predict.py

import streamlit as st
import pandas as pd
import numpy as np
import tensorflow as tf
import pickle
import tensorflow_hub as hub


hub_layer = hub.KerasLayer("https://tfhub.dev/google/nnlm-id-dim128-with-normalization/2",
                           input_shape=[], dtype=tf.string,)
tf.keras.utils.get_custom_objects()['KerasLayer'] = hub_layer
nlp = tf.keras.models.load_model('sentiment_PPKM_NLP_Model.h5')

with open('formalisasi.txt', 'r') as file:
        function_text = file.read()
with open("proses text.txt",'r') as file1:
        proc = file1.read()
with open('kamus.txt','rb') as file2:
        kamus = pickle.load(file2)

def create_function_from_text(function_tex):
    # Gunakan pustaka exec() untuk mengevaluasi teks menjadi objek fungsi
    exec(function_tex, globals())
    # Ambil fungsi yang telah dibuat dan kembalikan sebagai output
    return list(globals().values())[0]

formalize_words = create_function_from_text(function_text)
text_process = create_function_from_text(proc)

def run():
    with st.form('key=sentiment prediction'):
        sentiment = st.text_input('ketikkan tweet anda:', '')

        submitted = st.form_submit_button('Predict')

    data_inf = {
        'sentiment': sentiment
    }
    data_inf = pd.DataFrame([data_inf])

    # Menerapkan fungsi klasifikasi sentimen menggunakan apply pada DataFrame
    data_inf['proc_sentiment'] = data_inf['sentiment'].apply(text_process)

    if submitted:
        y_pred_inf = [np.argmax(pred) for pred in nlp.predict(data_inf['proc_sentiment'])]
        st.write('sentiment : ', str(y_pred_inf))


if __name__ == '__main__':
    run()