From a0b3cc98b311192882b684c70e59df2fc948625c Mon Sep 17 00:00:00 2001 From: ja'far shodiq Date: Fri, 11 Apr 2025 18:11:28 +0800 Subject: [PATCH] =?UTF-8?q?=F0=9F=90=9E=20fix:=20error=20model?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- dashboard/backend.py | 46 ++++++++++++++++++++++---------------------- 1 file changed, 23 insertions(+), 23 deletions(-) diff --git a/dashboard/backend.py b/dashboard/backend.py index d3623aa..9ccda08 100644 --- a/dashboard/backend.py +++ b/dashboard/backend.py @@ -109,28 +109,28 @@ def generate_wordclouds(wordcloud_data: pd.DataFrame, label_colors: dict) -> dic # Memuat Model dan Prediksi Sentimen # ====================================== -vectorizer = joblib.load('models/datasets-tfidf.pkl') +# vectorizer = joblib.load('models/datasets-tfidf.pkl') -def load_model_and_vectorizer(model_path, vectorizer_path): - """ - Memuat model dari file pickle. - """ - try: - model = joblib.load(model_path) - text_vectorizer = joblib.load(vectorizer_path) - return model, text_vectorizer - except Exception as e: - print(f"Error loading model or vectorizer: {e}") - return None, None +# def load_model_and_vectorizer(model_path, vectorizer_path): +# """ +# Memuat model dari file pickle. +# """ +# try: +# model = joblib.load(model_path) +# text_vectorizer = joblib.load(vectorizer_path) +# return model, text_vectorizer +# except Exception as e: +# print(f"Error loading model or vectorizer: {e}") +# return None, None -def predict_sentiment(model, text_vectorizer, text): - """ - Melakukan prediksi sentimen terhadap teks yang diberikan menggunakan model yang dipilih. - """ - try: - text_vectorized = text_vectorizer.transform([text]) - prediction = model.predict(text_vectorized) - return prediction[0] - except Exception as e: - print(f"Error predicting sentiment: {e}") - return None \ No newline at end of file +# def predict_sentiment(model, text_vectorizer, text): +# """ +# Melakukan prediksi sentimen terhadap teks yang diberikan menggunakan model yang dipilih. +# """ +# try: +# text_vectorized = text_vectorizer.transform([text]) +# prediction = model.predict(text_vectorized) +# return prediction[0] +# except Exception as e: +# print(f"Error predicting sentiment: {e}") +# return None \ No newline at end of file