context-log-python 0.2.0

A contextual logging framework for Python

📘 SmartProfiler & ContextLogger

A lightweight Python toolkit for profiling function execution times and enhancing logging with contextual information and AI-powered error suggestions.

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✨ Features

🔍 SmartProfiler

• Captures nested function timings using sys.setprofile.
• Measures execution time of each function call.
• Provides detailed breakdowns for performance tuning.

📝 ContextLogger

• Adds caller context (function, file, line number) to every log.
• Supports JSON and human-readable logging modes.
• Captures exceptions with tracebacks.
• Generates error fix suggestions using Hugging Face’s transformers.

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🚀 Installation

pip install transformers torch

torch is required for Hugging Face transformers.

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⚡ Quick Start

1. Setup Logging

import logging
from your_module import ContextLogger

logging.basicConfig(level=logging.DEBUG)
base_logger = logging.getLogger("app")

logger = ContextLogger(base_logger, json_mode=False)

2. Logging Examples

Info & Debug Logs

logger.info("Application started")
logger.debug("Configuration loaded successfully")

Warnings

logger.warning("Low disk space detected")

Errors with Auto-Suggestions

try:
    1 / 0
except Exception:
    logger.error("Division error occurred")

📌 Example output:

2025-08-26 19:45:12 - ERROR - <module> (app.py:12) - Division error occurred
Traceback:
Traceback (most recent call last):
  File "app.py", line 12, in <module>
    1 / 0
ZeroDivisionError: division by zero

Suggestion: Check if the divisor is zero before performing division.

JSON Logging

json_logger = ContextLogger(base_logger, json_mode=True)
json_logger.info("User API call", extra={"endpoint": "/users", "method": "GET"})

📌 Example JSON output:

{
  "timestamp": "2025-08-26 19:45:12",
  "level": "INFO",
  "message": "User API call",
  "context": "<module> (app.py:20)",
  "extra": {"endpoint": "/users", "method": "GET"}
}

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⏱️ Profiling Functions

@logger.timeit
def process_data(n):
    total = 0
    for i in range(n):
        total += helper(i)
    return total

def helper(x):
    return x * x

result = process_data(1000)
print("Result:", result)

📌 Example output:

2025-08-26 19:45:12 - INFO - process_data (app.py:10) - Function process_data executed in 0.003200 seconds
2025-08-26 19:45:12 - DEBUG - process_data (app.py:10) -    └── helper (app.py:16) took 0.001500 seconds

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🔧 Advanced Usage

Adding Metadata

logger.info("User logged in", extra={"user_id": 42, "role": "admin"})

Manual Suggestions

logger.error("File not found", suggestion="Verify the file path before accessing.")

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📊 Use Cases

• Debugging slow functions with nested profiling.
• Capturing and understanding errors faster with AI suggestions.
• Structured JSON logs for microservices & observability tools.
• Automatic caller context for simpler debugging.

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📜 License

MIT License – Free to use and modify.

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👨‍💻 Author

Crafted by an Ramakrishna Bapathu to improve debugging, observability, and developer productivity.