first commit
This commit is contained in:
Achillean-1
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README.md
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README.md
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### Setup Environtment
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python -m venv depression_detection_app
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### Powershell Administrator
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Set-ExecutionPolicy -Scope CurrentUser -ExecutionPolicy RemoteSigned
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### Environment Activation
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.\depression_detection_app\Scripts\activate
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### Install Requirements
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pip install -r requirements.txt
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### Start Program
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streamlit run main.py
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main.py
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main.py
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import streamlit as st
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def set_custom_css():
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st.markdown(
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"""
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<style>
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body {
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background-color: #f4f4f4;
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font-family: Arial, sans-serif;
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margin: 0; /* Menghilangkan margin default body */
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padding: 0; /* Menghilangkan padding default body */
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}
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.title {
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color: #f4f4f7;
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text-align: center;
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font-size: 2rem;
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font-weight: bold;
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margin-bottom: 10px;
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}
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.subtitle {
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text-align: center;
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font-size: 1.2rem;
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color: #f4f4f7;
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margin-bottom: 20px;
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}
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.stButton > button {
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background-color: #007bff;
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color: white;
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padding: 10px 24px;
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border-radius: 8px;
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font-size: 1rem;
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border: none;
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cursor: pointer;
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transition: 0.3s;
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display: block;
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margin: 0 auto;
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width: 50%;
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}
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.stButton > button:hover {
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background-color: #0056b3;
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box-shadow: 0 4px 8px rgba(0,0,0,0.1);
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}
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.center-container {
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display: flex;
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flex-direction: column;
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justify-content: center;
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align-items: center;
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}
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</style>
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""",
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unsafe_allow_html=True,
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)
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def main():
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st.set_page_config(page_title="Aplikasi Pendeteksi Depresi", layout="wide")
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set_custom_css()
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st.markdown('<div class="title">Selamat Datang di Aplikasi Pendeteksi Indikasi Depresi</div>', unsafe_allow_html=True)
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st.markdown('<div class="subtitle">Aplikasi ini menganalisis ekspresi wajah dan teks untuk mendeteksi potensi indikasi depresi.</div>', unsafe_allow_html=True)
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st.markdown('<div class="center-container">', unsafe_allow_html=True)
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if st.button("Mulai Analisis"):
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st.switch_page("pages/1_analisis_wajah.py")
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st.markdown('</div>', unsafe_allow_html=True)
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if __name__ == "__main__":
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main()
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BIN
model/Model_EfficientNet.tflite
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model/Model_EfficientNet.tflite
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model/Pemodelan_GRU_Valid.keras
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model/Pemodelan_GRU_Valid.keras
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model/tokenizer_Valid.pkl
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model/tokenizer_Valid.pkl
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pages/1_analisis_wajah.py
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pages/1_analisis_wajah.py
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#1_analisis_wajah.py
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import streamlit as st
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import cv2
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import numpy as np
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import time
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import matplotlib.pyplot as plt
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from collections import Counter, deque
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import tensorflow as tf
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from tensorflow.keras.applications.efficientnet import preprocess_input
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from contextlib import contextmanager
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import os
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import logging
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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CATEGORIES = ['Angry', 'Sad', 'Happy', 'Fearful', 'Disgust', 'Neutral', 'Surprised']
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POSITIVE_EMOTIONS = {'Happy', 'Surprised', 'Neutral'}
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NEGATIVE_EMOTIONS = {'Angry', 'Sad', 'Fearful', 'Disgust'}
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FRAME_INTERVAL = 0.5
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MODEL_PATH = "model/Model_EfficientNet.tflite"
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CAMERA_WIDTH = 320
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CAMERA_HEIGHT = 180
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MEDIA_WIDTH = 320
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MEDIA_HEIGHT = 180
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YOUTUBE_URL = "https://www.youtube.com/embed/3XA0bB79oGc?autoplay={}&mute=1"
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MAX_PREDICTIONS_BUFFER = 1000
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NEUTRAL_INDEX = CATEGORIES.index('Neutral')
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def load_custom_css():
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css = """
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<style>
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.main {
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background-color: #0b0f2e;
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color: white;
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}
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.stApp {
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background-color: #0b0f2e;
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}
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.metric-container {
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background-color: #1a2352;
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padding: 15px;
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border-radius: 8px;
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text-align: center;
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box-shadow: 0 2px 4px rgba(0,0,0,0.2);
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}
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.metric-label {
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font-size: 14px;
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opacity: 0.8;
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margin-bottom: 5px;
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}
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.metric-value {
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font-size: 20px;
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font-weight: bold;
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color: white;
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}
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.results-container {
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background-color: #111c4e;
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border-radius: 10px;
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padding: 20px;
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margin-top: 20px;
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}
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.emotion-score {
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font-size: 24px;
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font-weight: bold;
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text-align: center;
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margin: 10px 0;
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}
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.negative-score { color: #ff6b6b; }
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.positive-score { color: #69db7c; }
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.stButton > button {
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background-color: #007bff;
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color: white;
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border-radius: 8px;
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padding: 12px 24px;
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width: 100%;
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transition: background-color 0.3s ease;
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font-size: 16px;
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border: none;
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cursor: pointer;
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}
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.stButton > button:hover {
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background-color: #0056b3;
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}
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.status-indicator {
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padding: 8px 16px;
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border-radius: 20px;
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font-size: 14px;
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font-weight: 600;
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display: inline-block;
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margin: 15px 0;
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}
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.status-analyzing { background-color: #28a745; color: white; }
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.status-stopped { background-color: #dc3545; color: white; }
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.status-ready { background-color: #ffc107; color: black; }
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.youtube-container {
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background-color: #1a2352;
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border-radius: 8px;
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padding: 15px;
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box-shadow: 0 2px 4px rgba(0,0,0,0.2);
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margin-bottom: 20px;
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}
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.camera-container {
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background-color: #1a2352;
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border-radius: 8px;
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padding: 15px;
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box-shadow: 0 2px 4px rgba(0,0,0,0.2);
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margin-bottom: 20px;
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}
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.control-panel {
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background-color: #1a2352;
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border-radius: 8px;
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padding: 20px;
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box-shadow: 0 2px 4px rgba(0,0,0,0.2);
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}
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.panel-title {
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font-size: 18px;
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font-weight: bold;
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margin-bottom: 15px;
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color: white;
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}
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.main-title {
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font-size: 32px;
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font-weight: bold;
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margin-bottom: 15px;
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color: white;
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}
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.hidden {
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display: none;
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}
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.stImage > img {
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border-radius: 8px;
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}
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</style>
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"""
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st.markdown(css, unsafe_allow_html=True)
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class EmotionAnalyzer:
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def __init__(self):
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self.interpreter = None
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self.input_details = None
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self.output_details = None
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self.is_loaded = False
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@st.cache_resource
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def load_model(_self):
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if not os.path.exists(MODEL_PATH):
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raise FileNotFoundError(f"Model file {MODEL_PATH} tidak ditemukan")
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try:
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with st.spinner('Memuat model TFLite...'):
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start_time = time.time()
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interpreter = tf.lite.Interpreter(model_path=MODEL_PATH)
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interpreter.allocate_tensors()
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input_details = interpreter.get_input_details()
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output_details = interpreter.get_output_details()
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load_time = time.time() - start_time
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logger.info(f"Model loaded successfully in {load_time:.2f} seconds")
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return interpreter, input_details, output_details
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except Exception as e:
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logger.error(f"Error loading model: {e}")
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raise
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def initialize(self):
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if not self.is_loaded:
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self.interpreter, self.input_details, self.output_details = self.load_model()
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self.is_loaded = True
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def preprocess_image(self, image):
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try:
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image_resized = cv2.resize(image, (224, 224))
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image_array = np.expand_dims(image_resized, axis=0).astype(np.float32)
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image_array = preprocess_input(image_array)
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return image_array
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except Exception as e:
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logger.error(f"Error in preprocessing: {e}")
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return None
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def predict(self, image):
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if not self.is_loaded:
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raise RuntimeError("Model belum dimuat")
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try:
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processed_image = self.preprocess_image(image)
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if processed_image is None:
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return NEUTRAL_INDEX, 1.0
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self.interpreter.set_tensor(self.input_details[0]['index'], processed_image)
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self.interpreter.invoke()
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predictions = self.interpreter.get_tensor(self.output_details[0]['index'])[0]
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pred_idx = np.argmax(predictions)
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confidence = predictions[pred_idx]
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return pred_idx, confidence
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except Exception as e:
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logger.error(f"Error in prediction: {e}")
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return NEUTRAL_INDEX, 1.0
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class EmotionDataManager:
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def __init__(self):
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self.predictions = deque(maxlen=MAX_PREDICTIONS_BUFFER)
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self.timestamps = deque(maxlen=MAX_PREDICTIONS_BUFFER)
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self.start_time = None
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def reset(self):
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self.predictions.clear()
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self.timestamps.clear()
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self.start_time = None
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def add_prediction(self, pred_idx, timestamp=None):
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if self.start_time is None:
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self.start_time = time.time()
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if timestamp is None:
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timestamp = time.time() - self.start_time
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self.predictions.append(pred_idx)
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self.timestamps.append(timestamp)
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def get_emotion_scores(self):
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if not self.predictions:
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return 0, 0
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emotion_names = [CATEGORIES[idx] for idx in self.predictions]
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total = len(emotion_names)
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negative_count = sum(1 for emotion in emotion_names if emotion in NEGATIVE_EMOTIONS)
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positive_count = sum(1 for emotion in emotion_names if emotion in POSITIVE_EMOTIONS)
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negative_score = (negative_count / total) * 100
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positive_score = (positive_count / total) * 100
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return negative_score, positive_score
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def get_dominant_emotions(self, top_n=3):
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if not self.predictions:
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return []
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emotion_counts = Counter(self.predictions)
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most_common = emotion_counts.most_common(top_n)
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total = sum(emotion_counts.values())
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return [(CATEGORIES[emotion], count, (count/total)*100)
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for emotion, count in most_common]
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def get_emotion_changes(self, top_n=3):
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if len(self.predictions) < 2:
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return []
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changes = []
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for i in range(1, len(self.predictions)):
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if self.predictions[i] != self.predictions[i-1]:
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from_emotion = CATEGORIES[self.predictions[i-1]]
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to_emotion = CATEGORIES[self.predictions[i]]
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change_time = self.timestamps[i]
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changes.append((from_emotion, to_emotion, change_time))
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return sorted(changes, key=lambda x: x[2])[:top_n]
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@contextmanager
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def camera_context():
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cap = cv2.VideoCapture(0)
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cap.set(cv2.CAP_PROP_FRAME_WIDTH, CAMERA_WIDTH)
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cap.set(cv2.CAP_PROP_FRAME_HEIGHT, CAMERA_HEIGHT)
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cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)
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try:
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yield cap
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finally:
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cap.release()
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def create_metric_display(label, value, container_class="metric-container"):
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return f"""
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<div class="{container_class}">
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<div class="metric-label">{label}</div>
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<div class="metric-value">{value}</div>
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</div>
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"""
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def create_status_indicator(status):
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status_map = {
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'analyzing': ('Sedang Menganalisis', 'status-analyzing'),
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'stopped': ('Analisis Dihentikan', 'status-stopped'),
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'ready': ('Siap Memulai', 'status-ready')
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}
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text, css_class = status_map.get(status, ('Unknown', 'status-ready'))
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return f'<span class="status-indicator {css_class}">{text}</span>'
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def initialize_session_state():
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defaults = {
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'is_analyzing': False,
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'results_ready': False,
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'current_expression': "-",
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'current_accuracy': "-",
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'last_capture_time': 0,
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'analyzer': None,
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'data_manager': None,
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'video_started': False,
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'analysis_start_time': None
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}
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for key, value in defaults.items():
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if key not in st.session_state:
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st.session_state[key] = value
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if st.session_state.analyzer is None:
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st.session_state.analyzer = EmotionAnalyzer()
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if st.session_state.data_manager is None:
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st.session_state.data_manager = EmotionDataManager()
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def render_results():
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data_manager = st.session_state.data_manager
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if not data_manager.predictions:
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st.warning("Tidak ada data yang dikumpulkan selama analisis.")
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return
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st.success("✅ Analisis emosi wajah selesai!")
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negative_score, positive_score = data_manager.get_emotion_scores()
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col1, col2 = st.columns(2)
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with col1:
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st.markdown(create_metric_display(
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"Emosi Negatif",
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f"{negative_score:.1f}%",
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"metric-container negative-score"
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), unsafe_allow_html=True)
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with col2:
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st.markdown(create_metric_display(
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"Emosi Positif",
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f"{positive_score:.1f}%",
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"metric-container positive-score"
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), unsafe_allow_html=True)
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st.markdown("<div class='results-container'>", unsafe_allow_html=True)
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if len(data_manager.predictions) > 1:
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fig, ax = plt.subplots(figsize=(12, 6))
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fig.patch.set_facecolor('#f4f4f7')
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ax.set_facecolor('#f4f4f7')
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timestamps = list(data_manager.timestamps)
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expressions = [CATEGORIES[idx] for idx in data_manager.predictions]
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ax.scatter(timestamps, expressions, c='#3a7aff', alpha=0.7, s=30)
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ax.plot(timestamps, expressions, color='#3a7aff', alpha=0.5, linewidth=1)
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ax.set_xlabel("Waktu (detik)", color='#111c4e', fontsize=12)
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ax.set_ylabel("Ekspresi", color='#111c4e', fontsize=12)
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ax.set_title("Timeline Perubahan Ekspresi", color='#111c4e', fontsize=14, fontweight='bold')
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ax.tick_params(axis='both', colors='#111c4e', labelsize=10)
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for spine in ax.spines.values():
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spine.set_color('#111c4e')
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ax.grid(True, alpha=0.3)
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||||
plt.tight_layout()
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||||
st.pyplot(fig)
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||||
plt.close(fig)
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||||
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||||
col1, col2 = st.columns(2)
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||||
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||||
with col1:
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||||
st.subheader("Emosi Dominan")
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||||
dominant_emotions = data_manager.get_dominant_emotions()
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||||
for emotion, count, percentage in dominant_emotions:
|
||||
st.write(f"**{emotion}**: {percentage:.1f}% ({count} deteksi)")
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||||
|
||||
with col2:
|
||||
st.subheader("Perubahan Tercepat")
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||||
emotion_changes = data_manager.get_emotion_changes()
|
||||
if emotion_changes:
|
||||
for from_emotion, to_emotion, change_time in emotion_changes:
|
||||
st.write(f"**{from_emotion}** → **{to_emotion}** ({change_time:.1f}s)")
|
||||
else:
|
||||
st.info("Tidak ada perubahan emosi terdeteksi")
|
||||
|
||||
st.markdown("</div>", unsafe_allow_html=True)
|
||||
|
||||
def main():
|
||||
st.set_page_config(
|
||||
page_title="Analisis Emosi Wajah",
|
||||
layout="wide"
|
||||
)
|
||||
|
||||
initialize_session_state()
|
||||
load_custom_css()
|
||||
|
||||
st.markdown("<h1 class='main-title'>Analisis Emosi Wajah Real-time</h1>", unsafe_allow_html=True)
|
||||
|
||||
status = 'analyzing' if st.session_state.is_analyzing else ('ready' if not st.session_state.results_ready else 'stopped')
|
||||
st.markdown(create_status_indicator(status), unsafe_allow_html=True)
|
||||
|
||||
col1, col2 = st.columns([2, 1])
|
||||
|
||||
with col1:
|
||||
youtube_placeholder = st.empty()
|
||||
youtube_placeholder.markdown(
|
||||
f'<iframe width="100%" height="350" src="{YOUTUBE_URL.format(1 if st.session_state.video_started else 0)}" '
|
||||
f'frameborder="0" allowfullscreen style="border-radius: 10px;"></iframe>',
|
||||
unsafe_allow_html=True
|
||||
)
|
||||
st.markdown('</div>', unsafe_allow_html=True)
|
||||
|
||||
with col2:
|
||||
video_placeholder = st.empty()
|
||||
st.markdown('</div>', unsafe_allow_html=True)
|
||||
|
||||
st.subheader("📊 Metrik Real-time")
|
||||
|
||||
expression_placeholder = st.empty()
|
||||
accuracy_placeholder = st.empty()
|
||||
|
||||
st.subheader("Kontrol")
|
||||
button_text = 'Akhiri Analisis' if st.session_state.is_analyzing else 'Mulai Analisis'
|
||||
start_stop_button = st.button(button_text, key="main_control")
|
||||
|
||||
st.markdown('</div>', unsafe_allow_html=True)
|
||||
|
||||
expression_placeholder.markdown(
|
||||
create_metric_display("Ekspresi Terdeteksi", st.session_state.current_expression),
|
||||
unsafe_allow_html=True
|
||||
)
|
||||
accuracy_placeholder.markdown(
|
||||
create_metric_display("Tingkat Keyakinan", st.session_state.current_accuracy),
|
||||
unsafe_allow_html=True
|
||||
)
|
||||
|
||||
if start_stop_button:
|
||||
if not st.session_state.is_analyzing:
|
||||
try:
|
||||
st.session_state.analyzer.initialize()
|
||||
|
||||
st.session_state.data_manager.reset()
|
||||
st.session_state.is_analyzing = True
|
||||
st.session_state.results_ready = False
|
||||
st.session_state.last_capture_time = 0
|
||||
st.session_state.video_started = False
|
||||
st.session_state.analysis_start_time = time.time()
|
||||
|
||||
youtube_placeholder.markdown(
|
||||
f'<iframe width="100%" height="350" src="{YOUTUBE_URL.format(0)}" '
|
||||
f'frameborder="0" allowfullscreen style="border-radius: 10px;"></iframe>',
|
||||
unsafe_allow_html=True
|
||||
)
|
||||
|
||||
st.rerun()
|
||||
|
||||
except Exception as e:
|
||||
st.error(f"Error memulai analisis: {e}")
|
||||
st.session_state.is_analyzing = False
|
||||
else:
|
||||
st.session_state.is_analyzing = False
|
||||
st.session_state.results_ready = True
|
||||
st.session_state.current_expression = "-"
|
||||
st.session_state.current_accuracy = "-"
|
||||
st.session_state.video_started = False
|
||||
youtube_placeholder.markdown(
|
||||
f'<iframe width="100%" height="350" src="{YOUTUBE_URL.format(0)}" '
|
||||
f'frameborder="0" allowfullscreen style="border-radius: 10px;"></iframe>',
|
||||
unsafe_allow_html=True
|
||||
)
|
||||
video_placeholder.image(np.zeros((MEDIA_HEIGHT, MEDIA_WIDTH, 3), dtype=np.uint8), channels="RGB", width=MEDIA_WIDTH)
|
||||
st.rerun()
|
||||
|
||||
if st.session_state.is_analyzing:
|
||||
try:
|
||||
with camera_context() as cap:
|
||||
if not cap.isOpened():
|
||||
st.error("❌ Tidak dapat mengakses kamera")
|
||||
st.session_state.is_analyzing = False
|
||||
youtube_placeholder.markdown(
|
||||
f'<iframe width="100%" height="350" src="{YOUTUBE_URL.format(0)}" '
|
||||
f'frameborder="0" allowfullscreen style="border-radius: 10px;"></iframe>',
|
||||
unsafe_allow_html=True
|
||||
)
|
||||
st.rerun()
|
||||
|
||||
while st.session_state.is_analyzing:
|
||||
ret, frame = cap.read()
|
||||
if not ret:
|
||||
st.error("❌ Error membaca frame dari kamera")
|
||||
break
|
||||
|
||||
current_time = time.time()
|
||||
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
|
||||
|
||||
video_placeholder.image(frame_rgb, channels="RGB", width=MEDIA_WIDTH)
|
||||
|
||||
if not st.session_state.video_started and (current_time - st.session_state.analysis_start_time) >= 10:
|
||||
st.session_state.video_started = True
|
||||
youtube_placeholder.markdown(
|
||||
f'<iframe width="100%" height="350" src="{YOUTUBE_URL.format(1)}" '
|
||||
f'frameborder="0" allowfullscreen style="border-radius: 10px;"></iframe>',
|
||||
unsafe_allow_html=True
|
||||
)
|
||||
|
||||
if current_time - st.session_state.last_capture_time >= FRAME_INTERVAL:
|
||||
pred_idx, confidence = st.session_state.analyzer.predict(frame_rgb)
|
||||
|
||||
if pred_idx is not None:
|
||||
st.session_state.data_manager.add_prediction(pred_idx)
|
||||
|
||||
current_expression = CATEGORIES[pred_idx]
|
||||
current_accuracy = f"{confidence*100:.1f}%"
|
||||
|
||||
st.session_state.current_expression = current_expression
|
||||
st.session_state.current_accuracy = current_accuracy
|
||||
|
||||
expression_placeholder.markdown(
|
||||
create_metric_display("Ekspresi Terdeteksi", current_expression),
|
||||
unsafe_allow_html=True
|
||||
)
|
||||
accuracy_placeholder.markdown(
|
||||
create_metric_display("Tingkat Keyakinan", current_accuracy),
|
||||
unsafe_allow_html=True
|
||||
)
|
||||
|
||||
st.session_state.last_capture_time = current_time
|
||||
|
||||
time.sleep(0.03)
|
||||
|
||||
except Exception as e:
|
||||
st.error(f"Error selama analisis: {e}")
|
||||
st.session_state.is_analyzing = False
|
||||
youtube_placeholder.markdown(
|
||||
f'<iframe width="100%" height="350" src="{YOUTUBE_URL.format(0)}" '
|
||||
f'frameborder="0" allowfullscreen style="border-radius: 10px;"></iframe>',
|
||||
unsafe_allow_html=True
|
||||
)
|
||||
video_placeholder.image(np.zeros((MEDIA_HEIGHT, MEDIA_WIDTH, 3), dtype=np.uint8), channels="RGB", width=MEDIA_WIDTH)
|
||||
|
||||
if not st.session_state.is_analyzing:
|
||||
expression_placeholder.markdown(
|
||||
create_metric_display("Ekspresi Terdeteksi", "-"),
|
||||
unsafe_allow_html=True
|
||||
)
|
||||
accuracy_placeholder.markdown(
|
||||
create_metric_display("Tingkat Keyakinan", "-"),
|
||||
unsafe_allow_html=True
|
||||
)
|
||||
|
||||
if st.session_state.results_ready and not st.session_state.is_analyzing:
|
||||
render_results()
|
||||
|
||||
col1, col2 = st.columns(2)
|
||||
|
||||
with col1:
|
||||
if st.button("🔄 Reset Analisis", key="reset"):
|
||||
st.session_state.data_manager.reset()
|
||||
st.session_state.is_analyzing = False
|
||||
st.session_state.results_ready = False
|
||||
st.session_state.current_expression = "-"
|
||||
st.session_state.current_accuracy = "-"
|
||||
st.session_state.video_started = False
|
||||
youtube_placeholder.markdown(
|
||||
f'<iframe width="100%" height="350" src="{YOUTUBE_URL.format(0)}" '
|
||||
f'frameborder="0" allowfullscreen style="border-radius: 10px;"></iframe>',
|
||||
unsafe_allow_html=True
|
||||
)
|
||||
video_placeholder.image(np.zeros((MEDIA_HEIGHT, MEDIA_WIDTH, 3), dtype=np.uint8), channels="RGB", width=MEDIA_WIDTH)
|
||||
st.rerun()
|
||||
|
||||
with col2:
|
||||
if st.button("📝 Lanjut ke Jurnal", key="journal"):
|
||||
try:
|
||||
st.switch_page("pages/2_jurnaling.py")
|
||||
except Exception as e:
|
||||
st.error(f"Error: Halaman journaling tidak tersedia - {e}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
334
pages/2_jurnaling.py
Normal file
334
pages/2_jurnaling.py
Normal file
@ -0,0 +1,334 @@
|
||||
#2_jurnaling.py
|
||||
import streamlit as st
|
||||
import numpy as np
|
||||
import pickle
|
||||
import tensorflow as tf
|
||||
import matplotlib.pyplot as plt
|
||||
import pandas as pd
|
||||
import gdown
|
||||
import os
|
||||
import re
|
||||
from Sastrawi.Stemmer.StemmerFactory import StemmerFactory
|
||||
from nltk.corpus import stopwords
|
||||
|
||||
import nltk
|
||||
nltk.download('stopwords')
|
||||
|
||||
st.set_page_config(
|
||||
page_title="Analisis Journaling",
|
||||
layout="wide",
|
||||
initial_sidebar_state="collapsed"
|
||||
)
|
||||
|
||||
MODEL_URL = "https://drive.google.com/uc?id=1ArBDPUBcPMdsUzH_dKwdjVEK-DyXG9qz"
|
||||
TOKENIZER_URL = "https://drive.google.com/uc?id=1YVdwW-58y1Jie01MOjkd-4nY3bWFmt0E"
|
||||
MODEL_PATH = "model/Pemodelan_GRU_Valid.keras"
|
||||
TOKENIZER_PATH = "model/tokenizer_Valid.pkl"
|
||||
|
||||
os.makedirs("model", exist_ok=True)
|
||||
|
||||
@st.cache_resource
|
||||
def load_model():
|
||||
try:
|
||||
if not os.path.exists(MODEL_PATH):
|
||||
st.info("Mengunduh model dari Google Drive...")
|
||||
gdown.download(MODEL_URL, MODEL_PATH, quiet=False)
|
||||
return tf.keras.models.load_model(MODEL_PATH)
|
||||
except Exception as e:
|
||||
st.error(f"Gagal memuat model: {str(e)}")
|
||||
st.warning("Model tidak ditemukan. Menggunakan data demo.")
|
||||
return None
|
||||
|
||||
@st.cache_resource
|
||||
def load_tokenizer():
|
||||
try:
|
||||
if not os.path.exists(TOKENIZER_PATH):
|
||||
st.info("Mengunduh tokenizer dari Google Drive...")
|
||||
gdown.download(TOKENIZER_URL, TOKENIZER_PATH, quiet=False)
|
||||
with open(TOKENIZER_PATH, "rb") as handle:
|
||||
return pickle.load(handle)
|
||||
except Exception as e:
|
||||
st.error(f"Gagal memuat tokenizer: {str(e)}")
|
||||
st.warning("Tokenizer tidak ditemukan. Menggunakan data demo.")
|
||||
return None
|
||||
|
||||
model = load_model()
|
||||
tokenizer = load_tokenizer()
|
||||
|
||||
MAXLEN = 14
|
||||
|
||||
stop_words = set(stopwords.words('indonesian'))
|
||||
stemmer = StemmerFactory().create_stemmer()
|
||||
|
||||
st.markdown("""
|
||||
<style>
|
||||
.main-header {
|
||||
color: white;
|
||||
background-color: #1E1E5A;
|
||||
padding: 1.5rem;
|
||||
text-align: center;
|
||||
border-radius: 10px 10px 0 0;
|
||||
margin-bottom: 0;
|
||||
font-size: 2rem;
|
||||
font-weight: bold;
|
||||
}
|
||||
.sub-header {
|
||||
color: white;
|
||||
background-color: #1E1E5A;
|
||||
font-size: 1rem;
|
||||
padding: 0.7rem;
|
||||
text-align: center;
|
||||
border-radius: 0 0 10px 10px;
|
||||
margin-bottom: 2rem;
|
||||
letter-spacing: 1px;
|
||||
}
|
||||
.text-input-container {
|
||||
background-color: #f7f7f7;
|
||||
padding: 1rem;
|
||||
border-radius: 10px;
|
||||
margin-bottom: 1.5rem;
|
||||
box-shadow: 0 2px 5px rgba(0,0,0,0.1);
|
||||
}
|
||||
.stButton > button {
|
||||
background-color: #007bff;
|
||||
color: white;
|
||||
padding: 10px 24px;
|
||||
border-radius: 8px;
|
||||
font-size: 1rem;
|
||||
border: none;
|
||||
cursor: pointer;
|
||||
transition: 0.3s;
|
||||
display: block;
|
||||
margin: 0 auto;
|
||||
width: 100%;
|
||||
}
|
||||
.stButton > button:hover {
|
||||
background-color: #0056b3;
|
||||
box-shadow: 0 4px 8px rgba(0,0,0,0.1);
|
||||
}
|
||||
.result-container {
|
||||
background-color: white;
|
||||
border-radius: 10px;
|
||||
padding: 1.5rem;
|
||||
box-shadow: 0 3px 10px rgba(0,0,0,0.1);
|
||||
margin-top: 2rem;
|
||||
margin-bottom: 2rem;
|
||||
animation: fadeIn 0.5s;
|
||||
}
|
||||
.custom-button-container {
|
||||
margin-top: 20px;
|
||||
margin-bottom: 20px;
|
||||
}
|
||||
@keyframes fadeIn {
|
||||
0% { opacity: 0; }
|
||||
100% { opacity: 1; }
|
||||
}
|
||||
.emotion-label {
|
||||
font-weight: bold;
|
||||
font-size: 1.1rem;
|
||||
margin-bottom: 0.5rem;
|
||||
}
|
||||
.emotion-score {
|
||||
margin-left: 10px;
|
||||
color: #555;
|
||||
}
|
||||
.sentiment-label {
|
||||
font-weight: bold;
|
||||
font-size: 1.1rem;
|
||||
margin-top: 1rem;
|
||||
margin-bottom: 0.5rem;
|
||||
}
|
||||
.chart-title {
|
||||
text-align: center;
|
||||
font-weight: bold;
|
||||
margin-bottom: 1rem;
|
||||
color: #333;
|
||||
}
|
||||
.divider {
|
||||
margin-top: 1.5rem;
|
||||
margin-bottom: 1.5rem;
|
||||
border-top: 1px solid #eee;
|
||||
}
|
||||
</style>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
st.markdown('<div class="main-header">Analisis Jurnaling</div>', unsafe_allow_html=True)
|
||||
st.markdown('<div class="sub-header">TULISKAN EKSPRESIMU DENGAN KATA-KATA</div>', unsafe_allow_html=True)
|
||||
|
||||
text_input = st.text_area("", height=200, placeholder="Tuliskan isi jurnal anda di sini...")
|
||||
|
||||
col1, col2, col3 = st.columns([1, 2, 1])
|
||||
with col2:
|
||||
st.markdown('<div class="custom-button-container">', unsafe_allow_html=True)
|
||||
analyze_button = st.button("Analisis Teks", key="analyze", use_container_width=True)
|
||||
st.markdown('</div>', unsafe_allow_html=True)
|
||||
|
||||
def clean_text(text):
|
||||
text = text.lower()
|
||||
text = re.sub(r'http\S+', '', text)
|
||||
text = re.sub(r'[^a-zA-Z\s]', '', text)
|
||||
text = re.sub(r'\s+', ' ', text).strip()
|
||||
|
||||
words = text.split()
|
||||
words = [stemmer.stem(word) for word in words if word not in stop_words]
|
||||
return ' '.join(words)
|
||||
|
||||
def analyze_text(text):
|
||||
if model is None or tokenizer is None:
|
||||
return {
|
||||
"emotions": {
|
||||
"marah": 0.01,
|
||||
"sedih": 0.02,
|
||||
"jijik": 0.048,
|
||||
"takut": 0.01,
|
||||
"bahagia": 0.01,
|
||||
"netral": 0.945,
|
||||
"terkejut": 0.005
|
||||
},
|
||||
"dominant_emotion": "netral",
|
||||
"text": text
|
||||
}
|
||||
|
||||
clean_text_input = clean_text(text)
|
||||
|
||||
text_seq = tokenizer.texts_to_sequences([clean_text_input])
|
||||
|
||||
if not text_seq[0]:
|
||||
st.warning("Teks tidak mengandung kata yang dikenali oleh model. Coba gunakan kata-kata yang lebih umum.")
|
||||
return {
|
||||
"emotions": {label: 0.0 for label in ["marah", "sedih", "bahagia", "takut", "jijik", "netral", "terkejut"]},
|
||||
"dominant_emotion": "tidak_dikenali",
|
||||
"text": text
|
||||
}
|
||||
|
||||
text_padded = tf.keras.preprocessing.sequence.pad_sequences(text_seq, maxlen=MAXLEN, padding='post')
|
||||
|
||||
prediction = model.predict(text_padded, verbose=0)
|
||||
predicted_class = np.argmax(prediction, axis=1)[0]
|
||||
|
||||
label_mapping = {0: "marah", 1: "sedih", 2: "bahagia", 3: "takut", 4: "jijik", 5: "netral", 6: "terkejut"}
|
||||
emotion_label = label_mapping[predicted_class]
|
||||
|
||||
emotions = {}
|
||||
for i, label in label_mapping.items():
|
||||
emotions[label] = float(prediction[0][i])
|
||||
|
||||
return {
|
||||
"emotions": emotions,
|
||||
"dominant_emotion": emotion_label,
|
||||
"text": text
|
||||
}
|
||||
|
||||
if analyze_button:
|
||||
if text_input:
|
||||
result = analyze_text(text_input)
|
||||
st.session_state.text_analysis_result = result
|
||||
st.rerun()
|
||||
else:
|
||||
st.warning("Silakan masukkan teks terlebih dahulu.")
|
||||
|
||||
if 'text_analysis_result' in st.session_state:
|
||||
result = st.session_state.text_analysis_result
|
||||
|
||||
st.markdown("### Hasil:")
|
||||
st.markdown("#### Emosi Yang Terdeteksi:")
|
||||
|
||||
emotion_colors = {
|
||||
"marah": "#E53935",
|
||||
"sedih": "#7986CB",
|
||||
"jijik": "#8BC34A",
|
||||
"takut": "#FFB74D",
|
||||
"bahagia": "#4CAF50",
|
||||
"netral": "#9E9E9E",
|
||||
"terkejut": "#1E88E5"
|
||||
}
|
||||
|
||||
top_emotions = sorted(result["emotions"].items(), key=lambda x: x[1], reverse=True)[:3]
|
||||
|
||||
for emotion, score in top_emotions:
|
||||
emotion_name = emotion.capitalize()
|
||||
score_percent = score * 100
|
||||
color = emotion_colors.get(emotion, "#FFFFFF")
|
||||
|
||||
st.markdown(
|
||||
f'<div class="emotion-label" style="color:{color};">{emotion_name} <span class="emotion-score">{score_percent:.1f}%</span></div>',
|
||||
unsafe_allow_html=True
|
||||
)
|
||||
|
||||
st.markdown('<div class="chart-title">Top 3 Emosi</div>', unsafe_allow_html=True)
|
||||
|
||||
emotions = [e[0].capitalize() for e in top_emotions]
|
||||
scores = [e[1]*100 for e in top_emotions]
|
||||
|
||||
fig, ax = plt.subplots(figsize=(10, 4))
|
||||
|
||||
colors = [emotion_colors.get(emotion.lower(), "#1E88E5") for emotion in emotions]
|
||||
|
||||
bars = ax.barh(emotions, scores, color=colors, height=0.5)
|
||||
|
||||
for bar in bars:
|
||||
width = bar.get_width()
|
||||
ax.text(width + 1, bar.get_y() + bar.get_height()/2, f'{width:.1f}%',
|
||||
va='center', fontweight='bold')
|
||||
|
||||
ax.set_xlim(0, 100)
|
||||
ax.set_xlabel('Confidence (%)')
|
||||
ax.spines['top'].set_visible(False)
|
||||
ax.spines['right'].set_visible(False)
|
||||
ax.spines['bottom'].set_color('#DDDDDD')
|
||||
ax.spines['left'].set_color('#DDDDDD')
|
||||
ax.tick_params(bottom=False, left=False)
|
||||
ax.set_axisbelow(True)
|
||||
ax.grid(axis='x', linestyle='-', alpha=0.2)
|
||||
|
||||
st.pyplot(fig)
|
||||
|
||||
positive_emotions = ["bahagia", "netral", "terkejut"]
|
||||
negative_emotions = ["marah", "sedih", "jijik", "takut"]
|
||||
|
||||
positive_score = sum(result["emotions"][e] for e in positive_emotions) * 100
|
||||
negative_score = sum(result["emotions"][e] for e in negative_emotions) * 100
|
||||
|
||||
st.markdown('<div class="divider"></div>', unsafe_allow_html=True)
|
||||
|
||||
st.markdown(
|
||||
f'<div class="sentiment-label" style="color:#4CAF50;">Positive Sentiment <span class="emotion-score">{positive_score:.1f}%</span></div>',
|
||||
unsafe_allow_html=True
|
||||
)
|
||||
|
||||
st.markdown(
|
||||
f'<div class="sentiment-label" style="color:#E53935;">Negative Sentiment <span class="emotion-score">{negative_score:.1f}%</span></div>',
|
||||
unsafe_allow_html=True
|
||||
)
|
||||
|
||||
fig2, ax2 = plt.subplots(figsize=(10, 2))
|
||||
|
||||
sentiments = ["Positive", "Negative"]
|
||||
sentiment_scores = [positive_score, negative_score]
|
||||
sentiment_colors = ["#4CAF50", "#E53935"]
|
||||
|
||||
bars2 = ax2.barh(sentiments, sentiment_scores, color=sentiment_colors, height=0.5)
|
||||
|
||||
for bar in bars2:
|
||||
width = bar.get_width()
|
||||
ax2.text(width + 1, bar.get_y() + bar.get_height()/2, f'{width:.1f}%',
|
||||
va='center', fontweight='bold')
|
||||
|
||||
ax2.set_xlim(0, 100)
|
||||
ax2.set_xlabel('Sentiment Score (%)')
|
||||
ax2.spines['top'].set_visible(False)
|
||||
ax2.spines['right'].set_visible(False)
|
||||
ax2.spines['bottom'].set_color('#DDDDDD')
|
||||
ax2.spines['left'].set_color('#DDDDDD')
|
||||
ax2.tick_params(bottom=False, left=False)
|
||||
ax2.set_axisbelow(True)
|
||||
ax2.grid(axis='x', linestyle='-', alpha=0.2)
|
||||
|
||||
st.pyplot(fig2)
|
||||
|
||||
st.markdown('<div class="custom-button-container">', unsafe_allow_html=True)
|
||||
multimodal_button = st.button("Lihat Hasil Multimodal", key="multimodal", use_container_width=True)
|
||||
st.markdown('</div>', unsafe_allow_html=True)
|
||||
|
||||
if multimodal_button:
|
||||
st.switch_page("pages/3_hasil.py")
|
||||
340
pages/3_hasil.py
Normal file
340
pages/3_hasil.py
Normal file
@ -0,0 +1,340 @@
|
||||
#3_hasil.py
|
||||
import streamlit as st
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
from collections import Counter
|
||||
|
||||
CATEGORIES = ['Angry', 'Sad', 'Happy', 'Fearful', 'Disgust', 'Neutral', 'Surprised']
|
||||
|
||||
st.set_page_config(page_title="Analisis Multimodal", layout="wide")
|
||||
|
||||
st.markdown("""
|
||||
<style>
|
||||
.main-header {
|
||||
color: white;
|
||||
background-color: #1E1E5A;
|
||||
padding: 1.5rem;
|
||||
text-align: center;
|
||||
border-radius: 10px 10px 0 0;
|
||||
margin-bottom: 2rem;
|
||||
font-size: 2rem;
|
||||
font-weight: bold;
|
||||
}
|
||||
.section-header {
|
||||
color: #FFFFFF;
|
||||
font-weight: bold;
|
||||
margin-bottom: 20px;
|
||||
border-bottom: 2px solid #3498db;
|
||||
padding-bottom: 10px;
|
||||
}
|
||||
.positive-score {
|
||||
color: #2ecc71;
|
||||
font-weight: bold;
|
||||
}
|
||||
.negative-score {
|
||||
color: #e74c3c;
|
||||
font-weight: bold;
|
||||
}
|
||||
.stButton > button {
|
||||
background-color: #007bff;
|
||||
color: white;
|
||||
padding: 10px 24px;
|
||||
border-radius: 8px;
|
||||
font-size: 1rem;
|
||||
border: none;
|
||||
cursor: pointer;
|
||||
transition: 0.3s;
|
||||
display: block;
|
||||
margin: 0 auto;
|
||||
width: 100%;
|
||||
}
|
||||
.stButton > button:hover {
|
||||
background-color: #0056b3;
|
||||
box-shadow: 0 4px 8px rgba(0,0,0,0.1);
|
||||
}
|
||||
.custom-button-container {
|
||||
margin-top: 20px;
|
||||
margin-bottom: 20px;
|
||||
}
|
||||
.divider {
|
||||
margin-top: 1.5rem;
|
||||
margin-bottom: 1.5rem;
|
||||
border-top: 1px solid #eee;
|
||||
}
|
||||
</style>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
st.markdown('<div class="main-header">📊 Laporan Analisis Multimodal</div>', unsafe_allow_html=True)
|
||||
|
||||
emotion_mapping = {
|
||||
'Happy': 'bahagia',
|
||||
'Surprised': 'terkejut',
|
||||
'Neutral': 'netral',
|
||||
'Angry': 'marah',
|
||||
'Sad': 'sedih',
|
||||
'Fearful': 'takut',
|
||||
'Disgust': 'jijik',
|
||||
'bahagia': 'bahagia',
|
||||
'terkejut': 'terkejut',
|
||||
'netral': 'netral',
|
||||
'marah': 'marah',
|
||||
'sedih': 'sedih',
|
||||
'takut': 'takut',
|
||||
'jijik': 'jijik'
|
||||
}
|
||||
|
||||
positive_emotions = ['bahagia', 'terkejut', 'netral']
|
||||
negative_emotions = ['marah', 'sedih', 'takut', 'jijik']
|
||||
|
||||
if 'data_manager' not in st.session_state or st.session_state.data_manager is None:
|
||||
st.error("Data emosi wajah tidak tersedia. Silakan lakukan analisis wajah terlebih dahulu.")
|
||||
st.markdown('<div class="custom-button-container">', unsafe_allow_html=True)
|
||||
if st.button("Lakukan Analisis Wajah"):
|
||||
try:
|
||||
st.switch_page("pages/1_analisis_wajah.py")
|
||||
except FileNotFoundError:
|
||||
st.error("Halaman analisis wajah tidak ditemukan.")
|
||||
st.markdown('</div>', unsafe_allow_html=True)
|
||||
st.stop()
|
||||
|
||||
if 'text_analysis_result' not in st.session_state or not st.session_state.text_analysis_result:
|
||||
st.error("Data analisis teks tidak tersedia. Silakan lakukan analisis teks terlebih dahulu.")
|
||||
st.markdown('<div class="custom-button-container">', unsafe_allow_html=True)
|
||||
if st.button("Lakukan Analisis Teks"):
|
||||
try:
|
||||
st.switch_page("pages/2_analisis_teks.py")
|
||||
except FileNotFoundError:
|
||||
st.error("Halaman analisis teks tidak ditemukan.")
|
||||
st.markdown('</div>', unsafe_allow_html=True)
|
||||
st.stop()
|
||||
|
||||
data_manager = st.session_state.data_manager
|
||||
if not data_manager.predictions or not data_manager.timestamps:
|
||||
st.error("Tidak ada data emosi wajah yang valid untuk analisis.")
|
||||
st.stop()
|
||||
|
||||
face_emotion_data = [(timestamp, CATEGORIES[pred_idx]) for pred_idx, timestamp in zip(data_manager.predictions, data_manager.timestamps)]
|
||||
for timestamp, emotion in face_emotion_data:
|
||||
if not isinstance(timestamp, (int, float)) or not isinstance(emotion, str):
|
||||
st.error("Format data emosi wajah tidak valid.")
|
||||
st.stop()
|
||||
if emotion not in emotion_mapping:
|
||||
st.warning(f"Emosi tidak dikenal: {emotion}. Melewati emosi ini.")
|
||||
face_emotion_data = [(t, e) for t, e in face_emotion_data if e in emotion_mapping]
|
||||
if not face_emotion_data:
|
||||
st.error("Tidak ada data emosi wajah yang valid.")
|
||||
st.stop()
|
||||
|
||||
text_result = st.session_state.text_analysis_result
|
||||
if not isinstance(text_result, dict) or 'text' not in text_result:
|
||||
st.error("Format data analisis teks tidak valid.")
|
||||
st.stop()
|
||||
|
||||
# Process face emotions
|
||||
dominant_face_emotions = {}
|
||||
for _, emotion in face_emotion_data:
|
||||
standardized_emotion = emotion_mapping.get(emotion)
|
||||
if standardized_emotion:
|
||||
dominant_face_emotions[standardized_emotion] = dominant_face_emotions.get(standardized_emotion, 0) + 1
|
||||
|
||||
total_face_frames = len(face_emotion_data)
|
||||
if total_face_frames == 0:
|
||||
st.error("Tidak ada data emosi wajah yang valid untuk analisis.")
|
||||
st.stop()
|
||||
|
||||
face_emotion_percentages = {emotion: (count / total_face_frames) * 100
|
||||
for emotion, count in dominant_face_emotions.items()}
|
||||
top_face_emotions = sorted(face_emotion_percentages.items(), key=lambda x: x[1], reverse=True)[:3]
|
||||
|
||||
text_emotions = {}
|
||||
if "emotions" in text_result:
|
||||
text_emotions = {emotion_mapping.get(emotion, emotion): score * 100
|
||||
for emotion, score in text_result["emotions"].items()
|
||||
if emotion_mapping.get(emotion, emotion) in emotion_mapping.values()}
|
||||
elif "top_emotions" in text_result:
|
||||
text_emotions = {emotion_mapping.get(emotion, emotion): score * 100
|
||||
for emotion, score in text_result["top_emotions"]
|
||||
if emotion_mapping.get(emotion, emotion) in emotion_mapping.values()}
|
||||
|
||||
top_text_emotions = sorted(text_emotions.items(), key=lambda x: x[1], reverse=True)[:3]
|
||||
|
||||
combined_emotions = {}
|
||||
for emotion, percentage in face_emotion_percentages.items():
|
||||
combined_emotions[emotion] = combined_emotions.get(emotion, 0) + percentage
|
||||
|
||||
standardized_text_emotions = {emotion_mapping.get(e, e): score for e, score in text_emotions.items()}
|
||||
for emotion, percentage in standardized_text_emotions.items():
|
||||
if emotion in emotion_mapping.values():
|
||||
combined_emotions[emotion] = combined_emotions.get(emotion, 0) + percentage
|
||||
|
||||
for emotion in combined_emotions:
|
||||
appeared_in_face = emotion in face_emotion_percentages
|
||||
appeared_in_text = emotion in standardized_text_emotions
|
||||
divisor = 1 + (1 if appeared_in_face and appeared_in_text else 0)
|
||||
combined_emotions[emotion] /= divisor
|
||||
|
||||
total_combined = sum(combined_emotions.values())
|
||||
if total_combined > 0:
|
||||
combined_emotions = {emotion: (score / total_combined) * 100
|
||||
for emotion, score in combined_emotions.items()}
|
||||
top_combined_emotions = sorted(combined_emotions.items(), key=lambda x: x[1], reverse=True)[:3]
|
||||
|
||||
face_positive_score = sum(face_emotion_percentages.get(emotion, 0) for emotion in positive_emotions)
|
||||
face_negative_score = sum(face_emotion_percentages.get(emotion, 0) for emotion in negative_emotions)
|
||||
total_face_score = face_positive_score + face_negative_score
|
||||
if total_face_score > 0:
|
||||
face_positive_score = (face_positive_score / total_face_score) * 100
|
||||
face_negative_score = (face_negative_score / total_face_score) * 100
|
||||
else:
|
||||
face_positive_score = 0
|
||||
face_negative_score = 0
|
||||
st.warning("Tidak ada data emosi wajah yang valid untuk menghitung skor positif/negatif.")
|
||||
|
||||
if "positive_score" in text_result and "negative_score" in text_result:
|
||||
text_positive_score = text_result['positive_score'] * 100
|
||||
text_negative_score = text_result['negative_score'] * 100
|
||||
else:
|
||||
text_positive_score = sum(text_emotions.get(emotion, 0) for emotion in positive_emotions)
|
||||
text_negative_score = sum(text_emotions.get(emotion, 0) for emotion in negative_emotions)
|
||||
total_text_score = text_positive_score + text_negative_score
|
||||
if total_text_score > 0:
|
||||
text_positive_score = (text_positive_score / total_text_score) * 100
|
||||
text_negative_score = (text_negative_score / total_text_score) * 100
|
||||
else:
|
||||
text_positive_score = 0
|
||||
text_negative_score = 0
|
||||
st.warning("Tidak ada data emosi teks yang valid untuk menghitung skor positif/negatif.")
|
||||
|
||||
avg_positive_score = (face_positive_score + text_positive_score) / 2
|
||||
avg_negative_score = (face_negative_score + text_negative_score) / 2
|
||||
|
||||
emotion_changes = {}
|
||||
for i in range(1, len(face_emotion_data)):
|
||||
if face_emotion_data[i][1] != face_emotion_data[i-1][1]:
|
||||
key = f"{face_emotion_data[i-1][1]} → {face_emotion_data[i][1]}"
|
||||
time_diff = face_emotion_data[i][0] - face_emotion_data[i-1][0]
|
||||
emotion_changes[key] = time_diff
|
||||
|
||||
st.markdown('<h2 class="section-header">📝 Ringkasan Umum</h2>', unsafe_allow_html=True)
|
||||
st.write(f"**Durasi Analisis:** {face_emotion_data[-1][0]:.2f} detik")
|
||||
st.write(f"**Jumlah Perubahan Emosi:** {len(emotion_changes)}")
|
||||
st.markdown("### Skor Rata-rata Emosi (Wajah & Teks)")
|
||||
st.write(f"🟢 Rata-rata Emosi Positif: <span class='positive-score'>{avg_positive_score:.1f}%</span>", unsafe_allow_html=True)
|
||||
st.write(f"🔴 Rata-rata Emosi Negatif: <span class='negative-score'>{avg_negative_score:.1f}%</span>", unsafe_allow_html=True)
|
||||
|
||||
if avg_positive_score > 0 or avg_negative_score > 0:
|
||||
fig, ax = plt.subplots(figsize=(10, 3))
|
||||
labels = ['Positif', 'Negatif']
|
||||
values = [avg_positive_score, avg_negative_score]
|
||||
colors = ['#2ecc71', '#e74c3c']
|
||||
|
||||
bars = ax.barh(labels, values, color=colors, height=0.5)
|
||||
|
||||
for bar in bars:
|
||||
width = bar.get_width()
|
||||
ax.text(width + 1, bar.get_y() + bar.get_height()/2, f'{width:.1f}%',
|
||||
va='center', fontweight='bold')
|
||||
|
||||
ax.set_xlim(0, 100)
|
||||
ax.set_xlabel('Persentase (%)')
|
||||
ax.spines['top'].set_visible(False)
|
||||
ax.spines['right'].set_visible(False)
|
||||
ax.spines['bottom'].set_color('#DDDDDD')
|
||||
ax.spines['left'].set_color('#DDDDDD')
|
||||
ax.tick_params(bottom=False, left=False)
|
||||
ax.set_axisbelow(True)
|
||||
ax.grid(axis='x', linestyle='-', alpha=0.2)
|
||||
|
||||
st.pyplot(fig)
|
||||
else:
|
||||
st.warning("Tidak cukup data untuk menampilkan grafik skor positif/negatif.")
|
||||
|
||||
st.write("### Top 3 Emosi Gabungan (Wajah & Teks)")
|
||||
for emotion, score in top_combined_emotions:
|
||||
st.write(f"- {emotion.capitalize()}: {score:.1f}%")
|
||||
|
||||
st.markdown('</div>', unsafe_allow_html=True)
|
||||
st.markdown('<h2 class="section-header">😀 Analisis Ekspresi Wajah</h2>', unsafe_allow_html=True)
|
||||
st.write("### Top 3 Emosi Wajah")
|
||||
for emotion, percentage in top_face_emotions:
|
||||
count = dominant_face_emotions[emotion]
|
||||
st.write(f"- {emotion.capitalize()}: {percentage:.1f}% ({count} kali)")
|
||||
|
||||
st.write(f"### Skor Emosi Wajah")
|
||||
st.write(f"🟢 Skor Positif: <span class='positive-score'>{face_positive_score:.1f}%</span>", unsafe_allow_html=True)
|
||||
st.write(f"🔴 Skor Negatif: <span class='negative-score'>{face_negative_score:.1f}%</span>", unsafe_allow_html=True)
|
||||
|
||||
if len(face_emotion_data) > 1:
|
||||
fig, ax = plt.subplots(figsize=(12, 6))
|
||||
fig.patch.set_facecolor('#f4f4f7')
|
||||
ax.set_facecolor('#f4f4f7')
|
||||
|
||||
timestamps = [data[0] for data in face_emotion_data]
|
||||
emotions = [data[1] for data in face_emotion_data]
|
||||
|
||||
ax.scatter(timestamps, emotions, c='#3a7aff', alpha=0.7, s=30)
|
||||
ax.plot(timestamps, emotions, color='#3a7aff', alpha=0.5, linewidth=1)
|
||||
|
||||
ax.set_xlabel("Waktu (detik)", color='#111c4e', fontsize=12)
|
||||
ax.set_ylabel("Ekspresi", color='#111c4e', fontsize=12)
|
||||
ax.set_title("Timeline Perubahan Ekspresi", color='#111c4e', fontsize=14, fontweight='bold')
|
||||
|
||||
ax.tick_params(axis='both', colors='#111c4e', labelsize=10)
|
||||
for spine in ax.spines.values():
|
||||
spine.set_color('#111c4e')
|
||||
|
||||
ax.grid(True, alpha=0.3)
|
||||
|
||||
plt.tight_layout()
|
||||
|
||||
st.pyplot(fig)
|
||||
plt.close(fig)
|
||||
|
||||
if emotion_changes:
|
||||
st.write("### Perubahan Emosi Tercepat")
|
||||
for change, time in sorted(emotion_changes.items(), key=lambda x: x[1])[:3]:
|
||||
st.write(f"- {change} dalam {time:.2f} detik")
|
||||
|
||||
st.markdown('</div>', unsafe_allow_html=True)
|
||||
st.markdown('<h2 class="section-header">📝 Analisis Teks Jurnaling</h2>', unsafe_allow_html=True)
|
||||
st.write("### Top 3 Emosi Terdeteksi")
|
||||
for emotion, percentage in top_text_emotions:
|
||||
st.write(f"- {emotion.capitalize()}: {percentage:.1f}%")
|
||||
|
||||
st.write(f"### Skor Emosi")
|
||||
st.write(f"🟢 Skor Positif: <span class='positive-score'>{text_positive_score:.1f}%</span>", unsafe_allow_html=True)
|
||||
st.write(f"🔴 Skor Negatif: <span class='negative-score'>{text_negative_score:.1f}%</span>", unsafe_allow_html=True)
|
||||
st.write("### Teks Jurnal")
|
||||
st.write(text_result['text'])
|
||||
|
||||
st.markdown('</div>', unsafe_allow_html=True)
|
||||
st.markdown('<h2 class="section-header">🔍 Kesimpulan Akhir</h2>', unsafe_allow_html=True)
|
||||
st.markdown("### Fusion dengan Majority Voting")
|
||||
st.write("Metode ini menggabungkan keputusan dari setiap modalitas dengan menerapkan prinsip voting mayoritas.")
|
||||
|
||||
face_classification = 1 if face_negative_score >= face_positive_score else 0
|
||||
text_classification = 1 if text_negative_score >= text_positive_score else 0
|
||||
fused_score = np.mean([face_classification, text_classification])
|
||||
|
||||
if avg_negative_score >= 50:
|
||||
st.error("⚠️ Analisis menunjukkan potensi indikasi depresi.")
|
||||
st.write("Disarankan untuk berbicara dengan konselor atau psikolog.")
|
||||
elif avg_negative_score < 49:
|
||||
st.success("✅ Analisis menunjukkan kondisi emosi stabil.")
|
||||
st.write("Tetap jaga kesehatan mental dan lanjutkan kegiatan positif.")
|
||||
else:
|
||||
st.warning("⚖️ Analisis menunjukkan kondisi emosi netral (skor negatif 49%).")
|
||||
st.write("Pertimbangkan konsultasi jika merasa perlu.")
|
||||
|
||||
st.write(f"- Klasifikasi dari analisis wajah: {'Indikasi Negatif' if face_classification == 1 else 'Indikasi Positif'}")
|
||||
st.write(f"- Klasifikasi dari analisis teks: {'Indikasi Negatif' if text_classification == 1 else 'Indikasi Positif'}")
|
||||
|
||||
st.markdown('<div class="custom-button-container">', unsafe_allow_html=True)
|
||||
if st.button("Ulangi Analisis"):
|
||||
st.session_state.pop('data_manager', None)
|
||||
st.session_state.pop('text_analysis_result', None)
|
||||
try:
|
||||
st.switch_page("pages/1_analisis_wajah.py")
|
||||
except FileNotFoundError:
|
||||
st.error("Halaman analisis wajah tidak ditemukan.")
|
||||
st.markdown('</div>', unsafe_allow_html=True)
|
||||
10
requirements.txt
Normal file
10
requirements.txt
Normal file
@ -0,0 +1,10 @@
|
||||
opencv-contrib-python==4.9.0.80
|
||||
opencv-python==4.8.1.78
|
||||
pandas==2.1.4
|
||||
matplotlib==3.9.2
|
||||
numpy==1.26.2
|
||||
tensorflow==2.18.0
|
||||
streamlit==1.39.0
|
||||
gdown==5.2.0
|
||||
Sastrawi==1.0.1
|
||||
nltk==3.9.1
|
||||
Reference in New Issue
Block a user