PyDebugTools 0.2.1

A Python library for debugging python files and notebooks using GROQ

PyDebugger

PyDebuggerTools is a Python library designed to enhance error debugging in both .ipynb and .py scripts. It provides a detailed explanation of errors and suggests possible corrections using the power of LangChain and advanced traceback analysis.

Features

• Environment-Aware Debugging: Automatically detects whether the code is running in a .py file or a Jupyter Notebook and adapts its behavior accordingly.
• Error Analysis and Correction: Extracts error details (like line number, file name, and traceback) and generates concise explanations with possible solutions.
• Code Context Display: For .py files, displays the code context where the error occurred, highlighting the relevant lines.
• Seamless Integration with LangChain: Uses LangChain's AI capabilities to provide intelligent error solutions.
• Ask Questions: Allows users to ask questions about the error and get detailed explanations usig .ask_question() method.

Installation

Install the package using pip:

pip install PyDebugTools

Usage

Basic Example

from PyDebugTool.debug import PyDebugger

debugger = PyDebugger(langchain_model="llama3-8b-8192", api_key="your_groq_api_key")
debugger.enable()

How It Works

1. Initialization: Create an instance of PyDebugTool by specifying your LangChain model and API key.
2. Enable Debugger: Call enable() to activate error handling.
3. Error Handling: When an error occurs, PyDebugger will intercept it and display detailed insights along with possible fixes.

Detailed Behavior

• In .ipynb files: Overrides the default IPython traceback display to show enhanced error details and solutions.
• In .py Scripts: Replaces the default Python exception hook to provide enriched error handling in the console.

Example Code

When an error occurs, PyDebugger might display something like this:

from PyDebugTool.debug import PyDebugger

debugger = PyDebugger(langchain_model="llama3-8b-8192", api_key="your_groq_api_key")
debugger.enable()

import tensorflow as tf

Traceback (most recent call last):
  File "C:\Users\Sambit Mallick\Desktop\mlops\LLM_TOOL_SOFTWARE\test.py", line 6, in <module>
    import tensorflow as tf
ModuleNotFoundError: No module named 'tensorflow'

┌─────────────────────────────── 🤖 PyDebugger ───────────────────────────────┐
│ The error is caused by the Python interpreter not being able to find the    │
│ TensorFlow module. This is likely because TensorFlow is not installed in    │
│ the Python environment being used to run the script.                        │
│                                                                             │
│ To correct this error, you can install TensorFlow using pip:                │
│                                                                             │
│ `pip install tensorflow`                                                    │
│                                                                             │
│ Alternatively, you can install it using conda if you are using Anaconda:    │
│                                                                             │
│ `conda install tensorflow`                                                  │
└─────────────────────────────────────────────────────────────────────────────┘

Dependencies

• langchain_groq
• IPython
• rich

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contributing

Contributions are welcome! Please submit issues and pull requests to improve the library.

Author

Sambit Mallick - Feel free to reach out for collaboration or queries.

Acknowledgments

• Powered by LangChain and Groq LLM.