Update app.py

app.py

@@ -1,98 +1,68 @@
-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("Real-time prediction using webcam or image upload.")
-
-# === 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
-
-# === Webcam Stream (gunakan tombol kontrol) ===
-st.markdown("## 📷 Webcam Stream (Opsional)")
-
-use_webcam = st.checkbox("Aktifkan Webcam Stream")
-start_cam = st.button("Mulai Prediksi") if use_webcam else False
-stop_cam = st.button("Stop")
-
-if use_webcam and start_cam and not stop_cam:
-    stframe = st.empty()
-    cap = cv2.VideoCapture(0)
-
-    while cap.isOpened():
-        ret, frame = cap.read()
-        if not ret:
-            st.warning("❌ Tidak bisa mengakses kamera.")
-            break
-
-        frame = cv2.flip(frame, 1)
-        result = predict_and_draw(frame)
-        stframe.image(result, channels="BGR")
-
-        if stop_cam:
-            break
-
-    cap.release()
-    cv2.destroyAllWindows()
-
-# === Upload Gambar Alternatif ===
-st.markdown("---")
-st.markdown("### 🖼️ Atau Upload Gambar:")
-
-uploaded_file = st.file_uploader("Upload file gambar", 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")
+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")