Delete app,py

app,py

@@ -1,95 +0,0 @@
-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)
-        filename = os.path.basename(prediction_file.name)
-        model_name = filename.split('_')[1].split('.')[0] if "_" in filename else "unknown_model"
-
-        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)
-        correct_predictions = (merged_df['pred_answer'] == merged_df['Answer']).sum()
-        total_predictions = len(merged_df)
-        overall_accuracy = correct_predictions / total_predictions
-
-        results = {
-            'model_name': model_name,
-            'overall_accuracy': overall_accuracy,
-            'correct_predictions': correct_predictions,
-            'total_questions': total_predictions,
-        }
-
-        update_leaderboard(results)
-
-        return "Evaluation completed successfully! Leaderboard updated.", LEADERBOARD_FILE
-    except Exception as e:
-        return f"Error: {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()