error-detect 0.1.0

A package for detecting errors and generating solutions using an LLM API.

🌟 ErrorDetect: AI-Powered Python Error Fixing

ErrorDetect is a Python package that automatically captures, analyzes, and fixes errors using a Large Language Model (LLM). It integrates with Ollama LLM APIs to return only the corrected line of code for detected errors.

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🚀 Features

✅ Automatic Error Capture – Detects exceptions and captures error details.
✅ AI-Powered Fixes – Uses LLMs (like Llama3) to generate corrected lines of code.
✅ Traceback Extraction – Captures the exact line of code causing the error.
✅ Flexible Integration – Works with or without LLM integration.

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👥 Installation

You can install error_detect directly from PyPI:

pip install error_detect

Or install it from source:

git clone https://github.com/Rhul27/error_detect.git
cd error_detect
pip install .

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🛠️ Usage

1️⃣ Basic Usage (Without LLM Integration)

You can use ErrorDetect to capture and display error details:

from error_detect import ErrorDetect

client = ErrorDetect()

try:
    a = 1 / 0
except Exception:
    print(client.error_detect())

Output:

Type: ZeroDivisionError
Line: 10
Error: division by zero

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2️⃣ Using an LLM to Automatically Fix Errors

If you have an Ollama-compatible API running, you can connect to an AI model to fix errors automatically:

from error_detect import ErrorDetect

client = ErrorDetect("http://localhost:11434", "llama3.2")

try:
    a = 1 / 0
except Exception:
    output = client.get_error_solution()
    print(output)

Example Output:

Type: ZeroDivisionError
Line: 12
Error: division by zero
Solution : "a = 1.0 / (0.0001)  # Avoid division by zero"

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3️⃣ Handling Missing Dictionary Keys (KeyError Example)

try:
    data = {"name": "Alice"}
    print(data["age"])  # This key does not exist
except Exception:
    output = client.get_error_solution()
    print(output)

Output:

Type: KeyError
Line: 15
Error: 'age'
Solution : "age = data.get('age', 25)  # Provide a default value"

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⚙️ API Reference

ErrorDetect(ollama_url=None, model_name=None)

Parameters:

• ollama_url (str, optional) – The base URL of the Ollama LLM server.
• model_name (str, optional) – The name of the LLM model to use.

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error_detect()

Returns:
Returns a string containing error details:

Type: <ExceptionType>
Line: <LineNumber>
Error: <ErrorMessage>

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get_error_solution(error_message=None, error_line=None)

Automatically detects errors and gets the corrected code line using LLM.

Returns:
Formatted output with both the error details and the solution:

Type: <ExceptionType>
Line: <LineNumber>
Error: <ErrorMessage>
Error line : <ErrorLine>
Solution : "<Corrected Line of Code>"

If LLM is not integrated, returns:

LLM integration not configured.

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🌍 Environment Setup

To use the AI-powered error detection, ensure you have an Ollama LLM server running locally:

ollama serve

You can check available models via:

curl http://localhost:11434/api/tags

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🛠️ Troubleshooting

Problem: I get LLM integration not configured.
👉 Solution: Pass a valid ollama_url and model_name when initializing ErrorDetect.

Problem: LLM does not return a proper fix.
👉 Solution: Try a different model like "codellama" for better code-specific fixes.

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🤝 Contributing

We welcome contributions! Follow these steps to contribute:

1. Fork the repository.
2. Create a new feature branch (git checkout -b feature-branch).
3. Commit changes (git commit -m "Added new feature").
4. Push to your fork (git push origin feature-branch).
5. Open a Pull Request.

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📃 License

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

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📞 Contact

📧 Email: [e-mail] 🔗 GitHub: Rhul27/error_detect