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67 lines
2.2 KiB
67 lines
2.2 KiB
import json |
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import requests |
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import os |
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# 配置文件路径 |
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test_file_path = r"D:\workstation\chinese-roberta-wwm-ext\model-train-eval-NN\AI标注\test.json" |
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output_dir = r"D:\workstation\chinese-roberta-wwm-ext\model-train-eval-NN\AI标注\test01" |
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# 确保输出目录存在 |
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os.makedirs(output_dir, exist_ok=True) |
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# 读取测试数据 |
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with open(test_file_path, 'r', encoding='utf-8') as f: |
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test_data = json.load(f) |
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print(f"📁 加载测试数据: {len(test_data)} 条记录") |
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# 服务地址 |
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url = "http://localhost:8888/segment_batch_simple" |
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# 准备请求数据 - 直接使用原始格式 |
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broadcasts = test_data |
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print(f"🚀 开始调用双路径边界分类器批量分段接口...") |
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# 发送请求 |
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try: |
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response = requests.post(url, json=broadcasts) |
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if response.status_code == 200: |
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result = response.json() |
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print("✅ 批量分段成功!") |
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print(f"模型: {result['model']}") |
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print(f"总计: {result['total']}") |
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print(f"成功: {result['success']}") |
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print(f"失败: {result['failed']}") |
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print(f"处理时间: {result['processing_time']}秒") |
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print("\n📝 分段结果:") |
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print("=" * 80) |
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for broadcast_id, segments in result['results'].items(): |
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print(f"\n📻 {broadcast_id}:") |
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if 'error' in segments: |
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print(f"❌ 错误: {segments['error']}") |
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else: |
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for para_key, para_content in segments.items(): |
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print(f" {para_key}: {para_content}") |
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# 保存结果到文件 |
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output_file = os.path.join(output_dir, "batch_segment_results.json") |
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with open(output_file, 'w', encoding='utf-8') as f: |
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json.dump(result, f, ensure_ascii=False, indent=2) |
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print(f"\n💾 结果已保存到: {output_file}") |
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else: |
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print(f"❌ 请求失败: HTTP {response.status_code}") |
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print(response.text) |
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except requests.exceptions.ConnectionError: |
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print("❌ 连接失败，请确保双路径边界分类器服务正在运行") |
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print(" 启动命令: python simplified_dual_path_boundary_classifier_api.py") |
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print(" 服务地址: http://localhost:8888") |
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except Exception as e: |
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print(f"❌ 调用失败: {e}") |