Delete App_Function_Libraries/Gradio_UI/MMLU_Pro_tab.py

App_Function_Libraries/Gradio_UI/MMLU_Pro_tab.py

@@ -1,115 +0,0 @@
-# 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]