Update app.py

app.py

@@ -1,40 +1,112 @@
-import streamlit as st
+from fastapi import FastAPI, File, UploadFile, HTTPException
 import tensorflow as tf
 import numpy as np
 from PIL import Image
+import io
+import sys
 from tensorflow.keras.applications.resnet50 import preprocess_input
 
-@st.cache_resource
-def load_model():
-    return tf.keras.models.load_model("cnn_kfold_best_model.h5", compile=False)
+# 1. Inisialisasi Aplikasi
+app = FastAPI(title="Ashoka Hipospadia Classifier API")
 
-model = load_model()
-class_names = ["normal", "buried"]
+# 2. Load Model
+print("Sedang memuat model...")
+try:
+    model = tf.keras.models.load_model('cnn_kfold_best_model.h5')
+    print("Model berhasil dimuat!")
+except Exception as e:
+    print(f"Error memuat model: {e}")
+    sys.exit(1)  # Matikan server jika model gagal load
 
-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
+# Label kelas: 0 = normal, 1 = buried
+class_names = ['normal', 'buried']
 
-st.set_page_config(page_title="Ashoka Classifier")
+# 3. Fungsi Preprocessing
+def prepare_image(image_bytes):
+    """
+    Preprocessing gambar untuk model ResNet50
+    - Konversi ke RGB (3 channel)
+    - Resize ke 224x224
+    - Preprocessing ResNet50
+    """
+    try:
+        img = Image.open(io.BytesIO(image_bytes))
+        
+        # Paksa ubah ke RGB agar PNG transparan tidak error
+        img = img.convert("RGB")
+        
+        # Resize ke ukuran input model (224x224 untuk ResNet50)
+        img = img.resize((224, 224))
+        
+        # Convert ke numpy array
+        img_array = np.array(img)
+        
+        # Tambah batch dimension
+        img_array = np.expand_dims(img_array, axis=0)
+        
+        # Preprocessing ResNet50 (HARUS sama dengan training!)
+        img_array = preprocess_input(img_array)
+        
+        return img_array
+    except Exception as e:
+        print(f"Error saat memproses gambar: {e}")
+        return None
 
-st.title("🧠 Ashoka Hipospadia Classifier")
-st.write("Upload gambar untuk klasifikasi")
+# 4. Endpoint Prediksi
+@app.post("/predict")
+async def predict(file: UploadFile = File(...)):
+    """
+    Endpoint untuk prediksi gambar
+    Input: File gambar (JPG, PNG, BMP)
+    Output: JSON dengan class dan confidence
+    """
+    try:
+        # Baca file gambar
+        image_bytes = await file.read()
+        
+        # Proses gambar
+        processed_image = prepare_image(image_bytes)
+        
+        if processed_image is None:
+            raise HTTPException(status_code=400, detail="File bukan gambar yang valid")
+        
+        # Prediksi
+        prediction = model.predict(processed_image)
+        pred_value = float(prediction[0][0])
+        
+        # Hitung probabilitas
+        # Model output: 0 = normal, 1 = buried
+        prob_normal = (1 - pred_value) * 100
+        prob_buried = pred_value * 100
+        
+        # Tentukan kelas berdasarkan threshold 0.5
+        top_class_idx = 1 if pred_value > 0.5 else 0
+        
+        # Hasil dalam format JSON
+        result = {
+            "class": class_names[top_class_idx],
+            "confidence": float(max(prob_normal, prob_buried)),
+            "probabilities": {
+                "normal": float(prob_normal),
+                "buried": float(prob_buried)
+            }
+        }
+        return result
+        
+    except Exception as e:
+        # Cetak error ke log
+        print(f"CRITICAL ERROR: {e}")
+        raise HTTPException(status_code=500, detail=str(e))
 
-uploaded_file = st.file_uploader("Upload Image", type=["jpg","png","jpeg"])
+# 5. Endpoint Home
+@app.get("/")
+def home():
+    """Endpoint root untuk testing API"""
+    return {
+        "message": "Ashoka Hipospadia Classifier API Online! 🚀"\n,
+        "model": "ResNet50 Binary Classification"\n,
+        "classes": class_names
+    }
 
-if uploaded_file:
-    image = Image.open(uploaded_file)
-    st.image(image, use_container_width=True)
-
-    if st.button("Predict"):
-        with st.spinner("Predicting..."):
-            pred = model.predict(prepare_image(image))[0][0]
-
-        if pred > 0.5:
-            st.success(f"Buried ({pred*100:.2f}%)")
-        else:
-            st.success(f"Normal ({(1-pred)*100:.2f}%)")
+# API siap digunakan dengan uvicorn
+# Jalankan dengan: uvicorn app:app --host 0.0.0.0 --port 7860