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import streamlit as st
import tensorflow as tf
import numpy as np
from PIL import Image
import cv2
from tensorflow.keras.applications.efficientnet import preprocess_input
# === Konfigurasi halaman ===
st.set_page_config(page_title="Garbage Classifier", page_icon="♻️", layout="centered")
st.markdown("<h1 style='text-align: center; color: white;'>♻️ Garbage Classifier - EfNetB2</h1>", unsafe_allow_html=True)
st.write("Upload gambar untuk mendapatkan prediksi jenis sampah.")
# === Load model ===
@st.cache_resource
def load_model():
base_model = tf.keras.applications.EfficientNetB2(
include_top=False,
weights="imagenet",
input_shape=(224, 224, 3)
)
x = tf.keras.layers.GlobalAveragePooling2D()(base_model.output)
x = tf.keras.layers.Dropout(0.3)(x)
output = tf.keras.layers.Dense(6, activation='softmax')(x)
model = tf.keras.models.Model(inputs=base_model.input, outputs=output)
model.compile(optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"])
model.load_weights("model/best_model_weights_trial1.keras")
return model
try:
model = load_model()
except Exception as e:
st.error(f"Gagal memuat model: {e}")
st.stop()
labels = ['cardboard', 'glass', 'metal', 'paper', 'plastic', 'trash']
# === Preprocessing untuk EfficientNet ===
def preprocess(frame):
img = cv2.resize(frame, (224, 224))
img = img.astype('float32')
img = preprocess_input(img)
return np.expand_dims(img, axis=0)
# === Fungsi prediksi dan anotasi ===
def predict_and_draw(frame):
input_tensor = preprocess(frame)
pred = model.predict(input_tensor, verbose=0)[0]
class_idx = np.argmax(pred)
label = labels[class_idx]
confidence = pred[class_idx]
h, w, _ = frame.shape
cv2.rectangle(frame, (10, 10), (w - 10, h - 10), (0, 255, 0), 3)
text = f"{label} ({confidence * 100:.2f}%)"
(text_w, text_h), _ = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, 0.8, 2)
cv2.rectangle(frame, (10, 10), (10 + text_w + 20, 10 + text_h + 20), (0, 0, 0), -1)
cv2.putText(frame, text, (20, 30 + text_h), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)
return frame
# === Upload Gambar ===
st.markdown("### 🖼️ Upload Gambar untuk Prediksi")
uploaded_file = st.file_uploader("Pilih gambar sampah", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
pil_img = Image.open(uploaded_file).convert("RGB")
frame = np.array(pil_img)
result = predict_and_draw(frame)
st.image(result, channels="BGR")
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