epump-inspector 1.0.1

ePump Inspector Library and CLI for Firestore database analysis

ePump Inspector

A Python library and CLI tool for inspecting and analyzing ePump.app Firestore database data. Designed for both command-line usage and programmatic access for machine learning and data analysis tasks.

Features

• 🔍 Database Inspection: Complete read-only access to Firestore database
• 📊 ML/Analysis Ready: Core library designed for machine learning workflows
• 🖥️ Command Line Interface: Convenient CLI for quick database queries
• 🎯 Type Safety: Full type hints and structured configuration
• 🔐 Security First: Comprehensive .gitignore patterns for credentials
• 📈 Performance: Efficient querying with configurable limits
• 🐍 Python 3.8+: Modern Python with Poetry package management

Installation

Using Poetry (Recommended)

# Install dependencies
poetry install

# Run CLI commands
poetry run epump-inspector users

# Or activate the virtual environment
poetry shell
epump-inspector users

Using pip

pip install -e .
epump-inspector users

Configuration

Environment Variables

Create a .env file in the project root:

FIREBASE_PROJECT_ID=epump-e3713
FIREBASE_CREDENTIALS=/path/to/service-account.json
DEFAULT_LIMIT=10
LOG_LEVEL=warning

Firebase Setup

1. Go to Firebase Console
2. Select your project (epump-e3713)
3. Go to Project Settings → Service Accounts
4. Generate new private key
5. Save JSON file and set FIREBASE_CREDENTIALS path

CLI Usage

Basic Commands

# Get all users
epump-inspector users

# Get comprehensive user data (perfect for ML)
epump-inspector user-data --user-email [e-mail]

# Get system summary
epump-inspector summary --limit 5

# Get workouts for specific user
epump-inspector workouts --user-email [e-mail]

# Get API keys status
epump-inspector api-keys

# Pretty print JSON output
epump-inspector users --pretty

Advanced Commands

# Get specific workout analysis
epump-inspector workout-analyses --user-email [e-mail] --workout-id WORKOUT_ID

# Get AI feedback data
epump-inspector ai-feedback --user-email [e-mail]

# Check migration status
epump-inspector migration-status --user-email [e-mail]

# Generic collection access
epump-inspector collection --path "users/[e-mail]/workout_sessions"

Library Usage

Basic Example

from epump_inspector import DatabaseInspector, InspectorConfig

# Using environment configuration
inspector = DatabaseInspector()

# Or custom configuration
config = InspectorConfig(
    project_id="epump-e3713",
    credentials_path="/path/to/credentials.json",
    default_limit=20,
    log_level="info"
)
inspector = DatabaseInspector(config)

# Use context manager for automatic connection handling
with inspector.connection():
    users = inspector.get_users(limit=10)
    workouts = inspector.get_workouts(user_email="[e-mail]")
    
    print(f"Found {len(users)} users and {len(workouts)} workouts")

ML/Data Analysis Example

from epump_inspector import DatabaseInspector

inspector = DatabaseInspector()

with inspector.connection():
    # Get comprehensive user data for ML analysis
    user_data = inspector.get_user_data(
        user_email="[e-mail]",
        include_analyses=True  # Include workout analyses and AI suggestions
    )
    
    # Extract training data for AI models
    ai_feedback = inspector.get_ai_feedback(limit=1000)
    workout_analyses = inspector.get_workout_analyses(limit=1000)
    
    # Get system overview
    summary = inspector.get_all_users_summary(limit=100)
    
    # User data contains:
    # - workouts, supplements, diets
    # - health_goals, workout_goals, unified_goals
    # - ai_feedback, workout_analyses, ai_suggestions
    # - fitness_profile, active_diet
    # - goal_migration_completed status

Error Handling

from epump_inspector import DatabaseInspector, InspectorError, DataNotFoundError

inspector = DatabaseInspector()

try:
    with inspector.connection():
        # This will raise DataNotFoundError if not found
        workout = inspector.get_workout("[e-mail]", "nonexistent-id")
        
except DataNotFoundError as e:
    print(f"Data not found: {e}")
except InspectorError as e:
    print(f"Inspector error: {e}")
except Exception as e:
    print(f"Unexpected error: {e}")

API Reference

Core Classes

DatabaseInspector

Main class for database operations.

Key Methods:

• get_users(limit=10) - Get all users
• get_workouts(user_email=None, limit=10) - Get workouts
• get_user_data(user_email, include_analyses=True) - Comprehensive user data
• get_all_users_summary(limit=10) - System overview

InspectorConfig

Configuration management.

Properties:

• project_id - Firebase project ID
• credentials_path - Path to service account JSON
• default_limit - Default query limit
• log_level - Logging level

Available Data Types

• Users: User profiles and metadata
• Workouts: Exercise sessions and workout data
• Supplements: Supplement tracking data
• Diets: Diet plans and active diet settings
• Goals: Health goals, workout goals, unified goals
• AI Data: AI suggestions, feedback, and workout analyses
• API Keys: System API keys and tokens
• Migration: Goal migration status

Development

Setup Development Environment

# Clone and install
git clone <repository>
cd tool
poetry install

# Run tests
poetry run pytest

# Code formatting
poetry run black .
poetry run flake8 .
poetry run mypy .

# Build package
poetry build

Project Structure

tool/
├── epump_inspector/          # Core library package
│   ├── __init__.py          # Package exports
│   ├── core.py              # Main DatabaseInspector class
│   ├── config.py            # Configuration management
│   ├── cli.py               # Command-line interface
│   └── exceptions.py        # Custom exceptions
├── database/                 # Database abstraction layer
│   ├── __init__.py          # Abstract DAO interface
│   └── firestore_dao.py     # Firestore implementation
├── inspector.py             # Legacy CLI (maintained for compatibility)
├── cli.py                   # Standalone CLI wrapper
├── example_ml_usage.py      # ML usage examples
├── pyproject.toml           # Poetry configuration
├── .env.example             # Environment template
└── README.md                # This file

Security

• All sensitive files are excluded via .gitignore
• Credentials are loaded from environment variables
• No hardcoded API keys or secrets
• Type-safe configuration validation

Examples

See example_ml_usage.py for comprehensive ML/data analysis examples including:

• User workout pattern analysis
• Multi-user performance comparison
• AI training data extraction
• System health reporting

Support

For issues and questions:

1. Check the examples in example_ml_usage.py
2. Review the CLI help: epump-inspector --help
3. Check logs with --log-level debug

License

MIT License - see LICENSE file for details.