app

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+.sesskey

.gitignore

@@ -0,0 +1 @@
+.sesskey

app.py

@@ -1,14 +1,14 @@
 import json
+import os
 import random
 from collections import Counter
 
-import dotenv
-from datasets import load_dataset
+from datasets import Dataset, load_dataset
 from fasthtml.common import *
-from fasthtml_hf import setup_hf_backup
 from fastlite import database
+from huggingface_hub import create_repo, login
 
-dotenv.load_dotenv()
+login(token=os.environ.get("HF_TOKEN"))
 
 fact_dataset = load_dataset("griffin/iclr2025_data_scores", split="train").to_list()
 fact_dataset = [{"example_id": i, **example} for i, example in enumerate(fact_dataset)]
@@ -121,6 +121,22 @@ def render_example(example):
     )
 
 
+def upload_to_hf():
+    create_repo(
+        repo_id="rbiswasfc/iclr-eval-examples",
+        token=os.environ.get("HF_TOKEN"),
+        private=True,
+        repo_type="dataset",
+        exist_ok=True,
+    )
+
+    examples = db.t.examples
+    annotations = examples()
+
+    hf_ds = Dataset.from_list(annotations)
+    hf_ds.push_to_hub("rbiswasfc/iclr-eval-examples", token=os.environ.get("HF_TOKEN"))
+
+
 @rt("/")
 async def get(question_type: str = None):
     stats = get_stats()
@@ -207,6 +223,7 @@ async def post(decision: str, example: str):
             "decision": decision,
         }
     )
+    upload_to_hf()
 
     # Add the evaluated example's ID to the set of evaluated IDs
     evaluated_ids.add(example_dict["example_id"])
@@ -225,5 +242,5 @@ if __name__ == "__main__":
 
     import uvicorn
 
-    setup_hf_backup(app)
+    # setup_hf_backup(app)
     uvicorn.run(app, host="0.0.0.0", port=int(os.environ.get("PORT", 7860)))