Update app.py (#1)

- Update app.py (2ef87e7f2d4f3cc07fabc8e6ed293644140e8eeb)


Co-authored-by: Daffa Dians Ramadhan <Xaviant@users.noreply.huggingface.co>

app.py

@@ -4,103 +4,109 @@ import numpy as np
 from PIL import Image
 import io
 import sys
-import gradio as gr
-from tensorflow.keras.applications.resnet50 import preprocess_input
+from tensorflow.keras.applications.densenet import preprocess_input
 
-# =========================
-# 1. FastAPI Init
-# =========================
-app = FastAPI(title="Ashoka Buried Penis Classifier API")
+# 1. Inisialisasi Aplikasi
+app = FastAPI(title="Ashoka Hipospadia Classifier API")
 
-# =========================
 # 2. Load Model
-# =========================
-print("Loading model...")
+print("Sedang memuat model...")
 try:
-    model = tf.keras.models.load_model("cnn_kfold_best_model.h5")
-    print("Model loaded successfully")
+    model = tf.keras.models.load_model('cnn_kfold_best_model_v2.h5')
+    print("Model berhasil dimuat!")
 except Exception as e:
-    print("Failed to load model:", e)
-    sys.exit(1)
-
-class_names = ["Normal", "Buried"]
-
-# =========================
-# 3. Preprocessing
-# =========================
-def prepare_image(image: Image.Image):
-    image = image.convert("RGB")
-    image = image.resize((224, 224))
-    img_array = np.array(image)
-    img_array = np.expand_dims(img_array, axis=0)
-    img_array = preprocess_input(img_array)
-    return img_array
-
-# =========================
-# 4. Prediction Logic
-# =========================
-def predict_image(image):
-    if image is None:
-        return "No image uploaded", 0.0, 0.0
-
-    processed = prepare_image(image)
-    prediction = model.predict(processed)[0][0]
-
-    prob_buried = float(prediction * 100)
-    prob_normal = float((1 - prediction) * 100)
-
-    label = "Buried Penis" if prediction > 0.5 else "Normal"
-
-    return label, round(prob_normal, 2), round(prob_buried, 2)
-
-# =========================
-# 5. FastAPI Endpoint
-# =========================
+    print(f"Error memuat model: {e}")
+    sys.exit(1)  # Matikan server jika model gagal load
+
+# Label kelas: 0 = normal, 1 = buried
+class_names = ['normal', 'buried']
+
+# 3. Fungsi Preprocessing
+def prepare_image(image_bytes):
+    """
+    Preprocessing gambar untuk model DenseNet
+    - Konversi ke RGB (3 channel)
+    - Resize ke 224x224
+    - Preprocessing DenseNet
+    """
+    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
+
+# 4. Endpoint Prediksi
 @app.post("/predict")
-async def api_predict(file: UploadFile = File(...)):
-    image_bytes = await file.read()
-    image = Image.open(io.BytesIO(image_bytes))
-    label, normal, buried = predict_image(image)
-
-    return {
-        "class": label,
-        "probabilities": {
-            "normal": normal,
-            "buried": buried
+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))
+
+# 5. Endpoint Home
+@app.get("/")
+def home():
+    """Endpoint root untuk testing API"""
+    return {
+        "message": "Ashoka Hipospadia Classifier API Online! 🚀\n",
+        "model": "DenseNet Binary Classification\n",
+        "classes": class_names
     }
 
-# =========================
-# 6. Gradio UI
-# =========================
-with gr.Blocks() as demo:
-    gr.Markdown("""
-    # Ashoka Hipospadia Classifier API - ResNet50
-    **Medical screening tool for Buried Penis**
-
-    ⚠️ This tool is **NOT a diagnostic device**.  
-    Results must be interpreted by **qualified medical professionals**.
-    """)
-
-    with gr.Row():
-        image_input = gr.Image(
-            type="pil",
-            label="Upload / Drag & Drop Medical Image"
-        )
-
-    classify_btn = gr.Button("Analyze Image")
-
-    result_label = gr.Textbox(label="Prediction Result")
-    prob_normal = gr.Number(label="Normal Probability (%)")
-    prob_buried = gr.Number(label="Buried Probability (%)")
-
-    classify_btn.click(
-        fn=predict_image,
-        inputs=image_input,
-        outputs=[result_label, prob_normal, prob_buried]
-    )
-
-# =========================
-# 7. Mount Gradio to FastAPI
-# =========================
-app = gr.mount_gradio_app(app, demo, path="/")
+# API siap digunakan dengan uvicorn
+# Jalankan dengan: uvicorn app:app --host 0.0.0.0 --port 7860