FabriXAI User Guide

# Python Bug Fixer

The Python Bug Fixer agent is designed to assist users in analyzing their Python code snippets for errors and bugs. By inputting a piece of code, the agent identifies issues and provides corrected versions of the code that resolve these errors. Additionally, the agent explains the problems identified and how its fixes improve the code's functionality and adherence to best practices in Python programming. This tool is beneficial for both beginner and experienced programmers looking to enhance their coding skills and ensure their code runs effectively.

# Getting Started

To get started, make sure you have cloned the Python Bug Fixer. Follow Create from Template to clone and configure your AI agent.

# Configuration Instructions

# Knowledge

You can upload .pdf or .txt files to provide the agent with tailored context that meets your specific requirements.

# User Inputs

Field Title Description Instruction
Python Code The Python code snippet you want to analyze and fix. Input should be a valid Python code snippet. Ensure that the code is structurally complete but may contain bugs or errors to be fixed. Keep the length reasonable for analysis.

# AI Models

The default model is Llama 3.1 8B Instruct, but you can explore other supported AI models as well. If needed, refine your prompts for more tailored outputs.

TIP

For more detailed instructions on configuring an AI agent, please refer to Configure AI Agent

# Sample Inputs and Output

# Sample Inputs

  • Python Code:
def add_numbers(a, b):
    return a + b

result = add_numbers(5, '10')
print("The result is:", result)

# Sample Output

The original code has a type error. The function `add_numbers` is attempting to add an integer and a string, which is not allowed in Python.
To fix this, we should ensure that both inputs are of the same type (e.g., converting '10' to an integer).

Here is the corrected code:

def add_numbers(a, b):
    return a + b

result = add_numbers(5, int('10'))  # Convert string '10' to an integer
print("The result is:", result)


Now, the code will correctly output:
The result is: 15

# Tips of Effective Configuration

  • Use Valid Python Code: Ensure that the code snippet you provide is valid Python code, even if it contains errors. This helps the agent analyze it effectively.
  • Be Clear with Problems: If you have a specific bug in mind, include a description in the input or comment in the code to guide the agent towards your concerns.
  • Test Various Codes: Experiment with different types of bugs, such as syntax errors, runtime errors, or logical mistakes, to understand how the agent identifies and corrects issues.
  • Use Concise Examples: Providing concise code snippets makes it easier for the agent to analyze and focus on the central problems without unnecessary complexity.
  • Check the Output: Always review the output provided by the agent to ensure it meets your expectations and that the logic behind the fixes is clear and correct.

Continue exploring our collection of Agent Templates to discover more AI agent ideas in action!