tested huggingface dataset

.gitignore

@@ -174,5 +174,5 @@ tool_cache/
 detected_objects/
 
 # [Gradio]
-# demo_solver_cache/
+demo_solver_cache/
 backups/

app.py

@@ -25,6 +25,40 @@ from octotools.models.utils import make_json_serializable
 from utils import save_feedback
 
 
+########### Test Huggingface Dataset ###########
+from pathlib import Path
+from huggingface_hub import CommitScheduler
+
+# Add these near the top of the file with other constants
+DATASET_DIR = Path("feedback_dataset")
+DATASET_DIR.mkdir(parents=True, exist_ok=True)
+DATASET_PATH = DATASET_DIR / f"feedback-{time.strftime('%Y%m%d_%H%M%S')}.json"
+
+# Get Huggingface token from environment variable
+HF_TOKEN = os.getenv("HUGGINGFACE_TOKEN")
+
+scheduler = CommitScheduler(
+    repo_id="lupantech/OctoTools-Gradio-Demo-User-Data",
+    repo_type="dataset",
+    folder_path=DATASET_DIR,
+    path_in_repo="data",
+    token=HF_TOKEN
+)
+
+def save_feedback(root_cache_dir: str, feedback_type: str, comment: str = None) -> None:
+    """Save user feedback to Huggingface dataset"""
+    with scheduler.lock:
+        with DATASET_PATH.open("a") as f:
+            feedback_data = {
+                "query_id": os.path.basename(root_cache_dir),
+                "feedback_type": feedback_type,
+                "comment": comment,
+                "datetime": time.strftime("%Y%m%d_%H%M%S")
+            }
+            json.dump(feedback_data, f)
+            f.write("\n")
+########### End of Test Huggingface Dataset ###########
+
 class Solver:
     def __init__(
         self,

example.py

@@ -0,0 +1,47 @@
+import json
+from datetime import datetime
+from pathlib import Path
+from uuid import uuid4
+
+import gradio as gr
+
+from huggingface_hub import CommitScheduler
+
+
+JSON_DATASET_DIR = Path("json_dataset")
+JSON_DATASET_DIR.mkdir(parents=True, exist_ok=True)
+
+JSON_DATASET_PATH = JSON_DATASET_DIR / f"train-{uuid4()}.json"
+
+scheduler = CommitScheduler(
+    repo_id="example-space-to-dataset-json",
+    repo_type="dataset",
+    folder_path=JSON_DATASET_DIR,
+    path_in_repo="data",
+)
+
+
+def greet(name: str) -> str:
+    return "Hello " + name + "!"
+
+
+def save_json(name: str, greetings: str) -> None:
+    with scheduler.lock:
+        with JSON_DATASET_PATH.open("a") as f:
+            json.dump({"name": name, "greetings": greetings, "datetime": datetime.now().isoformat()}, f)
+            f.write("\n")
+
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        greet_name = gr.Textbox(label="Name")
+        greet_output = gr.Textbox(label="Greetings")
+    greet_btn = gr.Button("Greet")
+    greet_btn.click(fn=greet, inputs=greet_name, outputs=greet_output).success(
+        fn=save_json,
+        inputs=[greet_name, greet_output],
+        outputs=None,
+    )
+
+
+demo.launch()

feedback_dataset/feedback-20250217_212401.json

@@ -0,0 +1,10 @@
+{"query_id": "20250217_212439_f48ed6ff", "feedback_type": "upvote", "comment": null, "datetime": "20250217_212450"}
+{"query_id": "20250217_212439_f48ed6ff", "feedback_type": "upvote", "comment": null, "datetime": "20250217_212452"}
+{"query_id": "20250217_212439_f48ed6ff", "feedback_type": "It is good!", "comment": null, "datetime": "20250217_212459"}
+{"query_id": "20250217_212439_f48ed6ff", "feedback_type": "upvote", "comment": null, "datetime": "20250217_212523"}
+{"query_id": "20250217_212439_f48ed6ff", "feedback_type": "upvote", "comment": null, "datetime": "20250217_212524"}
+{"query_id": "20250217_212439_f48ed6ff", "feedback_type": "upvote", "comment": null, "datetime": "20250217_212524"}
+{"query_id": "20250217_212439_f48ed6ff", "feedback_type": "downvote", "comment": null, "datetime": "20250217_212524"}
+{"query_id": "20250217_212439_f48ed6ff", "feedback_type": "It is good!", "comment": null, "datetime": "20250217_212526"}
+{"query_id": "20250217_212439_f48ed6ff", "feedback_type": "upvote", "comment": null, "datetime": "20250217_212619"}
+{"query_id": "20250217_212439_f48ed6ff", "feedback_type": "It is good!", "comment": null, "datetime": "20250217_212650"}