add feedback storage

app.py

@@ -15,12 +15,17 @@ from skimage import measure
 from matplotlib import cm
 from glob import glob
 from natsort import natsorted
+from huggingface_hub import HfApi, upload_file
 
 # ===== 导入三个推理模块 =====
 from inference_seg import load_model as load_seg_model, run as run_seg
 from inference_count import load_model as load_count_model, run as run_count
 from inference_track import load_model as load_track_model, run as run_track
 
+HF_TOKEN = os.getenv("HF_TOKEN")
+DATASET_REPO = "phoebe777777/celltool_feedback"
+
+
 # ===== 清理缓存目录 =====
 print("===== clearing cache =====")
 cache_path = os.path.expanduser("~/.cache/")
@@ -74,6 +79,54 @@ load_all_models()
 DATASET_DIR = Path("solver_cache")
 DATASET_DIR.mkdir(parents=True, exist_ok=True)
 
+def save_feedback_to_hf(query_id, feedback_type, feedback_text=None, img_path=None, bboxes=None):
+    """保存反馈到 Hugging Face Dataset"""
+    
+    # 如果没有 token，回退到本地存储
+    if not HF_TOKEN:
+        print("⚠️ No HF_TOKEN found, using local storage")
+        save_feedback(query_id, feedback_type, feedback_text, img_path, bboxes)
+        return
+    
+    feedback_data = {
+        "query_id": query_id,
+        "feedback_type": feedback_type,
+        "feedback_text": feedback_text,
+        "image_path": img_path,
+        "bboxes": str(bboxes),  # 转为字符串
+        "datetime": time.strftime("%Y-%m-%d %H:%M:%S"),
+        "timestamp": time.time()
+    }
+    
+    try:
+        api = HfApi()
+        
+        # 创建临时文件
+        filename = f"feedback_{query_id}_{int(time.time())}.json"
+        
+        with open(filename, 'w', encoding='utf-8') as f:
+            json.dump(feedback_data, f, indent=2, ensure_ascii=False)
+        
+        # 上传到 dataset
+        api.upload_file(
+            path_or_fileobj=filename,
+            path_in_repo=f"data/{filename}",
+            repo_id=DATASET_REPO,
+            repo_type="dataset",
+            token=HF_TOKEN
+        )
+        
+        # 清理本地文件
+        os.remove(filename)
+        
+        print(f"✅ Feedback saved to HF Dataset: {DATASET_REPO}")
+        
+    except Exception as e:
+        print(f"⚠️ Failed to save to HF Dataset: {e}")
+        # 回退到本地存储
+        save_feedback(query_id, feedback_type, feedback_text, img_path, bboxes)
+
+
 def save_feedback(query_id, feedback_type, feedback_text=None, img_path=None, bboxes=None):
     """保存用户反馈到JSON文件"""
     feedback_data = {
@@ -999,7 +1052,15 @@ with gr.Blocks(
                     img_path = annot_val[0] if annot_val and len(annot_val) > 0 else None
                     bboxes = annot_val[1] if annot_val and len(annot_val) > 1 else []
                     
-                    save_feedback(
+                    # save_feedback(
+                    #     query_id=query_id,
+                    #     feedback_type=f"score_{int(score)}",
+                    #     feedback_text=comment,
+                    #     img_path=img_path,
+                    #     bboxes=bboxes
+                    # )
+                    # 使用 HF 存储
+                    save_feedback_to_hf(
                         query_id=query_id,
                         feedback_type=f"score_{int(score)}",
                         feedback_text=comment,
@@ -1173,7 +1234,15 @@ with gr.Blocks(
                     img_path = annot_val[0] if annot_val and len(annot_val) > 0 else None
                     bboxes = annot_val[1] if annot_val and len(annot_val) > 1 else []
                     
-                    save_feedback(
+                    # save_feedback(
+                    #     query_id=query_id,
+                    #     feedback_type=f"score_{int(score)}",
+                    #     feedback_text=comment,
+                    #     img_path=img_path,
+                    #     bboxes=bboxes
+                    # )
+                    # 使用 HF 存储
+                    save_feedback_to_hf(
                         query_id=query_id,
                         feedback_type=f"score_{int(score)}",
                         feedback_text=comment,
@@ -1487,7 +1556,15 @@ with gr.Blocks(
                     img_path = annot_val[0] if annot_val and len(annot_val) > 0 else None
                     bboxes = annot_val[1] if annot_val and len(annot_val) > 1 else []
                     
-                    save_feedback(
+                    # save_feedback(
+                    #     query_id=query_id,
+                    #     feedback_type=f"score_{int(score)}",
+                    #     feedback_text=comment,
+                    #     img_path=img_path,
+                    #     bboxes=bboxes
+                    # )
+                    # 使用 HF 存储
+                    save_feedback_to_hf(
                         query_id=query_id,
                         feedback_type=f"score_{int(score)}",
                         feedback_text=comment,
@@ -1516,7 +1593,7 @@ with gr.Blocks(
 if __name__ == "__main__":
     demo.queue().launch(
         server_name="0.0.0.0",
-        server_port=7862,
+        server_port=7860,
         share=False,
         ssr_mode=False,
         show_error=True,