Contribution Guidelines

I need food labels to test my project. Contributors are requested to use the main branch and add only food label image with nutrition facts.
Changes to the README or Code will not be counted as valid contributions. Your contributions are highly appreciated!

Dataset Contribution Format

• File name format:
  • itemname.jpg
  • itemname.jpeg
  • itemname.webp
• All images must be placed inside the dataset folder before adding.

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