Local sentiment analysis upload.

SentimentAnalysisModel/WeiboSentiment_Finetuned/BertChinese-Lora/predict.py

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+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+import re
+
+def preprocess_text(text):
+    return text
+
+def main():
+    print("正在加载微博情感分析模型...")
+    
+    # 使用HuggingFace预训练模型
+    model_name = "wsqstar/GISchat-weibo-100k-fine-tuned-bert"
+    local_model_path = "./model"
+    
+    try:
+        # 检查本地是否已有模型
+        import os
+        if os.path.exists(local_model_path):
+            print("从本地加载模型...")
+            tokenizer = AutoTokenizer.from_pretrained(local_model_path)
+            model = AutoModelForSequenceClassification.from_pretrained(local_model_path)
+        else:
+            print("首次使用，正在下载模型到本地...")
+            # 下载并保存到本地
+            tokenizer = AutoTokenizer.from_pretrained(model_name)
+            model = AutoModelForSequenceClassification.from_pretrained(model_name)
+            
+            # 保存到本地
+            tokenizer.save_pretrained(local_model_path)
+            model.save_pretrained(local_model_path)
+            print(f"模型已保存到: {local_model_path}")
+        
+        # 设置设备
+        device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        model.to(device)
+        model.eval()
+        print(f"模型加载成功! 使用设备: {device}")
+        
+    except Exception as e:
+        print(f"模型加载失败: {e}")
+        print("请检查网络连接或使用pipeline方式")
+        return
+    
+    print("\n============= 微博情感分析 =============")
+    print("输入微博内容进行分析 (输入 'q' 退出):")
+    
+    while True:
+        text = input("\n请输入微博内容: ")
+        if text.lower() == 'q':
+            break
+        
+        if not text.strip():
+            print("输入不能为空，请重新输入")
+            continue
+        
+        try:
+            # 预处理文本
+            processed_text = preprocess_text(text)
+            
+            # 分词编码
+            inputs = tokenizer(
+                processed_text,
+                max_length=512,
+                padding=True,
+                truncation=True,
+                return_tensors='pt'
+            )
+            
+            # 转移到设备
+            inputs = {k: v.to(device) for k, v in inputs.items()}
+            
+            # 预测
+            with torch.no_grad():
+                outputs = model(**inputs)
+                logits = outputs.logits
+                probabilities = torch.softmax(logits, dim=1)
+                prediction = torch.argmax(probabilities, dim=1).item()
+            
+            # 输出结果
+            confidence = probabilities[0][prediction].item()
+            label = "正面情感" if prediction == 1 else "负面情感"
+            
+            print(f"预测结果: {label} (置信度: {confidence:.4f})")
+            
+        except Exception as e:
+            print(f"预测时发生错误: {e}")
+            continue
+
+if __name__ == "__main__":
+    main()