InBody to SQLite

A Python tool that processes InBody body composition scan images using OpenAI GPT Vision to extract structured data and store it in a local SQLite database. Now includes a PDF Report Generator for beautiful progress tracking!

Features

Core Features

• AI-powered text extraction: Uses OpenAI GPT-4 Vision to analyze InBody scan images
• Unified data model: Combines data from different InBody machine models into a single schema
• Smart caching: Avoids reprocessing the same images using SHA-256 file hashes
• SQLite storage: Stores all extracted data in a local SQLite database
• Multi-language support: Handles InBody scans in different languages (English, Polish, etc.)
• Structured output: Uses JSON schema validation for consistent data extraction
• Batch processing: Process entire folders of images at once
• Export functionality: Export results to JSON for analysis

🆕 NEW: PDF Report Generator

• 📊 Beautiful Charts: Generate colorful progress reports with multiple chart types
• 📅 Time Range Filtering: Create reports for any date range (entire history by default)
• 🎨 Professional Design: High-quality PDF reports with modern styling
• 💡 Smart Insights: Automatic analysis of your fitness progress and achievements
• 🔧 Multiple Interfaces: Both command-line tool and Python API
• 📈 Comprehensive Analysis: Weight, body composition, health metrics, and segmental analysis

Installation

1. Clone the repository:

   git clone <repository-url>
   cd inbody-to-sqlite

2. Install dependencies using UV:

   uv sync

3. Set up environment variables:

   cp .env.example .env
   # Edit .env and add your OpenAI API key

Configuration

You need an OpenAI API key to use GPT-4 Vision. Get one from OpenAI Platform.

Create a .env file:

OPENAI_API_KEY=your-api-key-here

Usage

Core Data Processing

Process a folder of InBody images:

python -m inbody_to_sqlite /path/to/images

🆕 PDF Report Generation

Generate beautiful progress reports from your processed data:

# Generate a complete report
uv run python -m inbody_reports

# Custom report with title and filename
uv run python -m inbody_reports --output "my_fitness_journey.pdf" --title "My Progress Report"

# Report for specific date range
uv run python -m inbody_reports --start-date 2024-01-01 --end-date 2024-12-31

# Check available data
uv run python -m inbody_reports --list-data-range

Options:

• --force: Reprocess images even if already cached
• --export: Export results to JSON after processing
• --export-only: Only export existing results, don't process new images

Examples:

# Process all images in a folder
python -m inbody_to_sqlite ./my_inbody_scans

# Force reprocess all images and export results
python -m inbody_to_sqlite ./my_inbody_scans --force --export

# Export existing results only
python -m inbody_to_sqlite ./my_inbody_scans --export-only

Python API

Data Processing

from inbody_to_sqlite.main import InBodyProcessor

# Initialize processor
processor = InBodyProcessor(api_key="your-api-key")

# Process a folder
stats = processor.process_folder("/path/to/images")

# Export results
output_file = processor.export_results()

🆕 Report Generation

from inbody_reports import InBodyReportGenerator
from datetime import datetime, timedelta

# Initialize report generator
generator = InBodyReportGenerator('inbody_results.db')

# Generate complete report
report_path = generator.generate_report(
    output_path="progress_report.pdf",
    title="My Fitness Journey"
)

# Generate report for specific time range
six_months_ago = datetime.now() - timedelta(days=180)
recent_report = generator.generate_report(
    start_date=six_months_ago,
    title="Recent Progress"
)

Supported Data Fields

The tool extracts and unifies the following data from InBody scans:

Basic Information

• scan_date: Date and time of the scan
• height: Height in cm
• weight: Weight in kg
• age: Age in years
• gender: Gender (Male/Female)

Body Composition

• muscle_mass: Total muscle mass in kg
• body_fat_mass: Total body fat mass in kg
• body_fat_percentage: Body fat percentage
• total_body_water: Total body water in liters
• fat_free_mass: Fat-free mass in kg

Health Metrics

• bmi: Body Mass Index
• bmr: Basal Metabolic Rate in kcal
• visceral_fat_level: Visceral fat level
• pbf: Percent body fat
• whr: Waist-hip ratio

Scores & Recommendations

• inbody_score: InBody score
• fitness_score: Fitness score
• muscle_control: Muscle control recommendation in kg
• fat_control: Fat control recommendation in kg

Segmental Analysis

• segmental: Body part analysis for arms, trunk, and legs
  • Lean mass for each body part
  • Fat mass for each body part

Database Schema

The tool creates two main tables:

processed_images

• Tracks which images have been processed
• Stores file hash for cache validation
• Records processing success/failure

inbody_results

• Stores extracted InBody data
• Links to processed images via file hash
• Contains all the structured data fields

Supported Image Formats

• JPEG (.jpg, .jpeg)
• PNG (.png)
• BMP (.bmp)
• TIFF (.tiff)
• WebP (.webp)

InBody Models Supported

The tool has been tested with multiple InBody models and handles:

• Different languages (English, Polish, etc.)
• Varying data layouts

PDF Report Features

The new report generator creates comprehensive PDF reports with:

📊 Chart Types

• Weight Progression: Timeline with trend analysis
• Body Composition: Muscle vs fat mass over time
• Health Metrics: BMI, BMR, visceral fat levels
• Segmental Analysis: Body part measurements
• Progress Dashboard: Key metrics overview
• Before/After Comparison: First vs latest scans

🎨 Report Sections

1. Summary Dashboard - Key progress indicators
2. Weight Progression - Detailed weight analysis with trends
3. Body Composition - Muscle and fat mass changes
4. Health Metrics - BMI, BMR, visceral fat tracking
5. Progress Comparison - First vs latest scan analysis
6. Segmental Analysis - Body part distribution
7. Insights & Recommendations - AI-generated progress insights

📈 Sample Results

Your reports will show insights like:

• 📉 Weight decreased by 17.6kg over 773 days
• 💪 Body fat decreased by 14.0% - Great progress!
• ✅ Current BMI is in the healthy range
• 👍 Good tracking consistency - 19 scans over 773 days
• Different measurement units (automatically normalized)
• Missing fields in different models

Error Handling

• Caching: Images are cached by hash to avoid reprocessing
• Validation: Pydantic models ensure data consistency
• Error logging: Failed extractions are logged with error messages
• Graceful degradation: Missing fields are handled as optional

Examples

Processing Results

🔍 Scanning folder: ./images
📁 Found 5 image files

[1/5] 🔍 Processing inbody_scan_1.jpg...
✅ Successfully processed inbody_scan_1.jpg
   Date: 2023-06-03 06:53:00
   Weight: 93.4kg, Height: 181.0cm
   BMI: 28.5

[2/5] ⏭️  Skipping inbody_scan_2.jpg (already processed)

==================================================
📊 Processing Summary:
   Total files: 5
   Successfully processed: 3
   Failed: 1
   Skipped (cached): 1

📈 Database Statistics:
   Total processed images: 10
   Successful extractions: 9
   Failed extractions: 1
   Total InBody results: 9

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

License

This project is licensed under the MIT License.

Troubleshooting

Common Issues

1. "OPENAI_API_KEY environment variable is required"

   • Make sure you've created a .env file with your OpenAI API key

2. "No image files found"

   • Check that your folder contains supported image formats
   • Verify the folder path is correct

3. Processing failures

   • Check your OpenAI API key is valid and has sufficient credits
   • Ensure images are clear and readable InBody scans

Getting Help

• Check the processing logs for specific error messages
• Verify your OpenAI API key has access to GPT-4 Vision
• Ensure images are high quality and clearly show InBody data