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Configuration class that manages application-wide settings, directory structures, API keys, and operational parameters for a statistical analysis application.

Purpose

The Config class serves as a centralized configuration management system for a Flask-based statistical analysis application. It defines all necessary settings including Flask configuration, file system paths, database connections, LLM API credentials, statistical analysis parameters, and cleanup policies. The class automatically creates required directories upon instantiation and provides a single source of truth for all application configuration values.

Source Code

class Config:
    """Base configuration class"""
    
    # Flask settings
    SECRET_KEY = os.environ.get('SECRET_KEY') or 'smartstat-dev-key-change-in-production'
    MAX_CONTENT_LENGTH = 100 * 1024 * 1024  # 100MB max file size
    
    # Application directories
    BASE_DIR = Path(__file__).parent
    UPLOAD_FOLDER = BASE_DIR / 'uploads'
    GENERATED_SCRIPTS_FOLDER = BASE_DIR / 'smartstat_scripts'  # Changed from 'generated_scripts' to match actual usage
    REPORTS_FOLDER = BASE_DIR / 'reports'
    SESSIONS_FOLDER = BASE_DIR / 'sessions'
    SANDBOX_FOLDER = BASE_DIR / 'sandbox'
    OUTPUT_DIR = BASE_DIR / 'output'  # For agent-generated files
    
    # Database
    DATABASE_URL = os.environ.get('DATABASE_URL') or f'sqlite:///{BASE_DIR}/smartstat.db'
    
    def __init__(self):
        # Ensure directories exist
        for directory in [self.UPLOAD_FOLDER, self.GENERATED_SCRIPTS_FOLDER, 
                         self.REPORTS_FOLDER, self.SESSIONS_FOLDER, self.SANDBOX_FOLDER,
                         self.OUTPUT_DIR]:
            directory.mkdir(parents=True, exist_ok=True)
    
    # LLM API Configuration (matching your existing setup)
    OPENAI_API_KEY = os.environ.get('OPENAI_API_KEY') or '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'
    GEMINI_API_KEY = os.environ.get('GEMINI_API_KEY') or 'AIzaSyBN6Vv4ag8kl2NWjoj96Zz2Qs9GZdO6uEw'
    AZURE_OPENAI_ENDPOINT = os.environ.get('AZURE_OPENAI_ENDPOINT') or 'https://vice-llm-2.openai.azure.com/'
    AZURE_OPENAI_API_KEY = os.environ.get('AZURE_OPENAI_API_KEY') or '8DaDtzYz3HePiypmFb6JQmJd3zUCtyCQkiYE8bePRnpyk2YNkJZRJQQJ99BAACfhMk5XJ3w3AAABACOGyJVB'
    
    # LLM Model Configuration
    AVAILABLE_MODELS = {
        'gpt-4o': {
            'name': 'GPT-4o (OpenAI)',
            'provider': 'openai',
            'description': 'OpenAI GPT-4o - Latest model with excellent reasoning and analysis'
        },
        'claude-sonnet-4-5-20250929': {
            'name': 'Claude Sonnet 4.5 (Anthropic)',
            'provider': 'anthropic',
            'description': 'Anthropic Claude Sonnet 4.5 - Latest model with superior analytical and coding capabilities'
        },
        'gemini-2.0-flash-exp': {
            'name': 'Gemini 2.0 Flash (Google)',
            'provider': 'gemini',
            'description': 'Google Gemini 2.0 Flash - Experimental model with enhanced reasoning'
        }
    }
    
    DEFAULT_MODEL = 'gpt-4o'
    
    # Anthropic API Configuration
    ANTHROPIC_API_KEY = os.environ.get('ANTHROPIC_API_KEY') or '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'
    
    # Statistical Analysis Settings
    DEFAULT_SIGNIFICANCE_LEVEL = 0.05
    MAX_DATASET_ROWS = 100000  # Medium size datasets
    MAX_COLUMNS = 500
    DEFAULT_SAMPLE_SIZE = 1000  # For large dataset sampling
    
    # Python execution settings
    SCRIPT_TIMEOUT = 300  # 5 minutes max execution time
    ALLOWED_IMPORTS = [
        'pandas', 'numpy', 'scipy', 'matplotlib', 'seaborn', 'statsmodels',
        'sklearn', 'math', 'statistics', 'datetime', 'json', 'warnings'
    ]
    
    # SQL Server settings
    MSSQL_DRIVER = '{ODBC Driver 18 for SQL Server}'
    MSSQL_TIMEOUT = 60
    
    # File format settings
    ALLOWED_EXTENSIONS = {'csv', 'xlsx', 'xls', 'txt', 'tsv'}
    
    # Report settings
    REPORT_TEMPLATE_DIR = BASE_DIR / 'templates' / 'reports'
    
    # Cleanup settings
    AUTO_CLEANUP_ENABLED = True  # Enable automatic cleanup
    KEEP_RECENT_ANALYSES = 8     # Number of recent analyses to keep per session
    CLEANUP_OLD_SESSIONS_DAYS = 15  # Days after which to clean up entire sessions
    MAX_ANALYSES_PER_SESSION = 10  # Maximum analyses before forced cleanup

Parameters

Parameter Details

__init__: The constructor takes no parameters. It automatically creates all required directories (uploads, scripts, reports, sessions, sandbox, output) if they don't exist, using mkdir with parents=True and exist_ok=True to ensure safe directory creation.

Return Value

Instantiation returns a Config object with all class attributes accessible. The __init__ method returns None but has the side effect of creating necessary directories. All attributes are accessible as class variables or instance variables after instantiation.

Class Interface

Methods

Attributes

Dependencies

• os
• pathlib

Required Imports

import os
from pathlib import Path

Usage Example

# Basic instantiation
from pathlib import Path
import os

config = Config()

# Access Flask settings
secret_key = config.SECRET_KEY
max_size = config.MAX_CONTENT_LENGTH

# Access directory paths
upload_path = config.UPLOAD_FOLDER
reports_path = config.REPORTS_FOLDER

# Access API keys
openai_key = config.OPENAI_API_KEY
anthropic_key = config.ANTHROPIC_API_KEY

# Access model configuration
available_models = config.AVAILABLE_MODELS
default_model = config.DEFAULT_MODEL
model_info = config.AVAILABLE_MODELS['gpt-4o']

# Access statistical settings
significance = config.DEFAULT_SIGNIFICANCE_LEVEL
max_rows = config.MAX_DATASET_ROWS

# Access execution settings
timeout = config.SCRIPT_TIMEOUT
allowed_imports = config.ALLOWED_IMPORTS

# Access cleanup settings
if config.AUTO_CLEANUP_ENABLED:
    keep_recent = config.KEEP_RECENT_ANALYSES
    cleanup_days = config.CLEANUP_OLD_SESSIONS_DAYS

Best Practices

• Instantiate Config once at application startup and reuse the instance throughout the application lifecycle
• Override environment variables in production to avoid using hardcoded API keys and secrets
• The class automatically creates directories on instantiation, so ensure the application has write permissions
• Use Config.AVAILABLE_MODELS to dynamically discover supported LLM models rather than hardcoding model names
• Check Config.ALLOWED_EXTENSIONS before processing uploaded files to ensure security
• Respect Config.SCRIPT_TIMEOUT when executing user-generated Python scripts to prevent infinite loops
• Use Config.ALLOWED_IMPORTS as a whitelist when validating Python code execution for security
• Monitor Config.MAX_ANALYSES_PER_SESSION and implement cleanup logic to prevent resource exhaustion
• Change SECRET_KEY in production environments for security
• All directory paths are Path objects, use them with Path operations or convert to strings as needed
• The class uses class variables, so all instances share the same configuration values
• API keys have default values for development but should be overridden via environment variables in production

Tags

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