[MRG] refactor the displayed info, fix small UI issues

[HuaizhengZhang]

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PR Type

Enhancement

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Description

• Refactored the display format of suggestions in process_report function using [green] tags for better readability.
• Updated the status message in AdviseAgent.suggest method to be more descriptive.
• Refactored dataset information display in ask_data function using [green] tags.
• Updated the prompt for dataset information in baseline function for clarity.
• Modified advisor report generation in baseline function to include formatted user requirements.

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Changes walkthrough 📝

Relevant files

Enhancement

advisor.py Refactor suggestion display and update status message        mle/agents/advisor.py Refactored the display format of suggestions using [green] tags. Updated status message in AdviseAgent.suggest method. +16/-11  baseline.py Refactor dataset display and update prompts                            mle/workflow/baseline.py Refactored dataset information display using [green] tags. Updated prompt for dataset information. Modified advisor report generation to include formatted user requirements. +4/-4

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[github-actions]

PR Reviewer Guide 🔍

⏱️ Estimated effort to review: 2 🔵🔵⚪⚪⚪

🧪 No relevant tests

🔒 No security concerns identified

⚡ Key issues to review Code Consistency The use of [green] tags for formatting in process_report is inconsistent with the rest of the application's styling conventions. Consider aligning this with existing styles or ensure that all parts of the application support these tags. User Prompt Clarity The updated prompt in baseline function for dataset information may still be unclear to users. Consider specifying what types of dataset information are acceptable or expected.

[github-actions]

PR Code Suggestions ✨

Category	Suggestion	Score
Possible issue	Add checks to ensure keys exist in the dictionary before accessing them Consider checking if the keys exist in the suggestions dictionary before accessing them to avoid potential None values being formatted into the string, which could lead to misleading or incomplete information being displayed. mle/agents/advisor.py [13-26] -[green]Dataset Summary:[/green] {suggestions.get('data_summary')} -[green]Suggestion Summary:[/green] {suggestions.get('suggestion')} -[green]Task:[/green] {suggestions.get('task')} -[green]Model:[/green] {suggestions.get('model_or_algorithm')} -[green]Training Strategy:[/green] {suggestions.get('training_method')} -[green]Evaluation Metric:[/green] {suggestions.get('evaluation_metric')} -[green]Training Device:[/green] {suggestions.get('device')} -[green]Serving Strategy:[/green] {suggestions.get('serving_method')} -[green]Reference:[/green] {suggestions.get('reference')} -[green]Dependency:[/green] {suggestions.get('frameworks')} +[green]Dataset Summary:[/green] {suggestions['data_summary'] if 'data_summary' in suggestions else 'N/A'} +[green]Suggestion Summary:[/green] {suggestions['suggestion'] if 'suggestion' in suggestions else 'N/A'} +[green]Task:[/green] {suggestions['task'] if 'task' in suggestions else 'N/A'} +[green]Model:[/green] {suggestions['model_or_algorithm'] if 'model_or_algorithm' in suggestions else 'N/A'} +[green]Training Strategy:[/green] {suggestions['training_method'] if 'training_method' in suggestions else 'N/A'} +[green]Evaluation Metric:[/green] {suggestions['evaluation_metric'] if 'evaluation_metric' in suggestions else 'N/A'} +[green]Training Device:[/green] {suggestions['device'] if 'device' in suggestions else 'N/A'} +[green]Serving Strategy:[/green] {suggestions['serving_method'] if 'serving_method' in suggestions else 'N/A'} +[green]Reference:[/green] {suggestions['reference'] if 'reference' in suggestions else 'N/A'} +[green]Dependency:[/green] {suggestions['frameworks'] if 'frameworks' in suggestions else 'N/A'} Suggestion importance[1-10]: 9 Why: This suggestion improves the robustness of the code by preventing potential issues with missing keys in the dictionary, which could lead to misleading or incomplete information being displayed.	9
Maintainability	Refactor the code to use a loop for generating formatted strings To enhance the readability and maintainability of the code, consider using a loop to generate the formatted strings for each key in the suggestions dictionary instead of manually writing each line. mle/agents/advisor.py [13-26] -[green]Dataset Summary:[/green] {suggestions.get('data_summary')} -[green]Suggestion Summary:[/green] {suggestions.get('suggestion')} -[green]Task:[/green] {suggestions.get('task')} -[green]Model:[/green] {suggestions.get('model_or_algorithm')} -[green]Training Strategy:[/green] {suggestions.get('training_method')} -[green]Evaluation Metric:[/green] {suggestions.get('evaluation_metric')} -[green]Training Device:[/green] {suggestions.get('device')} -[green]Serving Strategy:[/green] {suggestions.get('serving_method')} -[green]Reference:[/green] {suggestions.get('reference')} -[green]Dependency:[/green] {suggestions.get('frameworks')} +output = [] +keys = ['data_summary', 'suggestion', 'task', 'model_or_algorithm', 'training_method', 'evaluation_metric', 'device', 'serving_method', 'reference', 'frameworks'] +for key in keys: + output.append(f"[green]{key.replace('_', ' ').title()}:[/green] {suggestions.get(key, 'N/A')}") +return '\n'.join(output) Suggestion importance[1-10]: 7 Why: This suggestion enhances the readability and maintainability of the code by reducing repetition and making it easier to update the list of keys in the future.	7