adding files to customize plugin for materials api

[yuvavt]

PR Type

enhancement, configuration changes

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Description

• Added setup.py for plugin packaging and distribution, including metadata, dependencies, and entry points.
• Implemented Singleton pattern with AbstractSingleton base class in abstract_singleton.py.
• Created MaterialsProjectPlugin class for interacting with the Materials Project API, including methods for handling API responses and defining plugin capabilities.
• Added plugins_config.yaml for configuring MaterialsProjectPlugin, including API key and base URL settings.

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Changes walkthrough 📝

	Relevant files
Configuration changes	setup.py Add setup.py for plugin packaging and distribution              setup.py Added setup.py for plugin packaging and distribution. Defined plugin metadata, dependencies, and entry points. +29/-0    plugins_config.yaml Add configuration for MaterialsProjectPlugin                          plugins_config.yaml Added configuration for MaterialsProjectPlugin. Included API key and base URL settings. +8/-0
Enhancement	abstract_singleton.py Implement Singleton pattern with AbstractSingleton base class src/auto_gpt_plugin_template/abstract_singleton.py Implemented Singleton pattern using metaclass. Added AbstractSingleton base class. +35/-0    materials_plugin.py Create MaterialsProjectPlugin for Materials Project API interaction src/auto_gpt_plugin_template/materials_plugin.py Created MaterialsProjectPlugin class for interacting with the Materials Project API. Implemented methods for handling API responses. Defined plugin capabilities and settings. +95/-0

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PR Description updated to latest commit (356c9f8)

• Copy walkthrough table to "Files Changed" Tab

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PR Reviewer Guide 🔍

⏱️ Estimated effort to review: 3 🔵🔵🔵⚪⚪

🧪 No relevant tests

🔒 Security concerns Hardcoded API Key: The API key for the Materials Project API is hardcoded in the MaterialsProjectPlugin class. This could lead to security risks such as unauthorized access if the repository is public or the code is exposed. It is recommended to manage sensitive data like API keys using environment variables or other secure methods.

⚡ Key issues to review Hardcoded API Key The API key is hardcoded in the MaterialsProjectPlugin class, which could lead to security risks if the code is exposed publicly. Consider using environment variables or a secure vault solution to manage sensitive data.

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PR Code Suggestions ✨

Category	Suggestion	Score
Security	Enhance security by using environment variables for API keys instead of hardcoding them Avoid hardcoding the API key directly in the source code. Instead, use environment variables or configuration files to manage sensitive information securely. src/auto_gpt_plugin_template/materials_plugin.py [11] -self.api_key = "vYixarnBRye6p1l9eCIZk6XIRNHY4spO" +import os +self.api_key = os.getenv('MATERIALS_PROJECT_API_KEY', 'default_api_key') Apply this suggestion Suggestion importance[1-10]: 10 Why: This suggestion addresses a significant security concern by preventing the hardcoding of sensitive information like API keys, which should be managed securely using environment variables.	10
Error handling	Add error handling to API requests to manage potential failures gracefully Implement error handling for the API request to manage exceptions and errors that may occur during the request. src/auto_gpt_plugin_template/materials_plugin.py [22-23] -api_response = requests.get(f"{self.base_url}{endpoint}", headers=headers, params=params) -return api_response.json() +try: + api_response = requests.get(f"{self.base_url}{endpoint}", headers=headers, params=params) + api_response.raise_for_status() + return api_response.json() +except requests.RequestException as e: + return {'error': str(e)} Apply this suggestion Suggestion importance[1-10]: 9 Why: Implementing error handling for API requests is crucial for robustness, as it ensures the application can handle and report errors gracefully.	9
Best practice	Use a context manager for file handling to ensure the file is properly closed after its contents are read Replace the direct use of open() for reading the README file with a context manager to ensure proper resource management. setup.py [17] -long_description=open('README.md').read() +with open('README.md', 'r') as f: + long_description=f.read() Apply this suggestion Suggestion importance[1-10]: 8 Why: This suggestion improves resource management by ensuring the file is properly closed after reading, which is a best practice in Python.	8