Delete vis_python_exe.py

vis_python_exe.py

@@ -1,783 +0,0 @@
-import sys
-import os
-import re
-import json
-import base64
-from io import BytesIO
-from PIL import Image
-import argparse
-from inference_engine.safe_persis_shared_vis_python_exe import PythonExecutor, ImageRuntime
-from openai import OpenAI
-import anthropic
-
-def encode_image(image):
-    """
-    Convert a PIL.Image object or image file path to base64-encoded string, and get resolution info.
-    
-    Args:
-        image: Can be a PIL.Image object or image file path.
-    Returns:
-        dict with keys:
-        - 'base64': base64-encoded string
-        - 'width': width in pixels
-        - 'height': height in pixels
-        - 'resolution': string "widthxheight"
-    """
-    img_obj = None
-    
-    if isinstance(image, str):
-        # Handle file path
-        img_obj = Image.open(image)
-        with open(image, "rb") as image_file:
-            base64_str = base64.b64encode(image_file.read()).decode('utf-8')
-    else:
-        # Handle PIL.Image object
-        img_obj = image
-        buffered = BytesIO()
-        image.save(buffered, format='PNG')
-        base64_str = base64.b64encode(buffered.getvalue()).decode('utf-8')
-    
-    width, height = img_obj.size
-    
-    return {
-        'base64': base64_str,
-        'width': width,
-        'height': height
-    }
-
-def encode_image_with_resize(image):
-    """
-    Convert a PIL.Image object or image file path to base64-encoded string, get resolution info.
-    If resolution > 1024x1024, resize to half.
-    
-    Args:
-        image: Can be a PIL.Image object or image file path
-    Returns:
-        dict with keys:
-        - 'base64': base64-encoded string
-        - 'width': width in pixels
-        - 'height': height in pixels
-        - 'resolution': string "widthxheight"
-    """
-    img_obj = None
-    
-    if isinstance(image, str):
-        img_obj = Image.open(image)
-    else:
-        img_obj = image
-
-    # Resize if larger than 1024x1024
-    width, height = img_obj.size
-    if width > 1024 or height > 1024:
-        new_size = (width // 2, height // 2)
-        img_obj = img_obj.resize(new_size, Image.LANCZOS)
-        width, height = img_obj.size
-
-    buffered = BytesIO()
-    img_obj.save(buffered, format='PNG')
-    base64_str = base64.b64encode(buffered.getvalue()).decode('utf-8')
-
-    return {
-        'base64': base64_str,
-        'width': width,
-        'height': height,
-        'resolution': f"{width}x{height}"
-    }
-
-def check(evaluator, pred_ans, real_ans):
-    if len(pred_ans) == 0:
-        return []
-    correctness = evaluator.score(pred_ans, real_ans)
-    return correctness
-
-def execute_codes(codes, messages, executor: PythonExecutor):
-    no_code_idx = []
-    codes_use = []
-    for i, code in enumerate(codes):
-        if code == "":
-            no_code_idx.append(i)
-        else:
-            codes_use.append(code)
-    batch_results = executor.batch_apply(codes_use, messages)
-    return batch_results, no_code_idx
-
-def process_prompt_init(question, image_path_list, prompt_template, prompt_type, api_name):
-    with open(prompt_template, "r") as fin:
-        sys = json.load(fin)
-    prompt_prefix = sys[prompt_type]
-
-    image_path = image_path_list[0]
-
-    if "<IMAGE_PLACE_HOLDER_0>" in question:
-        if "no_tool" in prompt_type:
-
-            if "claude" in api_name:
-                img_result = encode_image_with_resize(image_path)
-            else:
-                img_result = encode_image(image_path)
-            image_base64 = img_result['base64']
-            question_with_options = question
-            question = prompt_prefix.format(query=question_with_options)
-
-            parts = question.split("<IMAGE_PLACE_HOLDER_0>")
-            content = []
-            
-            # Add text before image (if any)
-            if parts[0].strip():
-                content.append({"type": "text", "text": parts[0].strip()})
-            # Add image
-            content.append({"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image_base64}"}})
-            
-            # Add text after image (if any)
-            if len(parts) > 1 and parts[1].strip():
-                content.append({"type": "text", "text": parts[1].strip()})
-
-            messages = [
-                {
-                    "role": "user",
-                    "content": content
-                }
-            ]
-
-            return messages
-
-        else:
-            if "claude" in api_name:
-                img_result = encode_image_with_resize(image_path)
-            else:
-                img_result = encode_image(image_path)
-            image_base64 = img_result['base64']
-            width = img_result['width']
-            height = img_result['height']
-            question_with_options = question
-            question = prompt_prefix.format(query=question_with_options, width=str(width), height=str(height))
-
-            # Split question into parts
-            parts = question.split("<IMAGE_PLACE_HOLDER_0>")
-            # Build message with image_clue tags
-            content = []
-            
-            # Add text before image (if any)
-            if parts[0].strip():
-                content.append({"type": "text", "text": parts[0].strip()})
-            
-            # Add image with tags
-            content.append({"type": "text", "text": "<image_clue_0>"})
-            content.append({"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image_base64}"}})
-            content.append({"type": "text", "text": "</image_clue_0>\n\n"})
-            
-            # Add text after image (if any)
-            if len(parts) > 1 and parts[1].strip():
-                content.append({"type": "text", "text": parts[1].strip()})
-
-            messages = [
-                {
-                    "role": "user",
-                    "content": content
-                }
-            ]
-
-            return messages
-
-    else:
-        if "no_tool" in prompt_type:
-
-            if "claude" in api_name:
-                img_result = encode_image_with_resize(image_path)
-            else:
-                img_result = encode_image(image_path)
-            image_base64 = img_result['base64']
-            question_with_options = question
-
-            messages = [
-                {
-                    "role": "user",
-                    "content": [{"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image_base64}"}}] + [{"type": "text", "text": prompt_prefix.format(query=question_with_options)}]
-                }
-            ]
-
-            return messages
-
-        else:
-            if "claude" in api_name:
-                img_result = encode_image_with_resize(image_path)
-            else:
-                img_result = encode_image(image_path)
-            image_base64 = img_result['base64']
-            width = img_result['width']
-            height = img_result['height']
-            question_with_options = question
-
-            messages = [
-                {
-                    "role": "user",
-                    "content": [{"type": "text", "text": "<image_clue_0>"}] + [{"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image_base64}"}}] + [{"type": "text", "text": "</image_clue_0>\n\n"}] + [{"type": "text", "text": prompt_prefix.format(query=question_with_options, width=str(width), height=str(height))}]
-                }
-            ]
-
-            return messages
-
-def process_prompt_init_multi_images(question, image_path_list, prompt_template, prompt_type, api_name):
-    with open(prompt_template, "r") as fin:
-        sys = json.load(fin)
-    prompt_prefix = sys[prompt_type]
-    
-    # Prepare image data
-    image_data = []
-    image_information = ""
-    
-    for i, image_path in enumerate(image_path_list):
-        if "claude" in api_name:
-            img_result = encode_image_with_resize(image_path)
-        else:
-            img_result = encode_image(image_path)
-        image_base64 = img_result['base64']
-        width = img_result['width']
-        height = img_result['height']
-        
-        image_data.append({
-            "index": i,
-            "base64": image_base64,
-            "width": width,
-            "height": height,
-            "placeholder": f"<IMAGE_PLACE_HOLDER_{i}>"
-        })
-        
-        image_information += f"width of image_clue_{i}: {width}, height of image_clue_{i}: {height}\n"
-    
-    # Format question
-    formatted_question = prompt_prefix.format(query=question, image_information=image_information)
-    
-    # Check if placeholder exists
-    has_placeholders = any(f"<IMAGE_PLACE_HOLDER_{i}>" in formatted_question for i in range(len(image_path_list)))
-    
-    if has_placeholders:
-        # Insert images at placeholder positions
-        if "no_tool" in prompt_type:
-            content = []
-            remaining_text = formatted_question
-            
-            for img_data in image_data:
-                placeholder = img_data["placeholder"]
-                if placeholder in remaining_text:
-                    parts = remaining_text.split(placeholder, 1)
-                    
-                    if parts[0]:
-                        content.append({"type": "text", "text": parts[0]})
-                    
-                    content.append({"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{img_data['base64']}"}})
-                    
-                    remaining_text = parts[1]
-            
-            if remaining_text:
-                content.append({"type": "text", "text": remaining_text})
-            
-            messages = [{"role": "user", "content": content}]
-            return messages
-        else:
-            content = []
-            remaining_text = formatted_question
-            
-            for img_data in image_data:
-                placeholder = img_data["placeholder"]
-                if placeholder in remaining_text:
-                    parts = remaining_text.split(placeholder, 1)
-                    
-                    if parts[0]:
-                        content.append({"type": "text", "text": parts[0]})
-                    
-                    i = img_data["index"]
-                    content.append({"type": "text", "text": f"<image_clue_{i}>"})
-                    content.append({"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{img_data['base64']}"}})
-                    content.append({"type": "text", "text": f"</image_clue_{i}>\n\n"})
-                    
-                    remaining_text = parts[1]
-            
-            if remaining_text:
-                content.append({"type": "text", "text": remaining_text})
-            
-            messages = [{"role": "user", "content": content}]
-            return messages
-    else:
-        # Handle as usual if no placeholder
-        if "no_tool" in prompt_type:
-            content = []
-            
-            for i, img_data in enumerate(image_data):
-                content.append({"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{img_data['base64']}"}})
-            
-            content.append({"type": "text", "text": formatted_question})
-            
-            messages = [{"role": "user", "content": content}]
-            return messages
-        else:
-            content = []
-            
-            for i, img_data in enumerate(image_data):
-                content.append({"type": "text", "text": f"<image_clue_{i}>"})
-                content.append({"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{img_data['base64']}"}})
-                content.append({"type": "text", "text": f"</image_clue_{i}>\n\n"})
-            
-            content.append({"type": "text", "text": formatted_question})
-            
-            messages = [{"role": "user", "content": content}]
-            return messages
-
-
-def update_messages_with_execute_content(image_nums_in_input, messages, images_result, text_result, error_result, image_clue_idx):
-    if error_result is None:
-        new_messages = []
-        image_content = []
-        for message_item in messages[:-1]:
-            new_messages.append(message_item)
-
-        assistant_message_item = messages[-1]['content']
-        interpreter_message_text_prefix = [{"type": "text", "text": f"<interpreter>\nText Result:\n{text_result}\nImage Result:\n"}]
-        if images_result is not None:
-            print(f"#### image_clue_index: {image_clue_idx},Image_nums_in_input: {image_nums_in_input}, len of images_result: {len(images_result)}")
-            # for image_base64_item in images_result[image_clue_idx-image_nums_in_input:]:
-            for image_base64_item in images_result:
-                interpreter_message_images = [{"type": "text", "text": f"<image_clue_{image_clue_idx}>"}] + [{"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image_base64_item}"}}] + [{"type": "text", "text": f"</image_clue_{image_clue_idx}>"}]
-                image_content += interpreter_message_images
-                image_clue_idx += 1
-        else:
-            image_content = [{"type": "text", "text": "None"}]
-        interpreter_message_text_profill = [{"type": "text", "text": "</interpreter>\n"}]
-
-        interpreter_message_item = interpreter_message_text_prefix + image_content + interpreter_message_text_profill
-        new_messages.append({"role": "assistant", "content": assistant_message_item})
-        new_messages.append({"role": "user", "content": interpreter_message_item})
-    else:
-        new_messages = []
-        for message_item in messages[:-1]:
-            new_messages.append(message_item)
-    
-        assistant_message_item = messages[-1]['content']
-        interpreter_message_text_prefix = [{"type": "text", "text": f"<interpreter>{error_result}"}]
-        interpreter_message_text_profill = [{"type": "text", "text": "</interpreter>\n"}]
-    
-        interpreter_message_item = interpreter_message_text_prefix + interpreter_message_text_profill
-        new_messages.append({"role": "assistant", "content": assistant_message_item})
-        new_messages.append({"role": "user", "content": interpreter_message_item})
-
-    return new_messages, image_clue_idx
-
-def update_messages_with_code(messages, generated_content):
-    message_item = {
-        "role": "assistant",
-        "content": [{"type": "text", "text": f"{generated_content}</code>\n"}]
-    }
-
-    messages.append(message_item)
-    return messages
-
-def update_messages_with_text(messages, generated_content):
-    message_item = {
-        "role": "assistant",
-        "content": [{"type": "text", "text": f"{generated_content}"}]
-    }
-
-    messages.append(message_item)
-    return messages
-
-def call_chatgpt_api(args, messages, client, max_tokens=10000, stop=None, temperature=0.6):
-    """Call ChatGPT API with the given messages"""
-    try:
-        client_type = args.client_type
-        api_name = args.api_name
-    except:
-        client_type = args['client_type']
-        api_name = args['api_name']
-    
-    if client_type == "openai" or client_type == "azure":
-        response = client.chat.completions.create(
-            model=api_name,
-            messages=messages,
-            max_tokens=max_tokens,
-            temperature=temperature,
-            top_p=1.0,
-            stop=stop,
-            timeout=300
-        )
-        response_text = response.choices[0].message.content
-    elif client_type == "anthropic":
-        message = client.messages.create(
-            model=api_name,
-            max_tokens=max_tokens,
-            messages=messages,
-            temperature=temperature,
-            top_p=1.0,
-            stop_sequences=stop
-        )  
-        response_text = message.content[0].text if isinstance(message.content, list) else message.content
-    elif client_type == "vllm":
-        response = client.chat.completions.create(
-            model=api_name,
-            messages=messages,
-            max_tokens=max_tokens,
-            temperature=temperature,
-            top_p=1.0,
-            stop=stop
-        )
-        response_text = response.choices[0].message.content
-    else:
-        print("Your args.client_type must be one of openai, azure, anthropic and vllm.")
-        return None, None
-    
-    # Check if stop sequence is encountered
-    stop_reason = None
-    if stop and any(s in response_text for s in stop):
-        for s in stop:
-            if s in response_text:
-                stop_reason = s
-                break
-    else:
-        if client_type in ["openai", "azure", "vllm"]:
-            stop_reason = response.choices[0].finish_reason
-        else:
-            stop_reason = "stop"
-
-    if "<code>" in response_text:
-        stop_reason = "</code>"
-    
-    return response_text, stop_reason
-
-def evaluate_single_data(args, data, client, executor):
-    try:
-        prompt_template = args.prompt_template
-        prompt = args.prompt
-        exe_code = args.exe_code
-        max_tokens = args.max_tokens
-        temperature = args.temperature
-        api_name = args.api_name
-    except:
-        prompt_template = args['prompt_template']
-        prompt = args['prompt']
-        exe_code = args['exe_code']
-        max_tokens = args['max_tokens']
-        temperature = args['temperature']
-        api_name = args['api_name']
-
-    image_path_list = data['image_path_list']
-
-    if "no_tool" in prompt:
-        if len(image_path_list) == 1:
-            messages = process_prompt_init(data["question"], image_path_list, prompt_template, prompt, api_name)
-        elif len(image_path_list) >= 2:
-            messages = process_prompt_init_multi_images(data["question"], image_path_list, prompt_template, prompt, api_name)
-    else:
-        if len(image_path_list) == 1:
-            prompt = "vistool_with_img_info_v2"
-            messages = process_prompt_init(data["question"], image_path_list, prompt_template, prompt, api_name)
-        elif len(image_path_list) >= 2:
-            prompt = "vistool_with_img_info_multi_image"
-            messages = process_prompt_init_multi_images(data["question"], image_path_list, prompt_template, prompt, api_name)
-    
-    # Generate initial response
-    response_text, pred_stop_reason = call_chatgpt_api(
-        args,
-        messages, 
-        client,
-        max_tokens=max_tokens,
-        stop=["</code>"] if exe_code else None,
-        temperature=temperature
-    )
-    
-    # Handle response
-    final_response = response_text
-    code_execution_count = 0
-    image_clue_idx = len(image_path_list)
-    
-    while True:
-        # Check if code execution is needed
-        if exe_code and pred_stop_reason == "</code>":
-            # Extract code to execute
-            messages = update_messages_with_code(messages, response_text)
-            code_to_execute = response_text.split("```python")[-1].split("```")[0].strip()
-            
-            # Execute code
-            exe_result = execute_codes([code_to_execute], messages, executor)[0][0]
-            if exe_result is None:
-                text_result = "None"
-                images_result = None
-            else:
-                output, report = exe_result
-                if report == "Done":
-                    error_result = None
-                    try:
-                        text_result = exe_result[0]['text']
-                    except:
-                        text_result = None
-                        print("text result is none.")
-                    try:
-                        images_result = exe_result[0]['images']
-                    except:
-                        images_result = None
-                        print("image result is none.")
-                else:
-                    error_result = report
-                    text_result = None
-                    images_result = None
-
-            messages, new_image_clue_idx = update_messages_with_execute_content(len(image_path_list), messages, images_result, text_result, error_result, image_clue_idx)
-            image_clue_idx = new_image_clue_idx
-            
-            code_execution_count += 1
-            
-            # Generate next response part
-            response_text, pred_stop_reason = call_chatgpt_api(
-                args,
-                messages, 
-                client,
-                max_tokens=max_tokens,
-                stop=["</code>"] if exe_code else None,
-                temperature=temperature
-            )
-
-        else:
-            final_response = response_text
-            messages = update_messages_with_text(messages, response_text)
-            break
-       
-    return messages, final_response
-
-
-def evaluate_single_data_multi_images(args, data, client, executor):
-    try:
-        prompt_template = args.prompt_template
-        prompt = args.prompt
-        exe_code = args.exe_code
-        max_tokens = args.max_tokens
-    except:
-        prompt_template = args['prompt_template']
-        prompt = args['prompt']
-        exe_code = args['exe_code']
-        max_tokens = args['max_tokens']
-
-    messages = process_prompt_init_multi_images(data["question"], data['image_path_list'], prompt_template, prompt)
-    
-    # Generate initial response
-    response_text, pred_stop_reason = call_chatgpt_api(
-        args,
-        messages, 
-        client,
-        max_tokens=max_tokens,
-        stop=["</code>"] if exe_code else None
-    )
-    
-    # Handle response
-    final_response = response_text
-    code_execution_count = 0
-    image_clue_idx = data['image_nums_in_input']
-    
-    while True:
-        # Check if code execution is needed
-        if exe_code and pred_stop_reason == "</code>":
-            # Extract code to execute
-            messages = update_messages_with_code(messages, response_text)
-            code_to_execute = response_text.split("```python")[-1].split("```")[0].strip()
-            
-            # Execute code
-            exe_result = execute_codes([code_to_execute], messages, executor)[0][0]
-            if exe_result is None:
-                text_result = "None"
-                images_result = None
-            else:
-                output, report = exe_result
-                if report == "Done":
-                    error_result = None
-                    try:
-                        text_result = exe_result[0]['text']
-                    except:
-                        text_result = None
-                        print("text result is none.")
-                    try:
-                        images_result = exe_result[0]['images']
-                    except:
-                        images_result = None
-                        print("image result is none.")
-                else:
-                    error_result = report
-                    text_result = None
-                    images_result = None
-
-            messages, new_image_clue_idx = update_messages_with_execute_content(data['image_nums_in_input'], messages, images_result, text_result, error_result, image_clue_idx)
-            image_clue_idx = new_image_clue_idx
-            
-            code_execution_count += 1
-            
-            # Generate next response part
-            response_text, pred_stop_reason = call_chatgpt_api(
-                args,
-                messages, 
-                client,
-                max_tokens=max_tokens,
-                stop=["</code>"] if exe_code else None
-            )
-
-        else:
-            final_response = response_text
-            messages = update_messages_with_text(messages, response_text)
-            break
-       
-    return messages, final_response
-
-def evaluate_single_data_video(args, data, client, executor):
-    try:
-        prompt_template = args.prompt_template
-        prompt = args.prompt
-        exe_code = args.exe_code
-        max_tokens = args.max_tokens
-    except:
-        prompt_template = args['prompt_template']
-        prompt = args['prompt']
-        exe_code = args['exe_code']
-        max_tokens = args['max_tokens']
-
-    messages = process_prompt_init_multi_images(data["question"], data['image_path_list'], prompt_template, prompt)
-    
-    # Generate initial response
-    response_text, pred_stop_reason = call_chatgpt_api(
-        args,
-        messages, 
-        client,
-        max_tokens=max_tokens,
-        stop=["</code>"] if exe_code else None
-    )
-    
-    # Handle response
-    final_response = response_text
-    code_execution_count = 0
-    image_clue_idx = data['image_nums_in_input']
-    
-    while True:
-        # Check if code execution is needed
-        if exe_code and pred_stop_reason == "</code>":
-            # Extract code to execute
-            messages = update_messages_with_code(messages, response_text)
-            code_to_execute = response_text.split("```python")[-1].split("```")[0].strip()
-            
-            # Execute code
-            exe_result = execute_codes([code_to_execute], messages, executor)[0][0]
-            if exe_result is None:
-                text_result = "None"
-                images_result = None
-            else:
-                output, report = exe_result
-                if report == "Done":
-                    error_result = None
-                    try:
-                        text_result = exe_result[0]['text']
-                    except:
-                        text_result = None
-                        print("text result is none.")
-                    try:
-                        images_result = exe_result[0]['images']
-                    except:
-                        images_result = None
-                        print("image result is none.")
-                else:
-                    error_result = report
-                    text_result = None
-                    images_result = None
-
-            messages, new_image_clue_idx = update_messages_with_execute_content(data['image_nums_in_input'], messages, images_result, text_result, error_result, image_clue_idx)
-            image_clue_idx = new_image_clue_idx
-            
-            code_execution_count += 1
-            
-            # Generate next response part
-            response_text, pred_stop_reason = call_chatgpt_api(
-                args,
-                messages, 
-                client,
-                max_tokens=max_tokens,
-                stop=["</code>"] if exe_code else None
-            )
-
-        else:
-            final_response = response_text
-            messages = update_messages_with_text(messages, response_text)
-            break
-       
-    return messages, final_response
-
-
-# New wrapper functions for safe execution with cleanup
-def evaluate_batch_with_cleanup(args, data_list, client):
-    """Wrapper function to ensure proper cleanup of resources when processing multiple items"""
-    # Initialize executor with process isolation
-    executor = PythonExecutor(use_process_isolation=True)
-    
-    try:
-        results = []
-        for data in data_list:
-            try:
-                result = evaluate_single_data(args, data, client, executor)
-                results.append(result)
-            except Exception as e:
-                print(f"Error processing data item: {str(e)}")
-                results.append((None, f"Error: {str(e)}"))
-                # Reset the executor for the next item
-                executor.reset()
-        
-        return results
-    finally:
-        # Ensure cleanup of persistent worker
-        del executor
-
-def evaluate_single_with_cleanup(args, data, client):
-    """Wrapper function for evaluating a single item with proper cleanup"""
-    # Initialize executor with process isolation
-    executor = PythonExecutor(use_process_isolation=True)
-
-    try:
-        result = evaluate_single_data(args, data, client, executor)
-        return result
-    finally:
-        # Ensure cleanup of persistent worker
-        del executor
-
-def evaluate_multi_images_with_cleanup(args, data_list, client):
-    """Wrapper function for multi-image evaluation with proper cleanup"""
-    # Initialize executor with process isolation
-    executor = PythonExecutor(use_process_isolation=True)
-    
-    try:
-        results = []
-        for data in data_list:
-            try:
-                result = evaluate_single_data_multi_images(args, data, client, executor)
-                results.append(result)
-            except Exception as e:
-                print(f"Error processing multi-image data: {str(e)}")
-                results.append((None, f"Error: {str(e)}"))
-                # Reset the executor for the next item
-                executor.reset()
-        
-        return results
-    finally:
-        # Ensure cleanup of persistent worker
-        del executor
-
-def evaluate_video_with_cleanup(args, data_list, client):
-    """Wrapper function for video evaluation with proper cleanup"""
-    # Initialize executor with process isolation
-    executor = PythonExecutor(use_process_isolation=True)
-    
-    try:
-        results = []
-        for data in data_list:
-            try:
-                result = evaluate_single_data_video(args, data, client, executor)
-                results.append(result)
-            except Exception as e:
-                print(f"Error processing video data: {str(e)}")
-                results.append((None, f"Error: {str(e)}"))
-                # Reset the executor for the next item
-                executor.reset()
-        
-        return results
-    finally:
-        # Ensure cleanup of persistent worker
-        del executor