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"""
Graded Challenge 7
Nama: Devin Yaung Lee
Batch: HCK-009
// eda.py //
program ini menjadi base model EDA interface.
"""
import streamlit as st
import pandas as pd
import numpy as np
from tensorflow import keras
from keras.models import load_model
import os
import nltk
nltk.download('punkt')
# Import the text preprocessing and prediction functions if they are defined elsewhere
from file import text_preprocessing
# Define a function to make predictions
def make_prediction(model, texts, text_preprocessing):
# Apply custom text preprocessing
preprocessed_texts = [text_preprocessing(text) for text in texts]
# Predict using the loaded model
predictions = model.predict(preprocessed_texts)
return predictions
def run():
st.title("Predict the User Sentiment")
# Check if the model directory exists before loading the model
model = load_model('model_lstm')
# User input for review text
user_input = st.text_area("Enter your review:")
if st.button('Predict'):
# Preprocess the user input
preprocessed_text = text_preprocessing(user_input)
# Make predictions using the preprocessed data
predictions = make_prediction(model, [preprocessed_text], text_preprocessing)
# Convert prediction probabilities to class labels
predicted_class = np.argmax(predictions, axis=1)[0]
# Mapping index to class label
class_labels = {0: 'bad', 1: 'neutral', 2: 'good'}
predicted_label = class_labels[predicted_class]
# Display the prediction
if predicted_label == 'bad':
st.error("The model predicts the sentiment as bad.")
elif predicted_label == 'neutral':
st.warning("The model predicts the sentiment as neutral.")
else:
st.success("The model predicts the sentiment as good.") |