Update app.py

app.py

@@ -297,9 +297,10 @@ def create_pod(model_name: str, username: str, n=10, wait_seconds=10):
                 env={
                     "BENCHMARK": "nous",
                     "MODEL_ID": f"{username}/{model_name}",
-                    "REPO": "https://github.com/mlabonne/llm-autoeval.git",
+                    "REPO": "-b yall https://github.com/mlabonne/llm-autoeval.git",
                     "TRUST_REMOTE_CODE": False,
                     "PRIVATE_GIST": False,
+                    "YALL_GIST_ID": "84af749b1c0ef7336858df408f46f388",
                     "DEBUG": False,
                     "GITHUB_API_TOKEN": os.environ["GITHUB_TOKEN"],
                 }
@@ -401,7 +402,6 @@ def merge_loop():
 
     # Sample two models
     dir_path = "/data"
-    print(f"pwd: {os.getcwd()}")
     sample = df.sample(n=2)
     models = [sample.iloc[i] for i in range(2)]
 
@@ -417,33 +417,28 @@ def merge_loop():
     # Merge configs
     yaml_config = create_config(models)
     print(f"YAML config:{yaml_config}")
-    # print(f"Total size: {human_readable_size(get_size(dir_path))}")
+    print(f"Data size: {human_readable_size(get_size(dir_path))}")
 
     # Merge models
     merge_models()
     print("Model merged!")
-    # print(f"Total size: {human_readable_size(get_size(dir_path))}")
 
     # Create model card
     print("Create model card")
     create_model_card(yaml_config, model_name, USERNAME, license)
-    # print(f"Total size: {human_readable_size(get_size(dir_path))}")
 
     # Upload model
     print("Upload model")
     upload_model(api, USERNAME, model_name)
-    # print(f"Total size: {human_readable_size(get_size(dir_path))}")
 
     # Clear data
     print("Clear data")
     clear_data()
-    # print(f"Total size: {human_readable_size(get_size(dir_path))}")
     
     # Evaluate model on Runpod
     print("Start evaluation")
     create_pod(model_name, USERNAME)
-    # print(f"Total size: {human_readable_size(get_size(dir_path))}")
-
+    print(f"Waiting for {WAIT_TIME/60} minutes...")
 
 # Set the HF_DATASETS_CACHE environment variable
 os.environ['HF_DATASETS_CACHE'] = "/data/hfcache/"