Update app.py

app.py

@@ -7,6 +7,7 @@ import tempfile
 from huggingface_hub import hf_hub_download, list_repo_files
 import logging
 import traceback
+import sklearn
 
 # Set up logging
 logging.basicConfig(
@@ -26,12 +27,16 @@ st.set_page_config(
 def load_model():
     """Load model from Hugging Face Hub"""
     try:
+        # 检查scikit-learn版本
+        logger.info(f"Using scikit-learn version: {sklearn.__version__}")
+        st.write(f"Using scikit-learn version: {sklearn.__version__}")
+        
         # 首先列出仓库中的所有文件
         logger.info("Listing repository files...")
         try:
             files = list_repo_files("404Brain-Not-Found-yeah/healing-music-classifier")
             logger.info(f"Repository files: {files}")
-            st.write("Available files in repository:", files)  # 显示在界面上
+            st.write("Available files in repository:", files)
         except Exception as e:
             logger.error(f"Error listing repository files: {str(e)}\n{traceback.format_exc()}")
             st.error(f"Error listing repository files: {str(e)}")
@@ -50,7 +55,7 @@ def load_model():
                 local_dir="temp_models"
             )
             logger.info(f"Model downloaded to: {model_path}")
-            st.write(f"Model downloaded to: {model_path}")  # 显示在界面上
+            st.write(f"Model downloaded to: {model_path}")
         except Exception as e:
             logger.error(f"Error downloading model: {str(e)}\n{traceback.format_exc()}")
             st.error(f"Error downloading model: {str(e)}")
@@ -64,7 +69,7 @@ def load_model():
                 local_dir="temp_models"
             )
             logger.info(f"Scaler downloaded to: {scaler_path}")
-            st.write(f"Scaler downloaded to: {scaler_path}")  # 显示在界面上
+            st.write(f"Scaler downloaded to: {scaler_path}")
         except Exception as e:
             logger.error(f"Error downloading scaler: {str(e)}\n{traceback.format_exc()}")
             st.error(f"Error downloading scaler: {str(e)}")
@@ -91,10 +96,21 @@ def load_model():
             st.write(f"Model file size: {model_size} bytes")
             st.write(f"Scaler file size: {scaler_size} bytes")
 
-            model = joblib.load(model_path)
-            scaler = joblib.load(scaler_path)
+            # 尝试使用不同的pickle协议加载
+            try:
+                model = joblib.load(model_path)
+                scaler = joblib.load(scaler_path)
+            except Exception as load_error:
+                logger.warning(f"Standard loading failed: {str(load_error)}")
+                # 尝试使用兼容模式加载
+                import pickle
+                with open(model_path, 'rb') as f:
+                    model = pickle.load(f, encoding='latin1')
+                with open(scaler_path, 'rb') as f:
+                    scaler = pickle.load(f, encoding='latin1')
+
             logger.info("Model and scaler loaded successfully")
-            st.success("Model and scaler loaded successfully!")  # 显示成功消息
+            st.success("Model and scaler loaded successfully!")
             return model, scaler
         except Exception as e:
             logger.error(f"Error loading model/scaler files: {str(e)}\n{traceback.format_exc()}")