Streamlit App Spams DEBUG Logs Despite Log Levels Being Set Correctly?

The Problem: Unwanted DEBUG Logs Overwhelming Your Console

Are you tired of seeing an endless stream of DEBUG logs flooding your console, making it impossible to focus on the actual issues with your Streamlit app? You’re not alone! Many developers have struggled with this problem, despite having their log levels set correctly. In this article, we’ll dive into the possible causes and provide a step-by-step guide to help you tame the debug log beast and regain control over your app’s logging.

Understanding Streamlit’s Logging Mechanism

Before we dive into the solution, it’s essential to understand how Streamlit handles logging. By default, Streamlit uses the Python module, which provides a robust logging system. However, this means that any module or package used within your Streamlit app can also log messages, leading to a verbosity nightmare.

Log Levels in Streamlit

In Streamlit, log levels determine the severity of the messages that are displayed in the console. The standard log levels, in order of increasing severity, are:

• DEBUG: Detailed, low-level information for debugging purposes.
• INFO: Confirmation of actions, events, or steps.
• WARNING: Potential issues or unexpected events.
• ERROR: Critical errors or failures.
• CRITICAL: Severe, application-critical errors.

By setting the log level to a higher severity, you can filter out less important messages and focus on the critical issues. However, this doesn’t always work as expected, which brings us to the root of the problem.

The Culprits: Third-Party Libraries and Modules

The primary cause of unwanted DEBUG logs is often third-party libraries and modules used within your Streamlit app. These libraries might have their own logging mechanisms, which can override your Streamlit app’s log level settings. Some common offenders include:

• TensorFlow: Known for its verbose logging, TensorFlow can flood your console with DEBUG messages.
• Pandas: When working with large datasets, Pandas can generate an overwhelming number of DEBUG logs.
• Matplotlib: This popular plotting library can produce a significant amount of DEBUG output.
• Other libraries: Many other libraries and modules can contribute to the DEBUG log spam, so be sure to investigate any dependencies used in your app.

Solutions to Tame the DEBUG Log Beast

Now that we’ve identified the culprits, let’s explore the solutions to regain control over your Streamlit app’s logging.

Solution 1: Set Log Levels for Specific Modules

You can set log levels for specific modules or libraries using the logging.config module. Create a dictionary with the log level settings for each module:

import logging.config

log_config = {
    'version': 1,
    'formatters': {'default': {'format': '%(asctime)s - %(name)s - %(levelname)s - %(message)s'}},
    'handlers': {'console': {'class': 'logging.StreamHandler', 'formatter': 'default'}},
    'loggers': {
        'root': {'level': 'INFO', 'handlers': ['console']},
        'tensorflow': {'level': 'WARNING'},
        'pandas': {'level': 'WARNING'},
        'matplotlib': {'level': 'WARNING'}
    }
}

logging.config.dictConfig(log_config)

In this example, we set the log level for TensorFlow, Pandas, and Matplotlib to WARNING, while keeping the root log level at INFO.

Solution 2: Filter Out Unwanted Logs Using a Custom Filter

Create a custom filter class to filter out unwanted logs:

import logging

class DebugFilter(logging.Filter):
    def filter(self, record):
        return record.levelno != logging.DEBUG

logger = logging.getLogger()
logger.addFilter(DebugFilter())

This custom filter will remove all DEBUG logs from the console output.

Solution 3: Use a Logging Configuration File

Create a logging configuration file (e.g., logging.conf) with the following content:

[loggers]
keys = root, tensorflow, pandas, matplotlib

[handlers]
keys = console

[formatters]
keys = default

[logger_root]
level = INFO
handlers = console

[logger_tensorflow]
level = WARNING
handlers = console
qualname = tensorflow

[logger_pandas]
level = WARNING
handlers = console
qualname = pandas

[logger_matplotlib]
level = WARNING
handlers = console
qualname = matplotlib

[handler_console]
class = StreamHandler
level = NOTSET
formatter = default

[formatter_default]
format = %(asctime)s - %(name)s - %(levelname)s - %(message)s
datefmt =

In your Streamlit app, load the logging configuration file using:

import logging.config
logging.config.fileConfig('logging.conf')

This approach allows you to manage log levels and filter unwanted logs in a centralized configuration file.

Bonus Tip: Use a Logging Library with Advanced Features

Consider using a logging library like loguru or structlog, which offer advanced features like log filtering, rotation, and formatting. These libraries can help you gain even more control over your Streamlit app’s logging.

Conclusion

In this article, we’ve explored the common causes of unwanted DEBUG logs in Streamlit apps and provided three solutions to tame the debug log beast. By setting log levels for specific modules, using a custom filter, or employing a logging configuration file, you can regain control over your app’s logging and focus on the critical issues that matter.

Additional Resources

If you’d like to dive deeper into logging in Streamlit, check out these resources:

• The official Streamlit documentation on logging: https://docs.streamlit.io/en/stable/logging.html
• Python’s logging module documentation: https://docs.python.org/3/library/logging.html
• Loguru documentation: https://loguru.readthedocs.io/en/stable/
• Structlog documentation: https://www.structlog.org/en/stable/

By applying the solutions and best practices outlined in this article, you’ll be able to tame the DEBUG log beast and create a more efficient, informative, and enjoyable development experience with your Streamlit app.

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