evaluation.py

import pandas as pd
import subprocess
import argparse

# Function to extract response from subprocess output
def extract_response(text):
    marker = "ASSISTANT:"
    start_index = text.find(marker)
    if start_index != -1:
        return text[start_index + len(marker):].strip()
    else:
        return None

# Main function
def main(model_name, input_file, output_file):
    # Read the Excel file
    all_sheets = pd.read_excel(input_file, sheet_name=None)

    results = []

    try:
        count = 0
        # Iterate through sheets and rows
        for sheet_name, df in all_sheets.items():
            count += 1
            print(f"Data from sheet: {sheet_name}")

            for index, row in df.iterrows():
                try:
                    # Extract data from current row
                    ques = row[0]
                    snap_id = f"Vdata/{row[2]}.PNG"
                    options = row[3]
                    ans = str(int(row[4]))

                    # Prepare prompt for subprocess
                    prompt = f"The question is: {ques}. Here are the options: {options}. Just choose the correct option from the image also show the answer with visible quotation and give proper explanation."

                    print(f"Processing row {index}: {prompt} with image {snap_id}")

                    # Call the secondary script using subprocess
                    result = subprocess.run(
                        ['python', model_name, '--prompt', prompt, '--image_url', snap_id],
                        capture_output=True,
                        text=True
                    )

                    if result.returncode == 0:
                        answer = extract_response(result.stdout.strip())

                        print(f"Response: {answer}")

                        # Collect data for the results list
                        results.append({
                            'ques': ques,
                            'sheet_name': sheet_name,
                            'row': index,
                            'image': snap_id,
                            'question': ques,
                            'options': options,
                            'correct_ans': ans,
                            'response': answer
                        })

                    else:
                        print(f"Error: {result.stderr}")

                except Exception as e:
                    print(f"Error processing the data: {e}")

    except Exception as e:
        print(f"Error {e}")

    # Create a DataFrame from the results
    results_df = pd.DataFrame(results)

    # Save the DataFrame to an Excel file
    results_df.to_excel(output_file, index=False)
    print(f"Results saved to {output_file}")

# Entry point for the script
if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Process Excel file and run model script.")
    parser.add_argument("--model_name", required=True, help="Name of the Python script for the model (e.g., model_script.py)")
    parser.add_argument("--input_file", required=True, help="Path to the input Excel file (e.g., MAPQADataset.xlsx)")
    parser.add_argument("--output_file", required=True, help="Path to the output Excel file (e.g., results.xlsx)")

    args = parser.parse_args()

    main(args.model_name, args.input_file, args.output_file)