app.py

import gradio as gr
import pandas as pd
import os
import re
from datetime import datetime

# Leaderboard Data (example CSV file for leaderboard)
LEADERBOARD_FILE = "leaderboard.csv"

def clean_answer(answer):
    if pd.isna(answer):
        return None
    answer = str(answer)
    clean = re.sub(r'[^A-Da-d]', '', answer)
    if clean:
        first_letter = clean[0].upper()
        if first_letter in ['A', 'B', 'C', 'D']:
            return first_letter
    return None

def update_leaderboard(results):
    # Append results to leaderboard file
    new_entry = {
        "Model Name": results['model_name'],
        "Overall Accuracy": f"{results['overall_accuracy']:.2%}",
        "Valid Accuracy": f"{results['valid_accuracy']:.2%}",
        "Correct Predictions": results['correct_predictions'],
        "Total Questions": results['total_questions'],
        "Timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
    }
    leaderboard_df = pd.DataFrame([new_entry])
    if os.path.exists(LEADERBOARD_FILE):
        existing_df = pd.read_csv(LEADERBOARD_FILE)
        leaderboard_df = pd.concat([existing_df, leaderboard_df], ignore_index=True)
    leaderboard_df.to_csv(LEADERBOARD_FILE, index=False)

def evaluate_predictions(prediction_file):
    ground_truth_file = "ground_truth.csv"  # Specify the path to the ground truth file
    if not prediction_file:
        return "Prediction file not uploaded", None

    if not os.path.exists(ground_truth_file):
        return "Ground truth file not found", None

    try:
        predictions_df = pd.read_csv(prediction_file.name)
        ground_truth_df = pd.read_csv(ground_truth_file)
        
        # Extract model name
        try:
            filename = os.path.basename(prediction_file.name)
            if "_" in filename and "." in filename:
                model_name = filename.split('_')[1].split('.')[0]
            else:
                model_name = "unknown_model"
        except IndexError:
            model_name = "unknown_model"

        # Merge dataframes
        merged_df = pd.merge(
            predictions_df, 
            ground_truth_df, 
            on='question_id', 
            how='inner'
        )
        merged_df['pred_answer'] = merged_df['predicted_answer'].apply(clean_answer)
        invalid_predictions = merged_df['pred_answer'].isna().sum()
        valid_predictions = merged_df.dropna(subset=['pred_answer'])
        correct_predictions = (valid_predictions['pred_answer'] == valid_predictions['Answer']).sum()
        total_predictions = len(merged_df)
        total_valid_predictions = len(valid_predictions)

        # Ensure no division by zero
        overall_accuracy = correct_predictions / total_predictions if total_predictions > 0 else 0
        valid_accuracy = (
            correct_predictions / total_valid_predictions
            if total_valid_predictions > 0
            else 0
        )

        field_metrics = {}
        for field in merged_df['Field'].unique():
            field_data = merged_df[merged_df['Field'] == field]
            field_valid_data = field_data.dropna(subset=['pred_answer'])

            field_correct = (field_valid_data['pred_answer'] == field_valid_data['Answer']).sum()
            field_total = len(field_data)
            field_valid_total = len(field_valid_data)
            field_invalid = field_total - field_valid_total

            field_metrics[field] = {
                'accuracy': field_correct / field_total if field_total > 0 else 0,
                'valid_accuracy': field_correct / field_valid_total if field_valid_total > 0 else 0,
                'correct': field_correct,
                'total': field_total,
                'invalid': field_invalid
            }

        results = {
            'model_name': model_name,
            'overall_accuracy': overall_accuracy,
            'valid_accuracy': valid_accuracy,
            'total_questions': total_predictions,
            'valid_predictions': total_valid_predictions,
            'invalid_predictions': invalid_predictions,
            'correct_predictions': correct_predictions,
            'field_performance': field_metrics
        }

        output_file = "evaluation_results.txt"
        write_evaluation_results(results, output_file)
        return "Evaluation completed successfully!", output_file

    except Exception as e:
        return f"Error during evaluation: {str(e)}", None


# Gradio Interface with Leaderboard
def display_leaderboard():
    if not os.path.exists(LEADERBOARD_FILE):
        return "Leaderboard is empty."
    leaderboard_df = pd.read_csv(LEADERBOARD_FILE)
    return leaderboard_df.to_markdown(index=False)

demo = gr.Blocks()

with demo:
    gr.Markdown("# Prediction Evaluation Tool with Leaderboard")
    with gr.Tab("Evaluate"):
        file_input = gr.File(label="Upload Prediction CSV")
        eval_status = gr.Textbox(label="Evaluation Status")
        eval_results_file = gr.File(label="Download Evaluation Results")
        eval_button = gr.Button("Evaluate")
        eval_button.click(
            evaluate_predictions, inputs=file_input, outputs=[eval_status, eval_results_file]
        )
    with gr.Tab("Leaderboard"):
        leaderboard_text = gr.Textbox(label="Leaderboard", interactive=False)
        refresh_button = gr.Button("Refresh Leaderboard")
        refresh_button.click(display_leaderboard, outputs=leaderboard_text)

if __name__ == "__main__":
    demo.launch()