Delete inference_example.py

inference_example.py

@@ -1,91 +0,0 @@
-"""
-情感检测推理示例
-使用detection_hug模型进行情感分类
-"""
-import torch
-from transformers import BertTokenizer
-from detection_model import EmotionDetectionModel
-
-def load_model(model_path="model.pt", device="cpu"):
-    """加载模型"""
-    print(f"加载模型: {model_path}")
-    
-    # 加载分词器
-    tokenizer = BertTokenizer.from_pretrained(".")
-    
-    # 创建模型
-    model = EmotionDetectionModel(
-        model_name="bert-base-chinese",
-        num_emotions=6,
-        dropout=0.1
-    )
-    
-    # 加载权重
-    checkpoint = torch.load(model_path, map_location=device)
-    if isinstance(checkpoint, dict) and 'model_state_dict' in checkpoint:
-        model.load_state_dict(checkpoint['model_state_dict'])
-    else:
-        model.load_state_dict(checkpoint)
-    
-    model = model.to(device)
-    model.eval()
-    
-    print("✅ 模型加载成功")
-    return model, tokenizer
-
-def predict(text, model, tokenizer, device="cpu"):
-    """预测单个文本的情感"""
-    # 情感标签
-    EMOTIONS = ["sadness", "joy", "love", "anger", "fear", "surprise"]
-    
-    # 编码
-    encoding = tokenizer(
-        text,
-        padding='max_length',
-        truncation=True,
-        max_length=512,
-        return_tensors='pt'
-    )
-    
-    input_ids = encoding['input_ids'].to(device)
-    attention_mask = encoding['attention_mask'].to(device)
-    
-    # 推理
-    with torch.no_grad():
-        outputs = model(input_ids=input_ids, attention_mask=attention_mask)
-        logits = outputs['logits']
-        probabilities = torch.softmax(logits, dim=-1)
-        predicted_id = torch.argmax(probabilities, dim=-1).item()
-        confidence = probabilities[0, predicted_id].item()
-    
-    return {
-        'emotion': EMOTIONS[predicted_id],
-        'confidence': confidence,
-        'all_probabilities': {
-            EMOTIONS[i]: float(probabilities[0, i])
-            for i in range(len(EMOTIONS))
-        }
-    }
-
-if __name__ == "__main__":
-    # 示例
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    model, tokenizer = load_model(device=device)
-    
-    # 测试文本
-    test_texts = [
-        "我今天很开心！",
-        "这让我感到非常难过。",
-        "我爱你。"
-    ]
-    
-    print("\n" + "="*60)
-    print("情感检测结果")
-    print("="*60)
-    
-    for text in test_texts:
-        result = predict(text, model, tokenizer, device)
-        print(f"\n文本: {text}")
-        print(f"情感: {result['emotion']}")
-        print(f"置信度: {result['confidence']:.4f}")
-