TruthInBite 🍎

AI-Powered Food Label Analyzer with Ingredient-Based Health Scoring

TruthInBite is an intelligent web application that analyzes food product labels using AI-powered OCR and provides comprehensive health insights based on ingredient quality, WHO compliance, and personalized health conditions.

✨ Key Features

🔍 Advanced Food Label Analysis

• AI-Powered OCR: Extract complete ingredient lists and nutrition facts from any food label image
• Ingredient Quality Scoring: 0-100 health score based on ingredient processing level and quality
• WHO Compliance Check: Separate analysis for WHO nutritional guidelines compliance
• Multi-format Support: Works with JPG, PNG, WebP image formats

🏥 Personalized Health Analysis

• 27+ Health Conditions: Support for diabetes, heart disease, allergies, dietary preferences, and more
• Indian Health Conditions: Specialized analysis for PCOD/PCOS, Jain dietary restrictions
• Smart Warnings: Real-time alerts for harmful ingredients based on your health profile
• Age-Specific Analysis: Tailored recommendations for children, elderly, pregnancy, and breastfeeding

🌿 Healthy Alternatives

• Indian Food Focus: Suggests traditional Indian healthy alternatives
• Budget-Conscious: Filter alternatives by price range (budget-friendly to premium)
• Local Availability: Recommendations available in Indian markets
• Preparation Tips: Easy-to-follow preparation instructions for alternatives

🚀 Getting Started

Prerequisites

• Python 3.7 or higher
• Google AI API Key (Gemini)

Installation

1. Clone the repository

git clone https://github.com/vipu18/TruthInBite.git
cd TruthInBite

2. Install dependencies

pip install -r requirements.txt

3. Get Google AI Studio API Key

• Get your Google AI Studio API key by visiting ai.google.dev.
• Sign in with your Google account.
• Click"Get API key in Google AI Studio"
• Creating or selecting a Google Cloud project.
• Clicking "Create API key" to generate your free Gemini API key.

4. Set up environment variables Create a .env file in the root directory:

GEMINI_API_KEY="your_google_ai_api_key_here"

5. Run the application

streamlit run app.py

6. Access the app Open your browser and navigate to http://localhost:8501

🛠️ Technology Stack

• Frontend: Streamlit (Interactive web interface)
• AI/ML: Google Gemini 2.5 Flash (OCR and health analysis)
• Image Processing: Pillow (PIL) for image handling
• Data Processing: Pandas for structured data analysis
• Environment Management: python-dotenv for configuration

📊 How It Works

1. Image Upload & Processing

# Upload food label image (JPG, PNG, WebP)
uploaded_file = st.file_uploader("Choose a food label image...")
image = Image.open(uploaded_file)

2. AI-Powered Data Extraction

# Extract structured data using Gemini AI
product_list = get_structured_data_from_gemini(image)

3. Ingredient-Based Health Scoring

• High Scores (80-100): Natural, whole ingredients with minimal processing
• Medium Scores (50-79): Some processed but recognizable ingredients
• Low Scores (0-49): Highly processed with artificial additives

4. Personalized Health Analysis

# Analyze based on user's health conditions
warnings = run_health_analysis(product, health_profile)

🏥 Supported Health Conditions

TruthInBite analyzes products for 27+ health conditions:

Metabolic Conditions

• Diabetes Type 1, Type 2, Pre-Diabetes
• High Blood Pressure, Heart Disease, High Cholesterol
• Kidney Disease, Liver Disease, Thyroid Issues

Women's Health

• PCOD/PCOS, Pregnancy, Breastfeeding

Digestive Health

• Gastric Issues, IBS (Irritable Bowel Syndrome)

Allergies & Sensitivities

• Nut Allergy, Gluten Sensitivity, Lactose Intolerance
• Soy Allergy, Egg Allergy, Shellfish Allergy

Lifestyle & Age Groups

• Weight Management, Muscle Building
• Child (2-12 years), Elderly (60+)

Dietary Preferences

• Vegetarian, Vegan, Jain Food

📁 Project Structure

TruthInBite/
├── app.py                 # Main Streamlit application [17.6KB]
├── ai_functions.py        # AI/ML functions for analysis [8.1KB]
├── helper_functions.py    # Health analysis and utilities [10.3KB]
├── requirements.txt       # Python dependencies [63B]
├── .env                   # Environment variables (API keys) [56B]
├── .gitignore            # Git ignore file [15B]
└── README.md             # Project documentation

🔬 Core Functions

AI Functions

# Extract structured data from food labels
get_structured_data_from_gemini(pil_image)

# Get ingredient-based health scoring
get_ai_health_summary(product_data, health_profile)

# Suggest healthy Indian alternatives
get_healthy_alternatives(product_data, health_profile, budget_range)

Helper Functions

# Analyze health warnings based on conditions
run_health_analysis(product, health_profile)

# Calculate per-serving nutrition with WHO compliance
calculate_per_serve_nutrition(nutrition_per_100g, net_weight)

# Get color-coded health scores
get_health_score_color(score)

🎯 Use Cases

For Health-Conscious Consumers

• Analyze packaged foods before purchasing
• Understand ingredient quality and processing levels
• Get personalized warnings for health conditions

For People with Medical Conditions

• Diabetes management with sugar content analysis
• Heart disease monitoring with trans fat detection
• Allergy management with comprehensive allergen detection

For Indian Families

• Traditional dietary restriction compliance (Jain, Vegetarian, Vegan)
• Budget-friendly healthy alternatives in Indian markets
• Age-appropriate food selection for children and elderly

📈 Scoring Methodology

Ingredient Quality Score (0-100)

• Deductions: -10-20 points per artificial additive, -15-25 for trans fats
• Additions: +5-10 points for whole food ingredients, +5-15 for natural nutrients
• WHO Compliance: Separate check for sugar, sodium, saturated fat limits

Health Warnings

• Real-time analysis of 200+ harmful ingredient keywords
• Condition-specific messaging (e.g., "Contains sugars - monitor blood glucose carefully")
• Severity indicators with emoji coding 🚨

🚀 Future Enhancements

• Mobile App: React Native version for on-the-go scanning
• Barcode Integration: Quick product lookup via barcode scanning
• Offline Mode: Local processing for basic ingredient analysis
• Community Reviews: User-generated product ratings and reviews
• Recipe Suggestions: Healthy recipes using alternative ingredients
• Multi-language: Hindi, Tamil, Bengali interface support
• Export Reports: PDF health analysis reports for doctor consultations

🤝 Contributing

We welcome contributions! Here's how you can help:

1. Fork the Project
2. Create a Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit Changes (git commit -m 'Add AmazingFeature')
4. Push to Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Development Guidelines

• Follow PEP 8 Python style guidelines
• Add unit tests for new features
• Update documentation for API changes
• Test with various food label formats

🔒 Privacy & Security

• No Data Storage: Images are processed in real-time and not stored
• API Security: Gemini API calls are secure and encrypted
• Local Processing: Health profile analysis happens locally
• Environment Variables: Sensitive API keys stored securely

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

👨‍💻 Author

Vipanshu Suman

• GitHub: @vipu18
• Project Link: TruthInBite

🙏 Acknowledgments

• Google AI for Gemini API integration
• WHO for nutritional guidelines and compliance standards
• Streamlit community for the excellent web framework
• Indian nutrition research for health condition mappings

📞 Support

For support, issues, or feature requests:

• Open an issue on GitHub
• Check existing documentation
• Review the troubleshooting guide