Update app.py

app.py

@@ -4,12 +4,13 @@ import numpy as np
 from predict import extract_features
 import os
 import tempfile
-from huggingface_hub import hf_hub_download
+from huggingface_hub import hf_hub_download, list_repo_files
 import logging
+import traceback
 
 # Set up logging
 logging.basicConfig(
-    level=logging.INFO,
+    level=logging.DEBUG,
     format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
 )
 logger = logging.getLogger(__name__)
@@ -25,20 +26,84 @@ st.set_page_config(
 def load_model():
     """Load model from Hugging Face Hub"""
     try:
+        # 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
+        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)}")
+            return None, None
+
+        # 创建临时目录
+        os.makedirs("temp_models", exist_ok=True)
+        logger.info("Created temp_models directory")
+
         logger.info("Downloading model from Hugging Face Hub...")
-        model_path = hf_hub_download(
-            repo_id="404Brain-Not-Found-yeah/healing-music-classifier",
-            filename="models/model.joblib"
-        )
-        scaler_path = hf_hub_download(
-            repo_id="404Brain-Not-Found-yeah/healing-music-classifier",
-            filename="models/scaler.joblib"
-        )
+        # 下载模型文件
+        try:
+            model_path = hf_hub_download(
+                repo_id="404Brain-Not-Found-yeah/healing-music-classifier",
+                filename="models/model.joblib",
+                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
+        except Exception as e:
+            logger.error(f"Error downloading model: {str(e)}\n{traceback.format_exc()}")
+            st.error(f"Error downloading model: {str(e)}")
+            return None, None
+
+        # 下载scaler文件
+        try:
+            scaler_path = hf_hub_download(
+                repo_id="404Brain-Not-Found-yeah/healing-music-classifier",
+                filename="models/scaler.joblib",
+                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
+        except Exception as e:
+            logger.error(f"Error downloading scaler: {str(e)}\n{traceback.format_exc()}")
+            st.error(f"Error downloading scaler: {str(e)}")
+            return None, None
         
-        logger.info("Loading model and scaler...")
-        return joblib.load(model_path), joblib.load(scaler_path)
+        # 加载模型文件
+        try:
+            logger.info("Loading model and scaler...")
+            # 检查文件是否存在
+            if not os.path.exists(model_path):
+                logger.error(f"Model file not found at: {model_path}")
+                st.error(f"Model file not found at: {model_path}")
+                return None, None
+            if not os.path.exists(scaler_path):
+                logger.error(f"Scaler file not found at: {scaler_path}")
+                st.error(f"Scaler file not found at: {scaler_path}")
+                return None, None
+
+            # 检查文件大小
+            model_size = os.path.getsize(model_path)
+            scaler_size = os.path.getsize(scaler_path)
+            logger.info(f"Model file size: {model_size} bytes")
+            logger.info(f"Scaler file size: {scaler_size} bytes")
+            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)
+            logger.info("Model and scaler loaded successfully")
+            st.success("Model and scaler loaded successfully!")  # 显示成功消息
+            return model, scaler
+        except Exception as e:
+            logger.error(f"Error loading model/scaler files: {str(e)}\n{traceback.format_exc()}")
+            st.error(f"Error loading model/scaler files: {str(e)}")
+            return None, None
+            
     except Exception as e:
-        logger.error(f"Error loading model: {str(e)}")
+        logger.error(f"Unexpected error in load_model: {str(e)}\n{traceback.format_exc()}")
+        st.error(f"Unexpected error in load_model: {str(e)}")
         return None, None
 
 def main():
@@ -70,7 +135,7 @@ def main():
             # Load model
             model, scaler = load_model()
             if model is None or scaler is None:
-                st.error("Model loading failed. Please try again later.")
+                st.error("Model loading failed. Please check the logs for details.")
                 return
 
             progress_bar.progress(50)
@@ -84,9 +149,14 @@ def main():
             progress_bar.progress(70)
 
             # Predict
-            scaled_features = scaler.transform([features])
-            healing_probability = model.predict_proba(scaled_features)[0][1]
-            progress_bar.progress(90)
+            try:
+                scaled_features = scaler.transform([features])
+                healing_probability = model.predict_proba(scaled_features)[0][1]
+                progress_bar.progress(90)
+            except Exception as e:
+                logger.error(f"Error during prediction: {str(e)}\n{traceback.format_exc()}")
+                st.error(f"Error during prediction: {str(e)}")
+                return
 
             # Display results
             st.subheader("Analysis Results")