Spaces:
Sleeping
Sleeping
| """ | |
| Graded Challenge | |
| Nama: Qothrunnadaa Alyaa | |
| Batch: HCK-009 | |
| File ini digunakan untuk menjalankan model prediksi rekomendasi user Sephora USA berdasarkan review yang diberikan | |
| """ | |
| import streamlit as st | |
| import pandas as pd | |
| import numpy as np | |
| import json | |
| import nltk | |
| import tensorflow as tf | |
| from tensorflow.keras.models import load_model | |
| from preprocessing import text_preprocessing | |
| from nltk.stem import WordNetLemmatizer | |
| nltk.download('punkt') | |
| nltk.download('wordnet') | |
| # Membuat function untuk dipanggil di app.py | |
| def run(): | |
| st.title('Sephora Products Recommendation Predictor') | |
| # Memasukkan review dari pelanggan/user | |
| review_text = st.text_input(label='Input your review here') | |
| # Menampilkan review yang dimasukkan user | |
| st.write(review_text) | |
| review_df = pd.DataFrame([review_text], columns=['review_text']) | |
| with open('stopwords.txt', 'r') as sw: | |
| stopwords = json.load(sw) | |
| stopwords = set(stopwords) | |
| lemmatizer = WordNetLemmatizer() | |
| review_df['review_text'] = review_df.apply(lambda row: text_preprocessing(row['review_text'], stopwords, lemmatizer), axis=1) | |
| # Memprediksi apakah user merekomendasikan produk yang dibeli berdasarkan review | |
| if st.button(label='Predict'): | |
| model = load_model('model_lstm_2') | |
| y_pred_inf = model.predict(review_df) | |
| if y_pred_inf <= 0.5: | |
| st.write('User did not recommend the product.') | |
| else: | |
| st.write('User recommend the product.') |