import os import sys import json import base64 import re import asyncio import aiofiles from tqdm.asyncio import tqdm_asyncio # Used for progress bar in async tasks import boto3 # Import boto3 for AWS Bedrock interaction from rouge import Rouge Test_Model = "Claude" # Define the model name for testing # ===== Configuration Items ===== TEST_JSON_PATH = "/code/CogReasoner/Test/VisualWebBench_webqa.json" # 测试数据路径 MODEL_NAME = "us.anthropic.claude-sonnet-4-20250514-v1:0" # Specify the Claude model name for inference MAX_SAMPLE = 245 # Maximum number of samples to test MAX_CONCURRENT_REQUESTS = 1 # Maximum concurrent requests (set to 10 for async processing) ACCURACY_PRINT_INTERVAL = 10 # Print current accuracy after processing this many samples OUTPUT_JSON_PATH = f"/code/CogReasoner/Code/Evalaute/Result/Test-{Test_Model}-VisualWebBench_WebQA.json" # Path where inference results will be saved FIXED_PROMPT = ( "You are given a screenshot of a webpage. Please generate the main text within the screenshot, " "which can be regarded as the heading of the webpage.\n\n" "You should directly tell me the main content, and do not output any explanation or any other contents." ) # ===== AWS Bedrock Claude Client Class ===== class BedrockClaudeClient: """ A client for interacting with the Claude model on AWS Bedrock. AWS credentials are configured directly in this code (for demonstration). """ def __init__(self, access_key, secret_key, region_name, model_id): """ Initializes the Bedrock runtime client with provided keys and region info. """ self.model_id = model_id try: self.bedrock_client = boto3.client( service_name='bedrock-runtime', region_name=region_name, aws_access_key_id=access_key, aws_secret_access_key=secret_key ) print(f"Boto3 client successfully created in region '{region_name}' for model '{self.model_id}'!") except Exception as e: raise ConnectionError(f"Failed to create Bedrock client: {e}. Please check your AWS credentials and region name.") def _parse_data_url(self, data_url): """ Parses a Data URL (e.g., data:image/png;base64,iVBOR...) Extracts media_type and Base64 data. """ if not data_url.startswith("data:"): print(f"Warning: Not a standard Data URL format: {data_url}") return None, None parts = data_url.split(',', 1) if len(parts) < 2: print(f"Warning: Incomplete Data URL format: {data_url}") return None, None metadata = parts[0][len("data:"):].split(';') media_type = metadata[0] base64_data = parts[1] if "base64" not in metadata: print(f"Warning: Data URL does not contain 'base64' encoding identifier: {data_url}") return None, None return base64_data, media_type # This method is kept as `def` (synchronous) because `boto3` client calls are synchronous. # It will be called within `asyncio.to_thread` in `process_item` to avoid blocking the event loop. def chat(self, messages, max_tokens=1024, temperature=0.7): """ Sends messages to the Claude model and gets a reply. This function is now fully compatible with OpenAI-format message lists, including handling system messages and embedded base64 image_url. """ if not hasattr(self, 'bedrock_client'): raise RuntimeError("Bedrock client not successfully initialized.") claude_system_message = None claude_messages_payload = [] # Convert OpenAI format to Claude Bedrock format for openai_msg in messages: role = openai_msg.get("role") content = openai_msg.get("content") if role == "system": claude_system_message = content elif role in ["user", "assistant"]: claude_content_blocks = [] if isinstance(content, str): claude_content_blocks.append({"type": "text", "text": content}) elif isinstance(content, list): for item in content: if item.get("type") == "text": claude_content_blocks.append({"type": "text", "text": item.get("text", "")}) elif item.get("type") == "image_url": image_url_dict = item.get("image_url", {}) url = image_url_dict.get("url") if url: base64_data, media_type = self._parse_data_url(url) if base64_data and media_type: claude_content_blocks.append({ "type": "image", "source": { "type": "base64", "media_type": media_type, "data": base64_data } }) else: print(f"Warning: Could not parse image data from Data URL {url}, skipping this content block.") else: print(f"Warning: Unsupported OpenAI content type: {item.get('type')}. Skipping this content block.") if claude_content_blocks: claude_messages_payload.append({"role": role, "content": claude_content_blocks}) else: print(f"Warning: '{role}' role message has no valid content, skipping.") else: print(f"Warning: Unsupported OpenAI message role: {role}. Skipping this message.") if not claude_messages_payload: raise ValueError("No valid 'user' or 'assistant' role messages to send to Claude after conversion.") # Build the request body body = { "anthropic_version": "bedrock-2023-05-31", "max_tokens": max_tokens, "temperature": temperature, "messages": claude_messages_payload } # Add system message if it exists if claude_system_message: body["system"] = claude_system_message try: response = self.bedrock_client.invoke_model( modelId=self.model_id, body=json.dumps(body) ) response_body = json.loads(response.get('body').read()) response_text = "" if response_body.get('content'): for content_block in response_body['content']: if content_block.get('type') == 'text': response_text += content_block['text'] usage = response_body.get('usage', {}) prompt_tokens = usage.get('input_tokens', 0) completion_tokens = usage.get('output_tokens', 0) return { "response_text": response_text, "prompt_tokens": prompt_tokens, "completion_tokens": completion_tokens } except Exception as e: error_message = str(e) if hasattr(e, 'response') and 'Error' in e.response: error_message = f"{e.response['Error'].get('Code', '')}: {e.response['Error'].get('Message', '')}" raise RuntimeError(f"Error calling Claude model: {error_message}") def eval_webqa(preds, golds, **kwargs): """ 计算 WebQA 的 F1 分数。 preds: 预测答案的列表。 golds: 参考答案的列表的列表 (每个问题可以有多个参考答案)。 """ assert len(preds) == len(golds), "预测数量和参考答案数量必须一致" f1_scores = [] # 注意：Rouge() 实例在循环外创建以提高效率 rouge = Rouge(metrics=['rouge-1']) for pred, gold_list in zip(preds, golds): if not pred: pred = " " # 避免空字符串导致ROUGE计算异常 # 计算当前预测与所有参考答案的 F1 分数，并取最大值 # gold_list 是当前问题的正确答案列表，例如 ['Sawfish'] 或 ['Answer A', 'Answer B'] try: current_f1 = max([rouge.get_scores([pred], [gold], avg=True)['rouge-1']['f'] for gold in gold_list]) f1_scores.append(current_f1) except Exception as e: # 如果发生错误（例如 gold_list 为空），则记录为0分并打印警告 print(f"Warning: Could not compute F1 score for pred='{pred}' and gold_list='{gold_list}'. Error: {e}") f1_scores.append(0.0) # 确保 f1_scores 不为空，以避免除以零的错误 if not f1_scores: return dict(f1=0.0) return dict( f1=sum(f1_scores) / len(f1_scores) * 100 ) # ===== Asynchronously Process Single Sample ===== async def process_item(index, item, sem, claude_client_instance, stats): async with sem: image_path = item["images"][0] # --- 关键修改 --- # `eval_webqa` 需要一个答案列表，所以我们将单个答案包装成列表 # 即使只有一个正确答案，也需要是列表形式，例如 ['Sawfish'] ground_truth = [item["messages"][1]["content"].strip()] user_prompt = item["messages"][0]["content"] # user_prompt 是包含 和问题的完整内容 # 读取并编码图片 async with aiofiles.open(image_path, "rb") as f: content = await f.read() encoded_image = base64.b64encode(content).decode("utf-8") image_data_uri = f"data:image/png;base64,{encoded_image}" prompt_text = user_prompt.replace("\n", "").strip() # 构造消息 messages =[ {"role": "system", "content": "You are a helpful assistant."}, { "role": "user", "content": [ {"type": "image_url", "image_url": {"url": image_data_uri}}, {"type": "text", "text": prompt_text}, ], }, ] try: # Send inference request to the model # The ClaudeClient.chat method is synchronous, so we run it in a thread pool response_data = await asyncio.to_thread( claude_client_instance.chat, messages = messages, max_tokens=2048, # Limit max length of generated text temperature=0.1 # Control randomness of generated text ) pred_text = response_data['response_text'].strip() # Extract model generated text content except Exception as e: pred_text = f"[ERROR] {str(e)}" # Capture exception and log error message await asyncio.sleep(10) # 暂停0.5秒。根据你的RPS限制调整这个值。 # 例如，如果RPS是2，你可能需要等待0.5秒。 return { "image": image_path, "ground_truth": ground_truth, # ground_truth 现在是一个列表 "prediction": pred_text, } # ===== Main Function ===== async def main(): """ Main execution function, responsible for loading test data, creating and running asynchronous tasks, collecting results, and saving them. """ # AWS credentials and model ID (fill in your actual values) # WARNING: Hardcoding credentials directly is insecure. For production, use environment variables, # AWS CLI configuration, or IAM roles. aws_access_key_id = "AKIAYEDGY53YI74GRHPL" # REPLACE WITH YOUR AWS ACCESS KEY ID aws_secret_access_key = "yAQVOVB1bbeykes6SCGEEuZZlzWPLaFtiEOGyNMk" # REPLACE WITH YOUR AWS SECRET ACCESS KEY aws_region_name = "us-east-1" aws_model_id = MODEL_NAME # Using MODEL_NAME from config # Initialize AWS Bedrock Claude client try: claude_client = BedrockClaudeClient( access_key=aws_access_key_id, secret_key=aws_secret_access_key, region_name=aws_region_name, model_id=aws_model_id ) except Exception as e: print(f"Failed to initialize Bedrock Claude client: {e}") sys.exit(1) # Exit program # Read test set JSON file with open(TEST_JSON_PATH, "r", encoding="utf-8") as f: test_data = json.load(f)[:MAX_SAMPLE] # Load data and truncate based on MAX_SAMPLE sem = asyncio.Semaphore(MAX_CONCURRENT_REQUESTS) stats = {"total": 0, "correct": 0} # Initialize statistics tasks = [process_item(i, item, sem, claude_client, stats) for i, item in enumerate(test_data)] print(f"\n🚀 Starting evaluation for WebQA on {len(tasks)} samples...\n") results = await tqdm_asyncio.gather(*tasks) predictions = [r["prediction"] for r in results] references = [r["ground_truth"] for r in results] # 这现在是一个列表的列表 # --- 关键修改 --- # 调用新的评估函数 metrics = eval_webqa(predictions, references) output = { "task": "WebQA", "model": Test_Model, "metrics": metrics, "results": results, } # 保存结果 os.makedirs(os.path.dirname(OUTPUT_JSON_PATH), exist_ok=True) with open(OUTPUT_JSON_PATH, "w", encoding="utf-8") as f: json.dump(output, f, indent=2, ensure_ascii=False) print(f"\n✅ Evaluation Complete!") print(f"📊 Metrics: {json.dumps(metrics, indent=2)}") print(f"📁 Results saved at: {OUTPUT_JSON_PATH}") # ===== Entry Point ===== if __name__ == "__main__": asyncio.run(main()) # Run the main asynchronous function sys.exit(0) # Force exit to prevent the async event loop from hanging