Try uploading to huggingface

app.py

@@ -4,13 +4,34 @@ import torch
 from transformers import AutoTokenizer
 import json
 from datetime import datetime
-import uuid
+from uuid import uuid4
+import os
+from pathlib import Path
+print(os.getenv('HF_TOKEN'))
+headers={"Authorization": f"Bearer {os.getenv('HF_TOKEN')}"}
+import requests
+response = requests.get("https://huggingface.co/datasets/hallisky/authorship-obfuscation-demo-data", headers=headers)
+print(response)
+from huggingface_hub import CommitScheduler
 
 tokenizer = AutoTokenizer.from_pretrained("gpt2")
 
 # Global variable to store the latest obfuscation result
 latest_obfuscation = {}
-user_id = str(uuid.uuid4())  # Generate a unique session-specific user ID
+user_id = str(uuid4())  # Generate a unique session-specific user ID
+
+JSON_DATASET_DIR = Path("json_dataset")
+JSON_DATASET_DIR.mkdir(parents=True, exist_ok=True)
+
+JSON_DATASET_PATH = JSON_DATASET_DIR / f"train-{user_id}.json"
+
+scheduler = CommitScheduler(
+    repo_id="authorship-obfuscation-demo-data",
+    repo_type="dataset",
+    folder_path=JSON_DATASET_DIR,
+    path_in_repo="data",
+    every=0.5
+)
 
 @spaces.GPU
 def temp(text):
@@ -18,9 +39,11 @@ def temp(text):
     return response
 
 def save_data(data):
-    with open("user_data.json", "a") as f:
-        json.dump(data, f)
-        f.write("\n")
+    with scheduler.lock:
+        with JSON_DATASET_PATH.open("a") as f:
+            json.dump(data, f)
+            f.write("\n")
+    scheduler.commit("Add new obfuscation data")
 
 def save_feedback(feedback_rating, feedback_text):
     global latest_obfuscation
@@ -77,7 +100,6 @@ def greet(input_text, length, function_words, grade_level, sarcasm, formality, v
     
     # Save the obfuscation result
     save_data(latest_obfuscation)
-    
     return response
 
 def reset_sliders():