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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"
@st.cache_resource
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.")
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