Update app.py

app.py

@@ -4,109 +4,103 @@ import numpy as np
 from PIL import Image
 import io
 import sys
+import gradio as gr
 from tensorflow.keras.applications.resnet50 import preprocess_input
 
-# 1. Inisialisasi Aplikasi
-app = FastAPI(title="Ashoka Hipospadia Classifier API")
+# =========================
+# 1. FastAPI Init
+# =========================
+app = FastAPI(title="Ashoka Buried Penis Classifier API")
 
+# =========================
 # 2. Load Model
-print("Sedang memuat model...")
+# =========================
+print("Loading model...")
 try:
-    model = tf.keras.models.load_model('cnn_kfold_best_model.h5')
-    print("Model berhasil dimuat!")
+    model = tf.keras.models.load_model("cnn_kfold_best_model.h5")
+    print("Model loaded successfully")
 except Exception as e:
-    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 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
-
-# 4. Endpoint Prediksi
+    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
+# =========================
 @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))
-
-# 5. Endpoint Home
-@app.get("/")
-def home():
-    """Endpoint root untuk testing API"""
+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 {
-        "message": "Ashoka Hipospadia Classifier API Online! 🚀",
-        "model": "ResNet50 Binary Classification",
-        "classes": class_names
+        "class": label,
+        "probabilities": {
+            "normal": normal,
+            "buried": buried
+        }
     }
 
-# API siap digunakan dengan uvicorn
-# Jalankan dengan: uvicorn app:app --host 0.0.0.0 --port 7860
+# =========================
+# 6. Gradio UI
+# =========================
+with gr.Blocks() as demo:
+    gr.Markdown("""
+    # 🩺 Pediatric Buried Penis Classification (AI)
+    **Medical screening tool (Research & Educational Purpose Only)**
+
+    ⚠️ 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="/")