DEV Community: Souvik Das

FoodTruth: Exposing Packaged Food Deception with Open-Weight Vision AI

Souvik Das — Tue, 20 Oct 2026 02:38:57 +0000

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

What I Built

Modern supermarket aisles are designed to mislead. Packaged food companies spend billions on deceptive front-of-pack claims like "No Added Sugar", "High Protein", and "100% Natural", while burying metabolic traps in fine print on the back panel:

Hidden Sugars: Products marked "No Added Sugar" routinely contain Maltodextrin (with a Glycemic Index of 110, spiking blood sugar faster than pure glucose), liquid glucose, or laxative doses of polyols like Maltitol.
The "Protein Sprinkle" Trick: Snack bars claim "Rich in Whey Protein" in bold front lettering, yet their ingredient lists place Whey Isolate behind maida (refined wheat flour), sugar, palm oil, and soy flour — merely sprinkling it in to satisfy legal claim thresholds.
Unlawful Disease Claims: Brands blatantly violate food safety regulations (such as FSSAI Section 24 and FDA guidelines) by claiming their snack or shake can "cure diabetes" or "control hypertension."

FoodTruth is an open-source, AI-powered food regulatory auditor and nutritional deception detector. By snapping or uploading dual packaging photos (Front Branding + Back Nutrition Table & Ingredients), FoodTruth runs an automated, multi-tiered audit that strips away corporate marketing and delivers the unvarnished metabolic truth.

       Front Branding                  Back Ingredients & Nutrition
  [ "15g Whey Protein" ]           [ Sugar, Palm Oil, Whey Isolate (2%) ]
             \                                      /
              \                                    /
               ▼                                  ▼
         ┌──────────────────────────────────────────────┐
         │          FoodTruth AI Audit Engine           │
         │   • Gemma Open Vision Schema Extraction      │
         │   • Sweetener Glycemic Index (GI) Matrix     │
         │   • DIAAS Protein Bioavailability & Dilution │
         │   • P:Cal Metabolic Efficiency Yield         │
         │   • FSSAI / FDA Regulatory Claims Audit      │
         └──────────────────────────────────────────────┘
                                │
                                ▼
         ┌──────────────────────────────────────────────┐
         │             Actionable Verdict               │
         │   "Protein Sprinkle Trick Detected"          │
         │   Optimized For: Quick pre-workout carbs     │
         │   Avoid If: Diabetic / Strict Calorie Deficit│
         └──────────────────────────────────────────────┘

Who Would Pay For It? (The Side Hustle Business Model)

FoodTruth is built with a direct commercial monetization pathway:

B2C Micro-SaaS (Consumer Grocery Companion): A mobile web app / PWA subscription ($4.99/month or $39/year) targeting diabetics, keto athletes, parents, and health-conscious consumers who want a 2-second barcode/packaging camera scan while walking supermarket aisles.
Pro Coach / Dietitian Pantry Audits ($19/mo per seat): Fitness trainers, dietitians, and functional medicine practitioners who audit clients' pantry items and generate branded, PDF-exportable nutritional truth reports.
B2B Pre-Print Packaging Compliance API ($49/audit or $199/mo): Emerging D2C clean-label food brands and packaging design agencies need to pre-audit their label artwork against FSSAI and FDA advertising regulations before committing thousands of dollars to physical printing and risking regulatory notices or product recalls.

Demo

Live Repository: https://github.com/dasouvik122005/FoodTruth
Local Interactive Setup: Clone the repo, launch FastAPI (python main.py) and Vite (npm run dev), then test live packaging scans or choose from pre-loaded real-world audit payloads (Yoga Bar, The Whole Truth, and SuperYou Wafer).

User Interface Highlights

Bespoke Editorial Aesthetic: Designed with an editorial high-contrast light layout, split-screen desktop experience, and fluid mobile-first ergonomics.
In-App Camera Viewfinder: Integrated mobile camera capture with canvas-based shutter action for instant grocery shelf snapping.
Deterministic Metric Cards:
- Metabolic Sugar Risk: High/Moderate/Low risk calculation based on real Glycemic Index (GI) database mapping.
- Protein Quality Tier: Classifies proteins by DIAAS bioavailability (Tier 1 Whey Isolate down to Tier 3 Wheat Gluten/Flour fillers).
- Calorie Yield (P:Cal): Calculates what percentage of total calories actually comes from bioavailable protein vs caloric padding.
- FSSAI Violation Alerts: Real-time legal citations identifying specific regulatory breaches.

Code

The entire codebase is open-source under the MIT license at GitHub:

dasouvik122005 / FoodTruth

FoodTruth uses Gemma to analyze food packaging, extract nutritional data, and audit for compliance and health warnings.

FoodTruth

FoodTruth is an AI-powered application that analyzes food packaging images (front and back) to extract nutritional information and ingredients. It uses the Gemini API for precise data extraction and runs comprehensive audits against predefined rules to verify compliance and highlight potential health warnings.

🚀 Features

Automated Data Extraction: Upload images of food labels, and the system automatically extracts nutrition facts and ingredient lists using Google's Gemini models.
Rule-Based Auditing: Runs safety and compliance checks on the extracted data, identifying things like hidden sugars, allergens, or misleading claims.
Interactive UI: A modern React frontend built with Vite and Tailwind CSS to easily review results.
Demo Mode: Includes pre-configured demo samples for quick testing.

🛠️ Tech Stack

Frontend: React 19, TypeScript, Vite, Tailwind CSS v4
Backend: Python, FastAPI, Uvicorn
AI/ML: Google GenAI (Gemini)

📋 Prerequisites

Node.js (v18 or higher)
Python (v3.10 or higher)
Google…

View on GitHub

Key Architectural Highlights

FoodTruth is architected into three clean decoupled layers:

Frontend: React 19, TypeScript, Tailwind CSS v4, dynamic camera capture stream via navigator.mediaDevices.
Backend: FastAPI (Python 3.10+), Pydantic v2 strict schema validation, asynchronous image streaming.
AI Vision & Audit Pipeline: Google Gemma open-weight models (gemma-4-26b-a4b-it / gemma-4-31b-it) extracting structured entities into Pydantic models, fed into a deterministic regulatory engine.

1. The Pydantic Extraction Schema (`backend/models.py`)

class ExtractionData(BaseModel):
    product_name: str
    serving_size: Optional[ServingSize] = Field(default_factory=ServingSize)
    front_claims: List[str]
    macros_per_serving: Optional[MacrosPerServing] = Field(default_factory=MacrosPerServing)
    ingredients_raw: List[str]
    parenthetical_sub_ingredients: List[ParentheticalSubIngredient]
    disease_or_medicinal_claims: List[str] = Field(default_factory=list)
    has_nutrition_table: bool
    has_ingredient_list: bool

2. The "Protein Sprinkle Trick" & Sweetener Detection Algorithm (`backend/rules.py`)

# Detect if a brand claims 'Whey Protein' on front, but buries it behind cheap fillers
def audit_protein(data: ExtractionData) -> ProteinAudit:
    ingredients = [i.lower().strip() for i in data.ingredients_raw]

    first_tier1_idx = -1
    for idx, ing in enumerate(ingredients):
        for t1 in PROTEIN_TIERS["TIER_1"]["sources"]:
            if t1 in ing:
                if first_tier1_idx == -1:
                    first_tier1_idx = idx
                break

    front_claims_lower = [c.lower() for c in data.front_claims]
    claims_whey = any("whey" in c for c in front_claims_lower)

    # In food labeling, ingredients are listed by descending weight.
    # If Whey is claimed on front but appears past index 2, it is a sprinkle!
    sprinkle_trick = claims_whey and (first_tier1_idx > 2 or first_tier1_idx == -1)

    return ProteinAudit(
        declared_g=data.macros_per_serving.protein_g or 0.0,
        source_tier=top_tier,
        true_source=true_source,
        sprinkle_trick_detected=sprinkle_trick
    )

How I Built It

The Open-Source AI Stack

FoodTruth is built around Gemma open-weight models (gemma-4-26b-a4b-it and gemma-4-31b-it), Google's family of lightweight, state-of-the-art open models.

Why Open-Weight Vision Models?

Analyzing food packaging is notoriously difficult for classical OCR:

Packaged labels are curved, glossy, wrinkled, or photographed under poor grocery fluorescent lighting.
Nutrition facts exist in non-standard tabular grids, dual columns (per 100g vs per serving), and multi-language panels.
Ingredients are packed into dense, micro-font comma-separated lists with parenthetical sub-ingredients (e.g. "Compound Chocolate [Sugar, Hydrogenated Vegetable Fat, Cocoa Solids, Emulsifier (INS 322)]").

Gemma's multimodal architecture performs holistic scene understanding: it locates both panels across arbitrary image orientations, transcribes ingredient text verbatim, normalizes nutritional metrics to grams, and outputs strict JSON matching our Pydantic schema in a single pass.

Hybrid AI + Deterministic Rules Architecture

Instead of asking an LLM to hallucinate whether a product is "healthy" (which produces vague, non-reproducible answers), FoodTruth employs a neuro-symbolic pipeline:

Perception Layer (Gemma): Performs vision OCR and extracts ground-truth product facts (exact ingredient sequence, front claim quotes, declared macro numbers).
Audit Layer (Python Deterministic Engine): Evaluates the extracted entities against an immutable glycemic index database (SWEETENER_DB), bioavailability tables (PROTEIN_TIERS), and statutory regex patterns (DISEASE_CLAIM_PATTERNS).
Verdict Generation: Produces explainable, reproducible scores that can stand up to regulatory scrutiny.

Why Does Open Innovation Matter?

1. Sovereignty Over Personal Health Telemetry

What you eat and the medical conditions you manage (Type 2 diabetes, insulin resistance, celiac disease, hypertension) represent some of the most sensitive personal data that exists. Closed, proprietary AI platforms log every prompt and image scan to enrich user ad-tracking profiles. Open-weight models like Gemma can run locally on edge hardware or private self-hosted servers, ensuring zero health telemetry leaks to third parties.

2. Impartiality and Truth Without Corporate Censorship

Closed foundation models are operated by massive cloud conglomerates with deep commercial ties to multinational food and beverage advertisers. Can consumers truly trust a closed-source model's nutritional advice when proprietary guardrails might quietly downplay the harms of ultra-processed food formulations?

Open innovation guarantees algorithmic transparency. Every prompt, every weight tensor, and every rule in FoodTruth is public, auditable by independent nutrition scientists, and immune to corporate lobbying.

3. Democratizing Preventive Healthcare in Emerging Markets

Countries like India, Mexico, and Brazil are facing catastrophic surges in lifestyle diseases and diabetes fueled by ultra-processed food marketing. Closed API token fees ($0.03–$0.05 per multimodal call) make consumer scanning tools economically unviable for everyday shoppers in emerging economies. Open-weight models empower developers to run batch inference or deploy quantised models on community servers at near-zero marginal cost.

My Agent Session

This project was developed end-to-end with Antigravity, Google's agentic coding assistant. The agent session encompassed:

Designing the dual-panel OCR prompt and Pydantic validation schema.
Implementing the glycemic risk and "protein sprinkle trick" heuristic engines.
Refactoring the frontend into a responsive, editorial interface with mobile camera viewfinder integration.
Resolving edge cases with non-standard nutritional tables and missing panels.

You can inspect the agent transcript through DevRelay:
{% agent_session a56d77e3-18a5-48b8-b5ba-c0c56fea78a7 %}

My First User

During development, I tested FoodTruth with a friend who is actively managing pre-diabetes and trying to maintain a high-protein calorie deficit for weight loss.

We scanned a popular supermarket "High Protein Wafer" claiming "10g Whey Protein" and "No Added Sugar".

The Audit Results Shocked Him:

The Protein Lie: Whey protein concentrate was placed 5th in the ingredient list — behind wheat flour (maida), maltitol, soy protein isolate, and hydrogenated vegetable oil. The front claim of "Whey" was pure marketing dilution.
The Sweetener Trap: The product contained 12g of Maltitol, which triggers gastrointestinal distress and has a glycemic index that still impacts blood glucose.
The Illegal Claim: The label claimed the product was "Clinically proven to reverse diabetes" — a direct violation of FSSAI advertising standards.

His Reaction:

"I've literally been eating two of these a day thinking I was hitting my protein goals and protecting my blood sugar. Finding out that the whey is basically just sprinkled on top of maida and vegetable oil is infuriating. If this were an app on my phone, I'd easily pay $5 a month just to scan items in the grocery aisle before buying them."

That feedback confirmed the side hustle thesis: consumers don't want generic calorie counters; they want a ruthless advocate that exposes label deception.

Built with passion for food transparency and open-source innovation during Hacktoberfest 2026.

FoodTruth: Exposing Packaged Food Deception with Open-Weight Vision AI

Souvik Das — Thu, 15 Oct 2026 02:46:56 +0000

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle

What I Built

Hidden Sugars: Products marked "No Added Sugar" routinely contain Maltodextrin (with a Glycemic Index of 110, spiking blood sugar faster than pure glucose), liquid glucose, or laxative doses of polyols like Maltitol.
The "Protein Sprinkle" Trick: Snack bars claim "Rich in Whey Protein" in bold front lettering, yet their ingredient lists place Whey Isolate behind maida (refined wheat flour), sugar, palm oil, and soy flour — merely sprinkling it in to satisfy legal claim thresholds.
Unlawful Disease Claims: Brands blatantly violate food safety regulations (such as FSSAI Section 24 and FDA guidelines) by claiming their snack or shake can "cure diabetes" or "control hypertension."

       Front Branding                  Back Ingredients & Nutrition
  [ "15g Whey Protein" ]           [ Sugar, Palm Oil, Whey Isolate (2%) ]
             \                                      /
              \                                    /
               ▼                                  ▼
         ┌──────────────────────────────────────────────┐
         │          FoodTruth AI Audit Engine           │
         │   • Gemma Open Vision Schema Extraction      │
         │   • Sweetener Glycemic Index (GI) Matrix     │
         │   • DIAAS Protein Bioavailability & Dilution │
         │   • P:Cal Metabolic Efficiency Yield         │
         │   • FSSAI / FDA Regulatory Claims Audit      │
         └──────────────────────────────────────────────┘
                                │
                                ▼
         ┌──────────────────────────────────────────────┐
         │             Actionable Verdict               │
         │   "Protein Sprinkle Trick Detected"          │
         │   Optimized For: Quick pre-workout carbs     │
         │   Avoid If: Diabetic / Strict Calorie Deficit│
         └──────────────────────────────────────────────┘

Who Would Pay For It? (The Side Hustle Business Model)

FoodTruth is built with a direct commercial monetization pathway:

B2C Micro-SaaS (Consumer Grocery Companion): A mobile web app / PWA subscription ($4.99/month or $39/year) targeting diabetics, keto athletes, parents, and health-conscious consumers who want a 2-second barcode/packaging camera scan while walking supermarket aisles.
Pro Coach / Dietitian Pantry Audits ($19/mo per seat): Fitness trainers, dietitians, and functional medicine practitioners who audit clients' pantry items and generate branded, PDF-exportable nutritional truth reports.
B2B Pre-Print Packaging Compliance API ($49/audit or $199/mo): Emerging D2C clean-label food brands and packaging design agencies need to pre-audit their label artwork against FSSAI and FDA advertising regulations before committing thousands of dollars to physical printing and risking regulatory notices or product recalls.

Demo

Live Repository: https://github.com/dasouvik122005/FoodTruth
Local Interactive Setup: Clone the repo, launch FastAPI (python main.py) and Vite (npm run dev), then test live packaging scans or choose from pre-loaded real-world audit payloads (Yoga Bar, The Whole Truth, and SuperYou Wafer).

User Interface Highlights

Bespoke Editorial Aesthetic: Designed with an editorial high-contrast light layout, split-screen desktop experience, and fluid mobile-first ergonomics.
In-App Camera Viewfinder: Integrated mobile camera capture with canvas-based shutter action for instant grocery shelf snapping.
Deterministic Metric Cards:
- Metabolic Sugar Risk: High/Moderate/Low risk calculation based on real Glycemic Index (GI) database mapping.
- Protein Quality Tier: Classifies proteins by DIAAS bioavailability (Tier 1 Whey Isolate down to Tier 3 Wheat Gluten/Flour fillers).
- Calorie Yield (P:Cal): Calculates what percentage of total calories actually comes from bioavailable protein vs caloric padding.
- FSSAI Violation Alerts: Real-time legal citations identifying specific regulatory breaches.

Code

The entire codebase is open-source under the MIT license at GitHub:

dasouvik122005 / FoodTruth

FoodTruth uses Gemma to analyze food packaging, extract nutritional data, and audit for compliance and health warnings.

FoodTruth

🚀 Features

Automated Data Extraction: Upload images of food labels, and the system automatically extracts nutrition facts and ingredient lists using Google's Gemini models.
Rule-Based Auditing: Runs safety and compliance checks on the extracted data, identifying things like hidden sugars, allergens, or misleading claims.
Interactive UI: A modern React frontend built with Vite and Tailwind CSS to easily review results.
Demo Mode: Includes pre-configured demo samples for quick testing.

🛠️ Tech Stack

Frontend: React 19, TypeScript, Vite, Tailwind CSS v4
Backend: Python, FastAPI, Uvicorn
AI/ML: Google GenAI (Gemini)

📋 Prerequisites

Node.js (v18 or higher)
Python (v3.10 or higher)
Google…

View on GitHub

Key Architectural Highlights

FoodTruth is architected into three clean decoupled layers:

Frontend: React 19, TypeScript, Tailwind CSS v4, dynamic camera capture stream via navigator.mediaDevices.
Backend: FastAPI (Python 3.10+), Pydantic v2 strict schema validation, asynchronous image streaming.
AI Vision & Audit Pipeline: Google Gemma open-weight models (gemma-4-26b-a4b-it / gemma-4-31b-it) extracting structured entities into Pydantic models, fed into a deterministic regulatory engine.

1. The Pydantic Extraction Schema (`backend/models.py`)

class ExtractionData(BaseModel):
    product_name: str
    serving_size: Optional[ServingSize] = Field(default_factory=ServingSize)
    front_claims: List[str]
    macros_per_serving: Optional[MacrosPerServing] = Field(default_factory=MacrosPerServing)
    ingredients_raw: List[str]
    parenthetical_sub_ingredients: List[ParentheticalSubIngredient]
    disease_or_medicinal_claims: List[str] = Field(default_factory=list)
    has_nutrition_table: bool
    has_ingredient_list: bool

2. The "Protein Sprinkle Trick" & Sweetener Detection Algorithm (`backend/rules.py`)

# Detect if a brand claims 'Whey Protein' on front, but buries it behind cheap fillers
def audit_protein(data: ExtractionData) -> ProteinAudit:
    ingredients = [i.lower().strip() for i in data.ingredients_raw]

    first_tier1_idx = -1
    for idx, ing in enumerate(ingredients):
        for t1 in PROTEIN_TIERS["TIER_1"]["sources"]:
            if t1 in ing:
                if first_tier1_idx == -1:
                    first_tier1_idx = idx
                break

    front_claims_lower = [c.lower() for c in data.front_claims]
    claims_whey = any("whey" in c for c in front_claims_lower)

    # In food labeling, ingredients are listed by descending weight.
    # If Whey is claimed on front but appears past index 2, it is a sprinkle!
    sprinkle_trick = claims_whey and (first_tier1_idx > 2 or first_tier1_idx == -1)

    return ProteinAudit(
        declared_g=data.macros_per_serving.protein_g or 0.0,
        source_tier=top_tier,
        true_source=true_source,
        sprinkle_trick_detected=sprinkle_trick
    )

How I Built It

The Open-Source AI Stack

FoodTruth is built around Gemma open-weight models (gemma-4-26b-a4b-it and gemma-4-31b-it), Google's family of lightweight, state-of-the-art open models.

Why Open-Weight Vision Models?

Analyzing food packaging is notoriously difficult for classical OCR:

Packaged labels are curved, glossy, wrinkled, or photographed under poor grocery fluorescent lighting.
Nutrition facts exist in non-standard tabular grids, dual columns (per 100g vs per serving), and multi-language panels.
Ingredients are packed into dense, micro-font comma-separated lists with parenthetical sub-ingredients (e.g. "Compound Chocolate [Sugar, Hydrogenated Vegetable Fat, Cocoa Solids, Emulsifier (INS 322)]").

Hybrid AI + Deterministic Rules Architecture

Instead of asking an LLM to hallucinate whether a product is "healthy" (which produces vague, non-reproducible answers), FoodTruth employs a neuro-symbolic pipeline:

Perception Layer (Gemma): Performs vision OCR and extracts ground-truth product facts (exact ingredient sequence, front claim quotes, declared macro numbers).
Audit Layer (Python Deterministic Engine): Evaluates the extracted entities against an immutable glycemic index database (SWEETENER_DB), bioavailability tables (PROTEIN_TIERS), and statutory regex patterns (DISEASE_CLAIM_PATTERNS).
Verdict Generation: Produces explainable, reproducible scores that can stand up to regulatory scrutiny.

Why Does Open Innovation Matter?

1. Sovereignty Over Personal Health Telemetry

2. Impartiality and Truth Without Corporate Censorship

3. Democratizing Preventive Healthcare in Emerging Markets

My Agent Session

This project was developed end-to-end with Antigravity, Google's agentic coding assistant. The agent session encompassed:

Designing the dual-panel OCR prompt and Pydantic validation schema.
Implementing the glycemic risk and "protein sprinkle trick" heuristic engines.
Refactoring the frontend into a responsive, editorial interface with mobile camera viewfinder integration.
Resolving edge cases with non-standard nutritional tables and missing panels.

You can inspect the agent transcript through DevRelay:
{% agent_session a56d77e3-18a5-48b8-b5ba-c0c56fea78a7 %}

My First User

During development, I tested FoodTruth with a friend who is actively managing pre-diabetes and trying to maintain a high-protein calorie deficit for weight loss.

We scanned a popular supermarket "High Protein Wafer" claiming "10g Whey Protein" and "No Added Sugar".

The Audit Results Shocked Him:

The Protein Lie: Whey protein concentrate was placed 5th in the ingredient list — behind wheat flour (maida), maltitol, soy protein isolate, and hydrogenated vegetable oil. The front claim of "Whey" was pure marketing dilution.
The Sweetener Trap: The product contained 12g of Maltitol, which triggers gastrointestinal distress and has a glycemic index that still impacts blood glucose.
The Illegal Claim: The label claimed the product was "Clinically proven to reverse diabetes" — a direct violation of FSSAI advertising standards.

His Reaction:

"I've literally been eating two of these a day thinking I was hitting my protein goals and protecting my blood sugar. Finding out that the whey is basically just sprinkled on top of maida and vegetable oil is infuriating. If this were an app on my phone, I'd easily pay $5 a month just to scan items in the grocery aisle before buying them."

That feedback confirmed the side hustle thesis: consumers don't want generic calorie counters; they want a ruthless advocate that exposes label deception.

Built with passion for food transparency and open-source innovation during Hacktoberfest 2026.

Genuire

Souvik Das — Sun, 11 Oct 2026 18:18:25 +0000

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass

What I Built

Genuire is an advanced, real-time AI/ML platform designed to protect job seekers from fraudulent employment opportunities. I built this for a close friend who is actively navigating the difficult job market and recently encountered a highly sophisticated employment scam.

Genuire solves this problem by actively auditing job descriptions, verifying URLs and corporate registrations, and analyzing recruiter contact channels. It acts as a shield, calculating a comprehensive Trust Score (0-100%) using a 5-Pillar Trust Engine before the applicant submits any sensitive data, ensuring they don't fall victim to phishing or identity theft.

Demo

genuire.onrender.com

Code

rashmi-crypto / Genuire

Genuire: Fake Job Posting Detection Platform

Genuire is an advanced, real-time AI/ML platform designed to protect job seekers from fraudulent employment opportunities. By actively auditing job descriptions, verifying URLs and corporate registrations, and analyzing recruiter contact channels, Genuire calculates a comprehensive Trust Score (0-100%) before applicants submit their sensitive data.

✨ Key Features

Real-Time Threat Detection: Instantaneous analysis of job postings to identify potential scams.
Multimodal Gemma Analysis: Uses Google's gemma-4-31b-it vision models to scan uploaded screenshots of job postings for visual and textual fraud indicators.
Smart URL Scraping: Automatically fetches and populates job details (title, company, description) from major job boards for seamless analysis.
5-Pillar Trust Engine: Employs a multi-faceted verification pipeline incorporating machine learning and heuristic risk metrics.
Premium Visual Dashboard: A sleek, responsive user interface featuring a dynamic horizontal pipeline tracker, automatic Light Mode, and detailed trust telemetry.

🛠️ System Architecture

Genuire…

View on GitHub

How I Built It

Genuire is built around a hybrid AI architecture. It leverages Google's open-weights gemma-4-31b-it vision model to power its multimodal analysis. When a user uploads a screenshot of a suspicious job posting, Gemma scans it for visual and textual fraud indicators.

The open-source AI is complemented by a high-performance scikit-learn machine learning pipeline. It uses an SGDClassifier for NLP text analysis and a RandomForestClassifier for tabular risk data (like location threat ratios and character lengths). The application is glued together using a Flask backend and a responsive Vanilla JS/CSS frontend featuring a dynamic 7-stage verification pipeline tracker.

Why Does Open Innovation Matter?

Open innovation is crucial for security and fraud-prevention tools. By utilizing open-weights models like Gemma and open-source libraries like scikit-learn, developers can completely scrutinize the model's biases and fine-tune its capabilities specifically for threat detection without relying on a black box.

A closed API would make it difficult to transparently explain why a particular job posting was flagged or approved. Open innovation made it possible to build a transparent "Trust Engine" where the user can see exactly which of the 5 pillars passed or failed, giving them the confidence and context they need to make safe career decisions.

My Agent Session

For this project, I pair-programmed extensively with my AI coding agent (Google Gemini/Antigravity). The agent was instrumental in helping me architect the 5-Pillar Trust Engine, fine-tune the scikit-learn models for better accuracy, and rapidly prototype the Flask backend. We also collaborated closely to polish the CSS for the dynamic horizontal pipeline tracker on the frontend, turning a complex ML backend into a highly premium, user-friendly dashboard!

Prize Categories

Gemma - Best Use of Gemma: Genuire integrates Google's open-weight gemma-4-31b-it model to conduct advanced multimodal image analysis on job posting screenshots, extracting visual and textual fraud indicators.
Render - Best Use of Render: The entire Genuire Flask application and AI runtime is configured for seamless deployment and hosting on Render, as defined by our render.yaml configuration.
Entire - Best Use of Entire: As detailed in the 'My Agent Session' section above, I utilized an AI coding agent to architect the core Trust Engine and build out the frontend styling, sharing that session directly in this write-up.

Team Members

A huge thanks to my amazing teammates who helped bring Genuire!

@rashmipyne

Genuire: An AI-Powered Job Scam Detection Platform

Souvik Das — Mon, 05 Oct 2026 04:32:55 +0000

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

What I Built

Demo

genuire.onrender.com

Code

rashmi-crypto / Genuire

Genuire: Fake Job Posting Detection Platform

✨ Key Features

Real-Time Threat Detection: Instantaneous analysis of job postings to identify potential scams.
Multimodal Gemma Analysis: Uses Google's gemma-4-31b-it vision models to scan uploaded screenshots of job postings for visual and textual fraud indicators.
Smart URL Scraping: Automatically fetches and populates job details (title, company, description) from major job boards for seamless analysis.
5-Pillar Trust Engine: Employs a multi-faceted verification pipeline incorporating machine learning and heuristic risk metrics.
Premium Visual Dashboard: A sleek, responsive user interface featuring a dynamic horizontal pipeline tracker, automatic Light Mode, and detailed trust telemetry.

🛠️ System Architecture

Genuire…

View on GitHub

How I Built It

Why Does Open Innovation Matter?

My Agent Session

Prize Categories

Gemma - Best Use of Gemma: Genuire integrates Google's open-weight gemma-4-31b-it model to conduct advanced multimodal image analysis on job posting screenshots, extracting visual and textual fraud indicators.
Render - Best Use of Render: The entire Genuire Flask application and AI runtime is configured for seamless deployment and hosting on Render, as defined by our render.yaml configuration.
Entire - Best Use of Entire: As detailed in the 'My Agent Session' section above, I utilized an AI coding agent to architect the core Trust Engine and build out the frontend styling, sharing that session directly in this write-up.

Team Members

A huge thanks to my amazing teammates who helped bring Genuire!

@rashmipyne

DEV Community: Souvik Das

FoodTruth: Exposing Packaged Food Deception with Open-Weight Vision AI

What I Built

Who Would Pay For It? (The Side Hustle Business Model)

Demo

User Interface Highlights

Code

dasouvik122005 / FoodTruth

FoodTruth uses Gemma to analyze food packaging, extract nutritional data, and audit for compliance and health warnings.

FoodTruth

🚀 Features

🛠️ Tech Stack

📋 Prerequisites

Key Architectural Highlights

1. The Pydantic Extraction Schema (backend/models.py)

2. The "Protein Sprinkle Trick" & Sweetener Detection Algorithm (backend/rules.py)

How I Built It

The Open-Source AI Stack

Why Open-Weight Vision Models?

Hybrid AI + Deterministic Rules Architecture

Why Does Open Innovation Matter?

1. Sovereignty Over Personal Health Telemetry

2. Impartiality and Truth Without Corporate Censorship

3. Democratizing Preventive Healthcare in Emerging Markets

My Agent Session

My First User

FoodTruth: Exposing Packaged Food Deception with Open-Weight Vision AI

What I Built

Who Would Pay For It? (The Side Hustle Business Model)

Demo

User Interface Highlights

Code

dasouvik122005 / FoodTruth

FoodTruth uses Gemma to analyze food packaging, extract nutritional data, and audit for compliance and health warnings.

FoodTruth

🚀 Features

🛠️ Tech Stack

📋 Prerequisites

Key Architectural Highlights

1. The Pydantic Extraction Schema (backend/models.py)

2. The "Protein Sprinkle Trick" & Sweetener Detection Algorithm (backend/rules.py)

How I Built It

The Open-Source AI Stack

Why Open-Weight Vision Models?

Hybrid AI + Deterministic Rules Architecture

Why Does Open Innovation Matter?

1. Sovereignty Over Personal Health Telemetry

2. Impartiality and Truth Without Corporate Censorship

3. Democratizing Preventive Healthcare in Emerging Markets

My Agent Session

My First User

Genuire

What I Built

Demo

Code

rashmi-crypto / Genuire

Genuire: Fake Job Posting Detection Platform

✨ Key Features

🛠️ System Architecture

How I Built It

Why Does Open Innovation Matter?

My Agent Session

Prize Categories

Team Members

Genuire: An AI-Powered Job Scam Detection Platform

What I Built

Demo

Code

rashmi-crypto / Genuire

Genuire: Fake Job Posting Detection Platform

✨ Key Features

🛠️ System Architecture

How I Built It

Why Does Open Innovation Matter?

My Agent Session

Prize Categories

Team Members

1. The Pydantic Extraction Schema (`backend/models.py`)

2. The "Protein Sprinkle Trick" & Sweetener Detection Algorithm (`backend/rules.py`)

1. The Pydantic Extraction Schema (`backend/models.py`)

2. The "Protein Sprinkle Trick" & Sweetener Detection Algorithm (`backend/rules.py`)