Update init.py

init.py

@@ -4,8 +4,10 @@ from pathlib import Path
 from huggingface_hub import HfApi, Repository
 
 TOKEN_HUB = os.environ.get("TOKEN_HUB", None)
-QUEUE_REPO = os.environ.get("QUEUE_REPO", None)
-QUEUE_PATH = os.environ.get("QUEUE_PATH", None)
+QUEUE_REPO_EDACC = os.environ.get("QUEUE_REPO_EDACC", None)
+QUEUE_REPO_AFRI = os.environ.get("QUEUE_REPO_AFRI", None)
+QUEUE_PATH_EDACC = os.environ.get("QUEUE_PATH", None)
+QUEUE_PATH_AFRI = os.environ.get("QUEUE_PATH", None)
 
 hf_api = HfApi(
     endpoint="https://huggingface.co", 
@@ -22,26 +24,35 @@ def load_all_info_from_dataset_hub():
     else:
         print("Pulling evaluation requests and results.")
 
-        eval_queue_repo = Repository(
-            local_dir=QUEUE_PATH,
-            clone_from=QUEUE_REPO,
+        eval_queue_repo_edacc = Repository(
+            local_dir=QUEUE_PATH_EDACC,
+            clone_from=QUEUE_REPO_EDACC,
             use_auth_token=TOKEN_HUB,
             repo_type="dataset",
         )
-        eval_queue_repo.git_pull()
+        eval_queue_repo_edacc.git_pull()
+
+        eval_queue_repo_afrispeech = Repository(
+            local_dir=QUEUE_PATH_AFRI,
+            clone_from=QUEUE_REPO_AFRI,
+            use_auth_token=TOKEN_HUB,
+            repo_type="dataset",
+        )
+        eval_queue_repo_afrispeech.git_pull()
         
         # Local directory where dataset repo is cloned + folder with eval requests
-        directory = QUEUE_PATH / EVAL_REQUESTS_PATH
+        directory = QUEUE_PATH_EDACC / EVAL_REQUESTS_PATH
         requested_models = get_all_requested_models(directory)
         requested_models = [p.stem for p in requested_models]
         # Local directory where dataset repo is cloned
-        csv_results = get_csv_with_results(QUEUE_PATH)
-        if csv_results is None:
+        csv_results_edacc = get_csv_with_results(QUEUE_PATH_EDACC)
+        csv_results_afrispeech = get_csv_with_results(QUEUE_PATH_AFRI)
+        if csv_results_edacc is None or csv_results_afrispeech is None:
             passed = False
     if not passed:
         raise ValueError("No Hugging Face token provided. Skipping evaluation requests and results.")
 
-    return eval_queue_repo, requested_models, csv_results
+    return eval_queue_repo_edacc, requested_models, csv_results_edacc, csv_results_afrispeech
 
 
 def upload_file(requested_model_name, path_or_fileobj):