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| import streamlit as st | |
| import numpy as np | |
| from PIL import Image | |
| import requests | |
| import os | |
| import tflite_runtime.interpreter as tflite | |
| # URL model dari Hugging Face | |
| cnn_model_url = "https://huggingface.co/diahretnou/insectsmodel/blob/main/cnn_model.tflite" | |
| mobilenet_model_url = "https://huggingface.co/diahretnou/insectsmodel/blob/main/convert_to_tflite.py" | |
| def load_model_from_url(url, filename): | |
| if not os.path.exists(filename): | |
| with st.spinner(f"Mengunduh model: {filename}..."): | |
| response = requests.get(url) | |
| with open(filename, 'wb') as f: | |
| f.write(response.content) | |
| return tf.keras.models.load_model(filename) | |
| # Load model dari URL | |
| cnn_model = load_model_from_url(cnn_model_url, "cnn_model.h5") | |
| mobilenet_model = load_model_from_url(mobilenet_model_url, "mobilenet_model.h5") | |
| # Label dan deskripsi | |
| class_names = ['Butterfly', 'Dragonfly', 'Grasshopper', 'Ladybird', 'Mosquito'] | |
| descriptions = { | |
| 'Grasshopper': "Grasshopper adalah serangga herbivora yang dikenal dengan kemampuan melompat jauh...", | |
| 'Butterfly': "Butterfly adalah serangga cantik dengan sayap berwarna-warni...", | |
| 'Dragonfly': "Dragonfly adalah serangga pemangsa yang hidup di dekat air...", | |
| 'Ladybird': "Ladybird, atau kepik, adalah serangga kecil berwarna cerah...", | |
| 'Mosquito': "Mosquito adalah serangga kecil yang dikenal sebagai penghisap darah..." | |
| } | |
| def preprocess_image(image): | |
| img = image.resize((150, 150)) | |
| img = np.array(img) / 255.0 | |
| return np.expand_dims(img, axis=0) | |
| # UI setup | |
| st.set_page_config(page_title="Insect Classifier", layout="wide") | |
| st.markdown("<h1 style='text-align: center;'>🦋 Insect Classifier</h1>", unsafe_allow_html=True) | |
| st.markdown("<h4 style='text-align: center; color: #666;'>Diah Retno Utami - 4TIB</h4>", unsafe_allow_html=True) | |
| st.markdown("---") | |
| # Layout 2 kolom | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| st.subheader("Upload Gambar") | |
| uploaded_file = st.file_uploader("Pilih gambar serangga", type=["jpg", "jpeg", "png"]) | |
| if uploaded_file is not None: | |
| image = Image.open(uploaded_file).convert("RGB") | |
| st.image(image, caption='Preview Gambar', use_column_width=True) | |
| with col2: | |
| st.subheader("Hasil Prediksi") | |
| if uploaded_file is not None: | |
| img = preprocess_image(image) | |
| # CNN | |
| cnn_pred = cnn_model.predict(img) | |
| cnn_index = int(np.argmax(cnn_pred[0])) | |
| cnn_label = class_names[cnn_index] | |
| cnn_conf = float(np.max(cnn_pred[0])) | |
| # MobileNet | |
| mobilenet_pred = mobilenet_model.predict(img) | |
| mobilenet_index = int(np.argmax(mobilenet_pred[0])) | |
| mobilenet_label = class_names[mobilenet_index] | |
| mobilenet_conf = float(np.max(mobilenet_pred[0])) | |
| st.markdown(f"**CNN Model:** {cnn_label} ({cnn_conf*100:.2f}%)") | |
| st.markdown(f"**MobileNetV2 Model:** {mobilenet_label} ({mobilenet_conf*100:.2f}%)") | |
| else: | |
| st.info("Silakan upload gambar terlebih dahulu.") | |
| # Deskripsi | |
| if uploaded_file is not None: | |
| st.markdown("---") | |
| st.subheader("📚 Deskripsi Serangga") | |
| if cnn_conf >= 0.5: | |
| st.write(descriptions.get(cnn_label, "Deskripsi tidak tersedia.")) | |
| else: | |
| st.write("Gambar tidak dapat dikenali dengan tingkat kepercayaan yang memadai.") | |