Update utils.py

utils.py

@@ -12,15 +12,12 @@ tqdm.pandas()
 api = HfApi()
 token = os.getenv("token")  # Das Token wird aus den Hugging Face Secrets abgerufen
 tokenread = os.getenv("tokenread")
+localdir = "SpotifyHitPrediction"
 
-# Überprüfen, ob das Token vorhanden ist
-if token is None:
-    raise ValueError("Hugging Face API-Token ist nicht gesetzt.")
+repo = Repository(local_dir=localdir, clone_from="https://huggingface.co/spaces/Add1E/SpotifyHitPrediction", token=token)
 
-# Klonen Sie das Repository (dies wird in Ihrem Space ausgeführt)
-repo = Repository(local_dir="SpotifyHitPrediction", clone_from="Add1E/SpotifyHitPrediction")
 
-def predict_popularity(features, trainset):
+def predict_popularity(features,trainset):
     predictions = [None] * 2
     predictions[0], predictions[1] = rf_model.predict([features]), model.predict([features])
     addToCsvAndTrain(trainset)
@@ -29,18 +26,20 @@ def predict_popularity(features, trainset):
 
 def addToCsvAndTrain(trainset):
     trainset = [
-        [trainset[0],trainset[1],trainset[2],trainset[3],trainset[4],trainset[5],trainset[6],trainset[7],
-         trainset[8],trainset[9],trainset[10],trainset[11],trainset[12],trainset[13]
+        [trainset[0], trainset[1], trainset[2], trainset[3], trainset[4], trainset[5], trainset[6], trainset[7],
+         trainset[8], trainset[9], trainset[10], trainset[11], trainset[12], trainset[13]
          ]
     ]
     neues_df = pd.DataFrame(trainset, columns= data.columns)
     df = pd.concat([data, neues_df], ignore_index=True)
-    df.to_csv('top50.csv', index=False)
-    repo.git_add('top50.csv')
+    df.to_csv(f'{localdir}/top50.csv', index=False)
+    repo.git_add(os.path.abspath(f'{localdir}/top50.csv'))
     repo.git_commit("Add top50.csv")
     repo.git_push()
 
 
+
+
 data = pd.read_csv('top50.csv', encoding='ISO-8859-1')
 print(data.head())