update data saving

src/app.py

@@ -105,54 +105,20 @@ def load_model(from_pretrained, base_model, cache_dir, device):
         model.set_criterion_fn('mean')
     return model
 
-import json
-from datetime import datetime
-
 # -----------------
-# Result Feedback
+# Result Feedback Module Import
 # -----------------
-def save_feedback(text, domain, statistics, p_value, feedback_type):
-    """
-    保存用户反馈到 JSON 文件
-    feedback_type: 'expected' 或 'unexpected'
-    """
-    # 确定保存路径（根据环境选择）
-    if os.environ.get('SPACE_ID'):
-        feedback_dir = Path('/tmp/feedback_data')
-    else:
-        feedback_dir = APP_DIR / 'feedback_data'
-    
-    feedback_dir.mkdir(exist_ok=True, parents=True)
-    feedback_file = feedback_dir / 'user_feedback.json'
-    
-    # 准备反馈数据
-    feedback_entry = {
-        'timestamp': datetime.now().isoformat(),
-        'text': text,
-        'domain': domain,
-        'statistics': float(statistics),
-        'p_value': float(p_value),
-        'feedback': feedback_type
-    }
-    
-    # 读取现有数据
-    if feedback_file.exists():
-        try:
-            with open(feedback_file, 'r', encoding='utf-8') as f:
-                feedback_data = json.load(f)
-        except:
-            feedback_data = []
-    else:
-        feedback_data = []
-    
-    # 添加新反馈
-    feedback_data.append(feedback_entry)
-    
-    # 保存到文件
-    with open(feedback_file, 'w', encoding='utf-8') as f:
-        json.dump(feedback_data, f, ensure_ascii=False, indent=2)
-    
-    return feedback_file
+from feedback import FeedbackManager
+
+# Initialize Feedback Manager with HF dataset
+# 请将 'your-username/your-dataset-name' 替换为您的实际 HF 数据集仓库 ID
+# 确保在环境变量中设置了 HF_TOKEN 以访问私有数据集
+FEEDBACK_DATASET_ID = os.environ.get('FEEDBACK_DATASET_ID', 'mamba413/user-feedback')
+feedback_manager = FeedbackManager(
+    dataset_repo_id=FEEDBACK_DATASET_ID,
+    hf_token=os.environ.get('HF_TOKEN'),
+    local_backup=True  # 保留本地备份
+)
 
 # -----------------
 # Configuration
@@ -340,15 +306,18 @@ if detect_clicked:
                 with feedback_col1:
                     if st.button("✅ Expected", use_container_width=True, type="secondary", key=f"expected_btn_{hash(text_input[:50])}"):
                         try:
-                            feedback_file = save_feedback(
+                            success, message = feedback_manager.save_feedback(
                                 current_text,
                                 current_domain,
                                 current_statistics,
                                 current_pvalue,
                                 'expected'
                             )
-                            st.success("✅ Thank you for your feedback!")
-                            st.caption(f"💾 Saved to: `{feedback_file.name}`")
+                            if success:
+                                st.success("✅ Thank you for your feedback!")
+                                st.caption(f"💾 {message}")
+                            else:
+                                st.error(f"Failed to save feedback: {message}")
                         except Exception as e:
                             st.error(f"Failed to save feedback: {str(e)}")
                             import traceback
@@ -357,15 +326,18 @@ if detect_clicked:
                 with feedback_col2:
                     if st.button("❌ Unexpected", use_container_width=True, type="secondary", key=f"unexpected_btn_{hash(text_input[:50])}"):
                         try:
-                            feedback_file = save_feedback(
+                            success, message = feedback_manager.save_feedback(
                                 current_text,
                                 current_domain,
                                 current_statistics,
                                 current_pvalue,
                                 'unexpected'
                             )
-                            st.warning("❌ Feedback recorded! This will help us improve.")
-                            st.caption(f"💾 Saved to: `{feedback_file.name}`")
+                            if success:
+                                st.warning("❌ Feedback recorded! This will help us improve.")
+                                st.caption(f"💾 {message}")
+                            else:
+                                st.error(f"Failed to save feedback: {message}")
                         except Exception as e:
                             st.error(f"Failed to save feedback: {str(e)}")
                             import traceback

src/feedback.py

@@ -0,0 +1,272 @@
+import os
+import json
+from datetime import datetime
+from pathlib import Path
+from huggingface_hub import HfApi, upload_file, hf_hub_download
+from typing import Optional
+import pandas as pd
+
+class FeedbackManager:
+    """管理用户反馈，支持保存到 Hugging Face 私有数据集"""
+    
+    def __init__(
+        self, 
+        dataset_repo_id: str = None,
+        hf_token: str = None,
+        local_backup: bool = True
+    ):
+        """
+        初始化 FeedbackManager
+        
+        Args:
+            dataset_repo_id: Hugging Face 数据集仓库 ID (格式: username/dataset-name)
+            hf_token: Hugging Face API token (用于私有数据集)
+            local_backup: 是否在本地保留备份
+        """
+        self.dataset_repo_id = dataset_repo_id
+        self.hf_token = hf_token or os.environ.get('HF_TOKEN')
+        self.local_backup = local_backup
+        
+        # 初始化 HF API
+        if self.dataset_repo_id and self.hf_token:
+            self.api = HfApi(token=self.hf_token)
+            # 确保数据集存在
+            self._ensure_dataset_exists()
+        else:
+            self.api = None
+            print("⚠️ No HF dataset configured. Will only save locally.")
+        
+        # 设置本地存储路径
+        if os.environ.get('SPACE_ID'):
+            self.local_dir = Path('/tmp/feedback_data')
+        else:
+            self.local_dir = Path(__file__).parent / 'feedback_data'
+        
+        self.local_dir.mkdir(exist_ok=True, parents=True)
+        self.local_file = self.local_dir / 'user_feedback.json'
+        
+    def _ensure_dataset_exists(self):
+        """确保 HF 数据集存在，如果不存在则创建"""
+        try:
+            from huggingface_hub import create_repo
+            # 尝试创建数据集仓库（如果已存在会抛出异常）
+            try:
+                create_repo(
+                    repo_id=self.dataset_repo_id,
+                    token=self.hf_token,
+                    private=True,
+                    repo_type="dataset"
+                )
+                print(f"✅ Created new private dataset: {self.dataset_repo_id}")
+                
+                # 创建初始的 README.md
+                readme_content = f"""---
+license: mit
+---
+
+# AdaDetectGPT User Feedback Dataset
+
+This dataset contains user feedback from the AdaDetectGPT detection system.
+
+## Data Format
+
+Each entry contains:
+- `timestamp`: When the feedback was submitted
+- `text`: The text that was analyzed
+- `domain`: The domain selected for analysis
+- `statistics`: The computed statistics value
+- `p_value`: The p-value from the detection
+- `feedback`: User feedback (expected/unexpected)
+"""
+                readme_file = self.local_dir / 'README.md'
+                readme_file.write_text(readme_content)
+                
+                upload_file(
+                    path_or_fileobj=str(readme_file),
+                    path_in_repo="README.md",
+                    repo_id=self.dataset_repo_id,
+                    repo_type="dataset",
+                    token=self.hf_token
+                )
+                
+            except Exception as e:
+                if "already exists" not in str(e):
+                    print(f"⚠️ Dataset check: {e}")
+                    
+        except Exception as e:
+            print(f"⚠️ Could not verify dataset: {e}")
+    
+    def _load_existing_data(self) -> list:
+        """从 HF 数据集加载现有数据"""
+        existing_data = []
+        
+        # 首先尝试从 HF 数据集加载
+        if self.api and self.dataset_repo_id:
+            try:
+                # 下载现有的反馈文件
+                local_path = hf_hub_download(
+                    repo_id=self.dataset_repo_id,
+                    filename="feedback_data.json",
+                    repo_type="dataset",
+                    token=self.hf_token,
+                    cache_dir=str(self.local_dir)
+                )
+                with open(local_path, 'r', encoding='utf-8') as f:
+                    existing_data = json.load(f)
+                print(f"📥 Loaded {len(existing_data)} existing feedback entries from HF")
+            except Exception as e:
+                # 文件可能还不存在
+                if "404" not in str(e):
+                    print(f"⚠️ Could not load from HF dataset: {e}")
+        
+        # 如果 HF 加载失败，尝试本地文件
+        if not existing_data and self.local_file.exists():
+            try:
+                with open(self.local_file, 'r', encoding='utf-8') as f:
+                    existing_data = json.load(f)
+                print(f"📥 Loaded {len(existing_data)} existing feedback entries from local")
+            except Exception as e:
+                print(f"⚠️ Could not load local data: {e}")
+        
+        return existing_data
+    
+    def save_feedback(
+        self, 
+        text: str, 
+        domain: str, 
+        statistics: float, 
+        p_value: float, 
+        feedback_type: str
+    ) -> tuple[bool, str]:
+        """
+        保存用户反馈到 HF 数据集和/或本地文件
+        
+        Args:
+            text: 被检测的文本
+            domain: 选择的领域
+            statistics: 统计值
+            p_value: p值
+            feedback_type: 'expected' 或 'unexpected'
+            
+        Returns:
+            (success, message): 是否成功和相关消息
+        """
+        # 准备反馈数据
+        feedback_entry = {
+            'timestamp': datetime.now().isoformat(),
+            'text': text,
+            'domain': domain,
+            'statistics': float(statistics),
+            'p_value': float(p_value),
+            'feedback': feedback_type
+        }
+        
+        # 加载现有数据
+        feedback_data = self._load_existing_data()
+        
+        # 添加新反馈
+        feedback_data.append(feedback_entry)
+        
+        success = False
+        messages = []
+        
+        # 保存到本地（作为备份）
+        if self.local_backup:
+            try:
+                with open(self.local_file, 'w', encoding='utf-8') as f:
+                    json.dump(feedback_data, f, ensure_ascii=False, indent=2)
+                messages.append(f"💾 Local backup saved")
+                success = True
+            except Exception as e:
+                messages.append(f"❌ Local save failed: {e}")
+        
+        # 上传到 HF 数据集
+        if self.api and self.dataset_repo_id:
+            try:
+                # 保存为 JSON 文件
+                upload_file(
+                    path_or_fileobj=str(self.local_file),
+                    path_in_repo="feedback_data.json",
+                    repo_id=self.dataset_repo_id,
+                    repo_type="dataset",
+                    token=self.hf_token,
+                    commit_message=f"Add feedback: {feedback_type} at {feedback_entry['timestamp']}"
+                )
+                
+                # 同时创建/更新 CSV 版本（方便查看）
+                df = pd.DataFrame(feedback_data)
+                csv_file = self.local_dir / 'feedback_data.csv'
+                df.to_csv(csv_file, index=False)
+                
+                upload_file(
+                    path_or_fileobj=str(csv_file),
+                    path_in_repo="feedback_data.csv",
+                    repo_id=self.dataset_repo_id,
+                    repo_type="dataset",
+                    token=self.hf_token,
+                    commit_message=f"Update CSV: {len(feedback_data)} total entries"
+                )
+                
+                messages.append(f"☁️ Uploaded to HF dataset: {self.dataset_repo_id}")
+                success = True
+                
+            except Exception as e:
+                messages.append(f"⚠️ HF upload failed: {e}")
+                # 如果 HF 上传失败但本地保存成功，仍然返回成功
+                success = success or self.local_backup
+        
+        return success, " | ".join(messages)
+    
+    def get_feedback_stats(self) -> dict:
+        """获取反馈统计信息"""
+        feedback_data = self._load_existing_data()
+        
+        if not feedback_data:
+            return {
+                'total_count': 0,
+                'expected_count': 0,
+                'unexpected_count': 0,
+                'domains': {}
+            }
+        
+        df = pd.DataFrame(feedback_data)
+        stats = {
+            'total_count': len(df),
+            'expected_count': len(df[df['feedback'] == 'expected']),
+            'unexpected_count': len(df[df['feedback'] == 'unexpected']),
+            'domains': df['domain'].value_counts().to_dict() if 'domain' in df.columns else {}
+        }
+        
+        return stats
+
+
+# 便捷函数（向后兼容）
+_default_manager: Optional[FeedbackManager] = None
+
+def init_feedback_manager(dataset_repo_id: str = None, hf_token: str = None):
+    """初始化全局反馈管理器"""
+    global _default_manager
+    _default_manager = FeedbackManager(
+        dataset_repo_id=dataset_repo_id,
+        hf_token=hf_token
+    )
+    return _default_manager
+
+def save_feedback(text: str, domain: str, statistics: float, p_value: float, feedback_type: str):
+    """
+    使用默认管理器保存反馈（向后兼容）
+    """
+    global _default_manager
+    if _default_manager is None:
+        # 从环境变量读取配置
+        dataset_repo_id = os.environ.get('FEEDBACK_DATASET_ID')
+        _default_manager = FeedbackManager(dataset_repo_id=dataset_repo_id)
+    
+    success, message = _default_manager.save_feedback(
+        text, domain, statistics, p_value, feedback_type
+    )
+    
+    if not success:
+        raise Exception(f"Failed to save feedback: {message}")
+    
+    return message