Update app.py

app.py

@@ -49,7 +49,7 @@ def clean_text(text):
     return text
 
 # Membaca kamus kata gaul Salsabila
-kamus_path = '/content/drive/MyDrive/Skripsi/assets/_json_colloquial-indonesian-lexicon.txt'  # Ganti dengan path yang benar
+kamus_path = '_json_colloquial-indonesian-lexicon.txt'  # Ganti dengan path yang benar
 with open(kamus_path) as f:
     data = f.read()
 lookp_dict = json.loads(data)
@@ -153,7 +153,7 @@ def extract_tfidf_features(texts, tfidf_vectorizer):
     return tfidf_matrix
 
 # Memuat model TF-IDF dengan joblib (pastikan path-nya benar)
-tfidf_model_path = '/content/drive/MyDrive/Skripsi/output-4/Norm_model_smote/X_tfidf_model.joblib'
+tfidf_model_path = 'X_tfidf_model.joblib'
 tfidf_vectorizer = joblib.load(tfidf_model_path)
 
 # Fungsi untuk prediksi sentimen
@@ -175,7 +175,7 @@ def predict_sentiment(text, model, tfidf_vectorizer, slang_dict):
     return sentiment_label
 
 # Memuat model sentimen dengan joblib (pastikan path-nya benar)
-sentiment_model_path = '/content/drive/MyDrive/Skripsi/output-4/Norm_model_smote/ensemble_clf_soft_smote.joblib'
+sentiment_model_path = 'ensemble_clf_soft_smote.joblib'
 sentiment_model = joblib.load(sentiment_model_path)
 
 def get_emoticon(sentiment):