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| # MMLU_Pro_tab.py | |
| # is a library that contains the Gradio UI code for the MMLU-Pro benchmarking tool. | |
| # | |
| ############################################################################################################## | |
| # Imports | |
| import os | |
| import gradio as gr | |
| import logging | |
| # | |
| # External Imports | |
| from tqdm import tqdm | |
| # Local Imports | |
| from App_Function_Libraries.Benchmarks_Evaluations.MMLU_Pro.MMLU_Pro_rewritten import ( | |
| load_mmlu_pro, run_mmlu_pro_benchmark, mmlu_pro_main, load_mmlu_pro_config | |
| ) | |
| # | |
| ############################################################################################################## | |
| # | |
| # Functions: | |
| # Set up logging | |
| logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') | |
| logger = logging.getLogger(__name__) | |
| def get_categories(): | |
| """Fetch categories using the dataset loader from MMLU_Pro_rewritten.py""" | |
| try: | |
| test_data, _ = load_mmlu_pro() # Use the function from MMLU_Pro_rewritten.py | |
| return list(test_data.keys()) # Return the categories from the test dataset | |
| except Exception as e: | |
| logger.error(f"Failed to load categories: {e}") | |
| return ["Error loading categories"] | |
| def load_categories(): | |
| """Helper function to return the categories for the Gradio dropdown.""" | |
| categories = get_categories() # Fetch categories from the dataset | |
| if categories: | |
| return gr.update(choices=categories, value=categories[0]) # Update dropdown with categories | |
| else: | |
| return gr.update(choices=["Error loading categories"], value="Error loading categories") | |
| def run_benchmark_from_ui(url, api_key, model, timeout, category, parallel, verbosity, log_prompt): | |
| """Function to run the benchmark with parameters from the UI.""" | |
| # Override config with UI parameters | |
| config = load_mmlu_pro_config( | |
| url=url, | |
| api_key=api_key, | |
| model=model, | |
| timeout=timeout, | |
| categories=[category] if category else None, | |
| parallel=parallel, | |
| verbosity=verbosity, | |
| log_prompt=log_prompt | |
| ) | |
| # Run the benchmarking process | |
| try: | |
| # Call the main benchmarking function | |
| mmlu_pro_main() | |
| # Assume the final report is generated in "eval_results" folder | |
| report_path = os.path.join("eval_results", config["server"]["model"].replace("/", "-"), "final_report.txt") | |
| # Read the final report | |
| with open(report_path, "r") as f: | |
| report = f.read() | |
| return report | |
| except Exception as e: | |
| logger.error(f"An error occurred during benchmark execution: {e}") | |
| return f"An error occurred during benchmark execution. Please check the logs for more information. Error: {str(e)}" | |
| def create_mmlu_pro_tab(): | |
| """Create the Gradio UI tab for MMLU-Pro Benchmark.""" | |
| with gr.Tab("MMLU-Pro Benchmark"): | |
| gr.Markdown("## Run MMLU-Pro Benchmark") | |
| with gr.Row(): | |
| with gr.Column(): | |
| # Inputs for the benchmark | |
| url = gr.Textbox(label="Server URL") | |
| api_key = gr.Textbox(label="API Key", type="password") | |
| model = gr.Textbox(label="Model Name") | |
| timeout = gr.Number(label="Timeout (seconds)", value=30) | |
| category = gr.Dropdown(label="Category", choices=["Load categories..."]) | |
| load_categories_btn = gr.Button("Load Categories") | |
| parallel = gr.Slider(label="Parallel Requests", minimum=1, maximum=10, step=1, value=1) | |
| verbosity = gr.Slider(label="Verbosity Level", minimum=0, maximum=2, step=1, value=1) | |
| log_prompt = gr.Checkbox(label="Log Prompt") | |
| with gr.Column(): | |
| # Run button and output display | |
| run_button = gr.Button("Run Benchmark") | |
| output = gr.Textbox(label="Benchmark Results", lines=20) | |
| # When "Load Categories" is clicked, load the categories into the dropdown | |
| load_categories_btn.click( | |
| load_categories, | |
| outputs=category | |
| ) | |
| # When "Run Benchmark" is clicked, trigger the run_benchmark_from_ui function | |
| run_button.click( | |
| run_benchmark_from_ui, # Use the function defined to run the benchmark | |
| inputs=[url, api_key, model, timeout, category, parallel, verbosity, log_prompt], | |
| outputs=output | |
| ) | |
| return [url, api_key, model, timeout, category, parallel, verbosity, log_prompt, run_button, output] |