About
AI-powered ingredient safety scanner that detects harmful additives in food products using computer vision and ML.
sugarshield.vercel.app
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
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(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - Ruthwik-Data/sugarshield: AI-powered ingredient safety scanner that detects harmful additives in food products using computer vision and ML. · GitHub
Skip to content
BranchesTags
Repository files navigation
SugarShield
Live:https://sugarshield.vercel.app/ · Eval Dashboard:https://sugarshield.vercel.app/eval
Food ingredient safety scanner with built-in evaluation infrastructure. The eval isn't an afterthought — it's a first-class feature of the product.
Why I Built This
Food labels use dozens of names for sugar — maltodextrin, dextrose, cane juice, rice syrup, corn syrup solids. A keyword-only system would miss half of them. Most nutrition apps don't tell you how confident they are or what kind of mistakes they make.
The core PM question was: what failure mode is worse — over-warning or missing hidden sugar? For a safety-first product, a false negative (telling someone a product is safe when it isn't) is riskier than a false positive (prompting a double-check). That answer shaped every eval and product decision.
Eval Infrastructure
Live Eval Dashboard →
The Product Decision
We intentionally over-warn rather than miss hidden sugar. Conservative bias is not a bug — it's an explicit product choice documented in the eval page.
Eval Metrics (Strict Mode)
MetricResultWhy It Matters
False Negatives0Intentional. Missing hidden sugar is the riskiest failure mode
Trigger Match Rate87%How often the model identifies the correct sugar ingredient
Conservative BiasIntentionalWarn more, miss less — safety over precision
Strict vs. Lenient Mode
The eval dashboard exposes two evaluation modes so users can see the tradeoff:
  • Strict Mode — flags artificial sweeteners, borderline ingredients, naturally occurring sugars in context
  • Lenient Mode — passes products where sugar is naturally occurring or debated nutritionally
This lets users understand why a product was flagged, not just that it was flagged.
Known Limitation (Documented)
The system may over-flag some products. This is intentional — explicitly stated on the eval page as a product tradeoff, not hidden from users.
PM Insight (From Eval Page)
Several WARN results (e.g., Diet Soda, Coconut Water) are intentional. These products contain sweeteners or naturally occurring sugars that are debated in nutritional science. SugarShield defaults to caution rather than silent pass to preserve user trust.
What This Demonstrates
  • Eval-first product design — the /eval page is built into the product, not a separate doc
  • Conservative bias as a product decision — not an accident, documented and explained to users
  • False negative optimization — 0 missed detections by intentional design
  • Confidence transparency — users see confidence level and which triggers fired
  • Failure mode honesty — known limitations surface in the product UI, not hidden in a README
How It Works
  • Frontend: Next.js 14, React 18, Tailwind CSS
  • AI Core: OpenAI GPT-4o/GPT-3.5-turbo for ingredient classification
  • Eval Data: Ground-truth evalSet.json with 15 test cases across input types (scan, link, upload)
  • Flow: User → scan/upload ingredient label → AI classifies sugar content → confidence score + trigger list → PASS / WARN / FAIL
Run Locally
  1. git clone https://github.com/Ruthwik-Data/sugarshield.git
  2. cd sugarshield && npm install
  3. Create .env.local with your OPENAI_API_KEY
  4. npm run dev → http://localhost:3000
  5. Visit /eval to see the full evaluation dashboard
Status
  • ✅ MVP with AI classification shipped
  • ✅ Eval dashboard with strict/lenient mode
  • ✅ False negative tracking (0 missed detections)
  • ✅ Conservative bias documented as product decision
  • ⏳ Multi-day historical trends
  • ⏳ Browser-based nutritional scanning for online grocery shopping
About
AI-powered ingredient safety scanner that detects harmful additives in food products using computer vision and ML.
sugarshield.vercel.app
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Readme
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GitHub - Ruthwik-Data/sugarshield: AI-powered ingredient safety scanner that detects harmful additives in food products using computer vision and ML. · GitHub
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SugarShield
Live:https://sugarshield.vercel.app/ · Eval Dashboard:https://sugarshield.vercel.app/eval
Food ingredient safety scanner with built-in evaluation infrastructure. The eval isn't an afterthought — it's a first-class feature of the product.
Why I Built This
Food labels use dozens of names for sugar — maltodextrin, dextrose, cane juice, rice syrup, corn syrup solids. A keyword-only system would miss half of them. Most nutrition apps don't tell you how confident they are or what kind of mistakes they make.
The core PM question was: what failure mode is worse — over-warning or missing hidden sugar? For a safety-first product, a false negative (telling someone a product is safe when it isn't) is riskier than a false positive (prompting a double-check). That answer shaped every eval and product decision.
Eval Infrastructure
Live Eval Dashboard →
The Product Decision
We intentionally over-warn rather than miss hidden sugar. Conservative bias is not a bug — it's an explicit product choice documented in the eval page.
Eval Metrics (Strict Mode)
MetricResultWhy It Matters
False Negatives0Intentional. Missing hidden sugar is the riskiest failure mode
Trigger Match Rate87%How often the model identifies the correct sugar ingredient
Conservative BiasIntentionalWarn more, miss less — safety over precision
Strict vs. Lenient Mode
The eval dashboard exposes two evaluation modes so users can see the tradeoff:
  • Strict Mode — flags artificial sweeteners, borderline ingredients, naturally occurring sugars in context
  • Lenient Mode — passes products where sugar is naturally occurring or debated nutritionally
This lets users understand why a product was flagged, not just that it was flagged.
Known Limitation (Documented)
The system may over-flag some products. This is intentional — explicitly stated on the eval page as a product tradeoff, not hidden from users.
PM Insight (From Eval Page)
Several WARN results (e.g., Diet Soda, Coconut Water) are intentional. These products contain sweeteners or naturally occurring sugars that are debated in nutritional science. SugarShield defaults to caution rather than silent pass to preserve user trust.
What This Demonstrates
  • Eval-first product design — the /eval page is built into the product, not a separate doc
  • Conservative bias as a product decision — not an accident, documented and explained to users
  • False negative optimization — 0 missed detections by intentional design
  • Confidence transparency — users see confidence level and which triggers fired
  • Failure mode honesty — known limitations surface in the product UI, not hidden in a README
How It Works
  • Frontend: Next.js 14, React 18, Tailwind CSS
  • AI Core: OpenAI GPT-4o/GPT-3.5-turbo for ingredient classification
  • Eval Data: Ground-truth evalSet.json with 15 test cases across input types (scan, link, upload)
  • Flow: User → scan/upload ingredient label → AI classifies sugar content → confidence score + trigger list → PASS / WARN / FAIL
Run Locally
  1. git clone https://github.com/Ruthwik-Data/sugarshield.git
  2. cd sugarshield && npm install
  3. Create .env.local with your OPENAI_API_KEY
  4. npm run dev → http://localhost:3000
  5. Visit /eval to see the full evaluation dashboard
Status
  • ✅ MVP with AI classification shipped
  • ✅ Eval dashboard with strict/lenient mode
  • ✅ False negative tracking (0 missed detections)
  • ✅ Conservative bias documented as product decision
  • ⏳ Multi-day historical trends
  • ⏳ Browser-based nutritional scanning for online grocery shopping
About
AI-powered ingredient safety scanner that detects harmful additives in food products using computer vision and ML.
sugarshield.vercel.app
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
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})();
})();
GitHub - Ruthwik-Data/sugarshield: AI-powered ingredient safety scanner that detects harmful additives in food products using computer vision and ML. · GitHub
Skip to content
BranchesTags
Repository files navigation
SugarShield
Live:https://sugarshield.vercel.app/ · Eval Dashboard:https://sugarshield.vercel.app/eval
Food ingredient safety scanner with built-in evaluation infrastructure. The eval isn't an afterthought — it's a first-class feature of the product.
Why I Built This
Food labels use dozens of names for sugar — maltodextrin, dextrose, cane juice, rice syrup, corn syrup solids. A keyword-only system would miss half of them. Most nutrition apps don't tell you how confident they are or what kind of mistakes they make.
The core PM question was: what failure mode is worse — over-warning or missing hidden sugar? For a safety-first product, a false negative (telling someone a product is safe when it isn't) is riskier than a false positive (prompting a double-check). That answer shaped every eval and product decision.
Eval Infrastructure
Live Eval Dashboard →
The Product Decision
We intentionally over-warn rather than miss hidden sugar. Conservative bias is not a bug — it's an explicit product choice documented in the eval page.
Eval Metrics (Strict Mode)
MetricResultWhy It Matters
False Negatives0Intentional. Missing hidden sugar is the riskiest failure mode
Trigger Match Rate87%How often the model identifies the correct sugar ingredient
Conservative BiasIntentionalWarn more, miss less — safety over precision
Strict vs. Lenient Mode
The eval dashboard exposes two evaluation modes so users can see the tradeoff:
  • Strict Mode — flags artificial sweeteners, borderline ingredients, naturally occurring sugars in context
  • Lenient Mode — passes products where sugar is naturally occurring or debated nutritionally
This lets users understand why a product was flagged, not just that it was flagged.
Known Limitation (Documented)
The system may over-flag some products. This is intentional — explicitly stated on the eval page as a product tradeoff, not hidden from users.
PM Insight (From Eval Page)
Several WARN results (e.g., Diet Soda, Coconut Water) are intentional. These products contain sweeteners or naturally occurring sugars that are debated in nutritional science. SugarShield defaults to caution rather than silent pass to preserve user trust.
What This Demonstrates
  • Eval-first product design — the /eval page is built into the product, not a separate doc
  • Conservative bias as a product decision — not an accident, documented and explained to users
  • False negative optimization — 0 missed detections by intentional design
  • Confidence transparency — users see confidence level and which triggers fired
  • Failure mode honesty — known limitations surface in the product UI, not hidden in a README
How It Works
  • Frontend: Next.js 14, React 18, Tailwind CSS
  • AI Core: OpenAI GPT-4o/GPT-3.5-turbo for ingredient classification
  • Eval Data: Ground-truth evalSet.json with 15 test cases across input types (scan, link, upload)
  • Flow: User → scan/upload ingredient label → AI classifies sugar content → confidence score + trigger list → PASS / WARN / FAIL
Run Locally
  1. git clone https://github.com/Ruthwik-Data/sugarshield.git
  2. cd sugarshield && npm install
  3. Create .env.local with your OPENAI_API_KEY
  4. npm run dev → http://localhost:3000
  5. Visit /eval to see the full evaluation dashboard
Status
  • ✅ MVP with AI classification shipped
  • ✅ Eval dashboard with strict/lenient mode
  • ✅ False negative tracking (0 missed detections)
  • ✅ Conservative bias documented as product decision
  • ⏳ Multi-day historical trends
  • ⏳ Browser-based nutritional scanning for online grocery shopping
About
AI-powered ingredient safety scanner that detects harmful additives in food products using computer vision and ML.
sugarshield.vercel.app
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages