Utilities

def enable_progress_bar():
    """Enable tqdm progress bars for evaluation operations."""

def disable_progress_bar(): 
    """Disable tqdm progress bars for evaluation operations."""

def is_progress_bar_enabled() -> bool:
    """Check if progress bars are currently enabled."""

import evaluate

# Check current progress bar status
print(f"Progress bars enabled: {evaluate.utils.logging.is_progress_bar_enabled()}")

# Disable progress bars for cleaner output
evaluate.utils.logging.disable_progress_bar()

# Run evaluation without progress bars
accuracy = evaluate.load("accuracy")
# ... no progress bar shown during loading

# Re-enable progress bars
evaluate.utils.logging.enable_progress_bar()

# Now progress bars will be shown again
bleu = evaluate.load("bleu")  # Progress bar visible

import evaluate
import argparse

def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--quiet", action="store_true", help="Disable progress bars")
    args = parser.parse_args()
    
    if args.quiet:
        evaluate.utils.logging.disable_progress_bar()
        
    # Run evaluation with controlled output
    metric = evaluate.load("rouge")
    results = metric.compute(
        predictions=["hello world"] * 1000,
        references=["hello world"] * 1000  
    )
    print(f"Results: {results}")

if __name__ == "__main__":
    main()

def infer_gradio_input_types(features: Features) -> Dict[str, str]:
    """Map metric feature types to Gradio input component types."""

def json_to_string_type(input_type: str) -> str:
    """Convert json input type to string type for Gradio."""

def parse_readme(readme_content: str) -> str:
    """Parse README content and remove YAML frontmatter."""

def parse_gradio_data(data: List[List[Any]]) -> Tuple[List, List]:
    """Parse data from Gradio Dataframe for metric computation."""

def parse_test_cases(test_cases: str) -> Dict[str, List]:
    """Parse test case strings into structured data for Gradio."""

def launch_gradio_widget(evaluation_module: EvaluationModule) -> gradio.Interface:
    """Launch interactive Gradio widget for an evaluation module."""

import evaluate

# Load a metric
accuracy = evaluate.load("accuracy")

# Launch interactive widget
interface = evaluate.utils.gradio.launch_gradio_widget(accuracy)

# The widget allows users to:
# - Input predictions and references interactively
# - See real-time evaluation results
# - Explore metric documentation
# - Try different input formats

import evaluate
import gradio as gr

def create_evaluation_interface():
    # Load multiple metrics
    accuracy = evaluate.load("accuracy")
    f1 = evaluate.load("f1")
    precision = evaluate.load("precision")
    recall = evaluate.load("recall")
    
    def evaluate_inputs(predictions_text, references_text):
        # Parse input text to lists
        predictions = [int(x.strip()) for x in predictions_text.split(",")]
        references = [int(x.strip()) for x in references_text.split(",")]
        
        # Compute all metrics
        results = {
            "accuracy": accuracy.compute(predictions=predictions, references=references),
            "f1": f1.compute(predictions=predictions, references=references),
            "precision": precision.compute(predictions=predictions, references=references),
            "recall": recall.compute(predictions=predictions, references=references)
        }
        
        return str(results)
    
    # Create interface
    interface = gr.Interface(
        fn=evaluate_inputs,
        inputs=[
            gr.Textbox(label="Predictions (comma-separated)", placeholder="1,0,1,0"),
            gr.Textbox(label="References (comma-separated)", placeholder="1,1,0,0")
        ],
        outputs=gr.Textbox(label="Evaluation Results"),
        title="Multi-Metric Evaluation",
        description="Evaluate predictions with multiple classification metrics"
    )
    
    return interface

# Launch custom interface
interface = create_evaluation_interface()
interface.launch()

import evaluate
import gradio as gr
import pandas as pd

def create_batch_evaluation_interface():
    def evaluate_csv_data(csv_file):
        # Read CSV file
        df = pd.read_csv(csv_file.name)
        
        if 'predictions' not in df.columns or 'references' not in df.columns:
            return "Error: CSV must contain 'predictions' and 'references' columns"
        
        predictions = df['predictions'].tolist()
        references = df['references'].tolist()
        
        # Run evaluation
        combined = evaluate.combine(["accuracy", "f1", "precision", "recall"])
        results = combined.compute(predictions=predictions, references=references)
        
        return str(results)
    
    interface = gr.Interface(
        fn=evaluate_csv_data,
        inputs=gr.File(label="Upload CSV with predictions and references"),
        outputs=gr.Textbox(label="Evaluation Results"),
        title="Batch Evaluation from CSV",
        description="Upload a CSV file with 'predictions' and 'references' columns"
    )
    
    return interface

import evaluate

# Parse test cases from string format
test_case_string = """
predictions: [1, 0, 1, 0]
references: [1, 1, 0, 0]
"""

parsed_cases = evaluate.utils.gradio.parse_test_cases(test_case_string)
print(parsed_cases)  # {'predictions': [1, 0, 1, 0], 'references': [1, 1, 0, 0]}

# Infer Gradio input types from metric features
accuracy = evaluate.load("accuracy")
input_types = evaluate.utils.gradio.infer_gradio_input_types(accuracy.features)
print(input_types)  # Maps feature types to Gradio component types

import evaluate

# Process metric README with YAML frontmatter
readme_with_yaml = """---
title: Accuracy
emoji: 🎯
tags:
- evaluate
- metric
---

# Accuracy

Accuracy is the fraction of predictions our model got right.
"""

clean_readme = evaluate.utils.gradio.parse_readme(readme_with_yaml)
print(clean_readme)  # Returns content without YAML frontmatter

import evaluate
import gradio as gr

def setup_comprehensive_evaluation():
    # Disable progress bars for cleaner interface
    evaluate.utils.logging.disable_progress_bar()
    
    # Load multiple evaluation modules
    metrics = {
        "accuracy": evaluate.load("accuracy"),
        "f1": evaluate.load("f1"),
        "bleu": evaluate.load("bleu"),
        "rouge": evaluate.load("rouge")
    }
    
    def evaluate_text_classification(predictions, references):
        pred_list = [int(x.strip()) for x in predictions.split(",")]
        ref_list = [int(x.strip()) for x in references.split(",")]
        
        results = {}
        for name, metric in metrics.items():
            if name in ["accuracy", "f1"]:  # Classification metrics
                results[name] = metric.compute(predictions=pred_list, references=ref_list)
        
        return str(results)
    
    def evaluate_text_generation(predictions, references):
        pred_list = [x.strip() for x in predictions.split("\n")]
        ref_list = [x.strip() for x in references.split("\n")]
        
        results = {}
        for name, metric in metrics.items():
            if name in ["bleu", "rouge"]:  # Generation metrics
                if name == "bleu":
                    results[name] = metric.compute(predictions=pred_list, references=[[r] for r in ref_list])
                else:
                    results[name] = metric.compute(predictions=pred_list, references=ref_list)
        
        return str(results)
    
    # Create tabbed interface
    classification_interface = gr.Interface(
        fn=evaluate_text_classification,
        inputs=[
            gr.Textbox(label="Predictions", placeholder="1,0,1,0"),
            gr.Textbox(label="References", placeholder="1,1,0,0")
        ],
        outputs=gr.Textbox(label="Results"),
        title="Classification Evaluation"
    )
    
    generation_interface = gr.Interface(
        fn=evaluate_text_generation,
        inputs=[
            gr.Textbox(label="Predictions", lines=5, placeholder="Generated text 1\nGenerated text 2"),
            gr.Textbox(label="References", lines=5, placeholder="Reference text 1\nReference text 2")
        ],
        outputs=gr.Textbox(label="Results"),
        title="Generation Evaluation"
    )
    
    # Combine interfaces
    demo = gr.TabbedInterface(
        [classification_interface, generation_interface],
        ["Classification", "Generation"]
    )
    
    return demo

# Launch comprehensive evaluation interface
demo = setup_comprehensive_evaluation()
demo.launch(share=True)  # Create shareable link

import evaluate

try:
    # This requires gradio to be installed
    interface = evaluate.utils.gradio.launch_gradio_widget(accuracy)
except ImportError:
    print("Install gradio: pip install gradio")

try:
    # Invalid test case format
    cases = evaluate.utils.gradio.parse_test_cases("invalid format")
except ValueError as e:
    print(f"Parse error: {e}")