feat: dynamic local detecting within same repo

app.py

@@ -3,7 +3,6 @@ from gradio.components import Dataframe
 from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns
 import pandas as pd
 from apscheduler.schedulers.background import BackgroundScheduler
-from huggingface_hub import snapshot_download
 import os
 from gradio.themes import Soft
 
@@ -23,10 +22,9 @@ from src.display.utils import (
     EVAL_COLS,
     EVAL_TYPES,
     AutoEvalColumn,
-    auto_eval_column_attrs,
-    Language,
+    auto_eval_column_attrs
 )
-from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, QUEUE_REPO, REPO_ID, RESULTS_REPO, TOKEN, LOCAL_MODE
+from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, REPO_ID, LOCAL_MODE
 from src.populate import get_evaluation_queue_df, get_leaderboard_df
 from src.submission.submit import add_new_eval
 
@@ -50,31 +48,6 @@ def initialize_data_directories():
     os.makedirs(EVAL_REQUESTS_PATH, exist_ok=True)
     os.makedirs(EVAL_RESULTS_PATH, exist_ok=True)
     
-    if LOCAL_MODE:
-        print("Running in local mode, using local directories only")
-        return
-    
-    # Try to download from HF if not in local mode
-    try:
-        print(f"Downloading request data from {QUEUE_REPO} to {EVAL_REQUESTS_PATH}")
-        snapshot_download(
-            repo_id=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH, repo_type="dataset", 
-            tqdm_class=None, etag_timeout=30, token=TOKEN
-        )
-    except Exception as e:
-        print(f"Failed to download request data: {e}")
-        print("Using local data only")
-    
-    try:
-        print(f"Downloading result data from {RESULTS_REPO} to {EVAL_RESULTS_PATH}")
-        snapshot_download(
-            repo_id=RESULTS_REPO, local_dir=EVAL_RESULTS_PATH, repo_type="dataset", 
-            tqdm_class=None, etag_timeout=30, token=TOKEN
-        )
-    except Exception as e:
-        print(f"Failed to download result data: {e}")
-        print("Using local data only")
-
 # Initialize data
 initialize_data_directories()
 

src/envs.py

@@ -2,21 +2,30 @@ import os
 
 from huggingface_hub import HfApi
 
-# Run in local mode (no Hugging Face connection required)
-# Set to True when developing locally without HF credentials
-LOCAL_MODE = True
+# Dynamically determine if we're running in local mode
+def is_local_mode():
+    if os.environ.get("SPACE_AUTHOR_NAME") and os.environ.get("SPACE_REPO_NAME") and os.environ.get("HF_TOKEN") and os.environ.get("SPACE_ID"):
+        return False
+    return True
+
+LOCAL_MODE = is_local_mode()
 
 # Info to change for your repository
 # ----------------------------------
 # Get token from environment or use None in local mode
 TOKEN = os.environ.get("HF_TOKEN") if not LOCAL_MODE else None
 
-OWNER = "libvulnwatch" # Change to your org - don't forget to create a results and request dataset, with the correct format!
+OWNER = "holistic-ai" # Change to your org - don't forget to create a results and request dataset, with the correct format!
 # ----------------------------------
 
-REPO_ID = f"{OWNER}/leaderboard"
-QUEUE_REPO = f"{OWNER}/vulnerability-requests"
-RESULTS_REPO = f"{OWNER}/vulnerability-assessments"
+REPO_ID = f"{OWNER}/LibVulnWatch"
+QUEUE_REPO = REPO_ID  # Use the same repository
+RESULTS_REPO = REPO_ID  # Use the same repository
+
+if not LOCAL_MODE:
+  REPO_ID = str(os.environ.get("SPACE_ID"))
+  QUEUE_REPO = REPO_ID
+  RESULTS_REPO = REPO_ID
 
 # If you setup a cache later, just change HF_HOME
 CACHE_PATH=os.getenv("HF_HOME", ".")

src/submission/submit.py

@@ -65,9 +65,9 @@ def add_new_eval(
         path = Path(request_file_path)
         API.upload_file(
             path_or_fileobj=path,
-            path_in_repo=request_filename,
+            path_in_repo=f"assessment-queue/{request_filename}",
             repo_id=QUEUE_REPO,
-            repo_type="dataset",
+            repo_type="space",
         )
         
         return styled_message(f"Library '{library_name}' has been added to the assessment queue! Assessment ID: {uid}")