Delete streamlit_app.py

streamlit_app.py

@@ -1,67 +0,0 @@
-import streamlit as st
-import tensorflow as tf
-import numpy as np
-from PIL import Image
-from tensorflow.keras.applications.resnet50 import preprocess_input
-
-# =========================
-# LOAD MODEL
-# =========================
-@st.cache_resource
-def load_model():
-    return tf.keras.models.load_model("cnn_kfold_best_model.h5", compile=False)
-
-model = load_model()
-class_names = ["normal", "buried"]
-
-# =========================
-# PREPROCESSING
-# =========================
-def prepare_image(img):
-    img = img.convert("RGB")
-    img = img.resize((224, 224))
-    img_array = np.array(img)
-    img_array = np.expand_dims(img_array, axis=0)
-    img_array = preprocess_input(img_array)
-    return img_array
-
-# =========================
-# UI
-# =========================
-st.set_page_config(
-    page_title="Ashoka Hipospadia Classifier",
-    layout="centered"
-)
-
-st.title("🧠 Ashoka Hipospadia Classifier")
-st.write("Upload gambar untuk klasifikasi **Normal** atau **Buried**")
-
-uploaded_file = st.file_uploader(
-    "Upload gambar (JPG / PNG)",
-    type=["jpg", "jpeg", "png"]
-)
-
-if uploaded_file is not None:
-    image = Image.open(uploaded_file)
-
-    st.image(image, caption="Gambar Input", use_container_width=True)
-
-    if st.button("🔍 Predict"):
-        with st.spinner("Melakukan prediksi..."):
-            processed_image = prepare_image(image)
-            prediction = model.predict(processed_image)[0][0]
-
-            prob_normal = (1 - prediction) * 100
-            prob_buried = prediction * 100
-
-            predicted_class = "buried" if prediction > 0.5 else "normal"
-            confidence = max(prob_normal, prob_buried)
-
-        st.success(f"### 🏷️ Prediksi: **{predicted_class.upper()}**")
-        st.write(f"**Confidence:** {confidence:.2f}%")
-
-        st.progress(int(confidence))
-
-        st.subheader("📊 Probabilitas")
-        st.write(f"- Normal: **{prob_normal:.2f}%**")
-        st.write(f"- Buried: **{prob_buried:.2f}%**")