feat: add batch prediction functionality and warm-up inference to improve performance

app.py

@@ -1,8 +1,8 @@
 import gradio as gr
-from inference import predict
+from inference import predict, predict_batch
 
 # Tema dan tata letak diatur di sini
-with gr.Blocks(theme=gr.themes.Soft()) as iface:
+with gr.Blocks(theme=gr.themes.Soft(), css=None, analytics_enabled=False) as iface:
     gr.Markdown(
         """
         # 🤖 Deteksi Bentuk Wajah (Indonesia)
@@ -30,6 +30,13 @@ with gr.Blocks(theme=gr.themes.Soft()) as iface:
     # Hubungkan tombol "Prediksi" dengan fungsi predict yang sudah diimpor
     submit_btn.click(fn=predict, inputs=image_input, outputs=label_output)
 
+    # Endpoint batch (opsional) untuk klien JSON; menjaga compat endpoint default tetap single
+    with gr.Accordion("Batch (opsional)", open=False):
+        files_in = gr.Files(type="file", label="Unggah beberapa gambar")
+        json_out = gr.JSON(label="Best of batch (JSON)")
+        batch_btn = gr.Button("Prediksi Batch")
+        batch_btn.click(fn=predict_batch, inputs=files_in, outputs=json_out, api_name="predict_batch")
+
 # Luncurkan aplikasi jika file ini dijalankan secara langsung
 if __name__ == "__main__":
     iface.launch()

inference.py

@@ -1,7 +1,8 @@
 import numpy as np
 from PIL import Image
 import tensorflow as tf
-from typing import List, Dict
+from typing import List, Dict, Tuple
+import time
 import os
 
 # --- 1. Konfigurasi Model ---
@@ -49,6 +50,13 @@ class FaceShapeModel:
             self.model = rebuilt_model
             print("Model berhasil dibangun ulang dan bobot telah dimuat.")
 
+            # Warm-up: satu inference dummy agar jalur model siap (mengurangi cold-start)
+            try:
+                _ = self.model(tf.zeros((1, IMG_SIZE, IMG_SIZE, 3)))
+                print("Warm-up inference completed.")
+            except Exception as werr:
+                print(f"Warm-up failed (non-fatal): {werr}")
+
         except Exception as e:
             print(f"Error saat memuat model di inference.py: {e}")
             self.model = None
@@ -57,10 +65,16 @@ class FaceShapeModel:
         """Melakukan prediksi pada satu gambar."""
         if not self.model:
             return {"Error": "Model tidak dapat dimuat."}
-            
+        if image is None:
+            return {"Error": "Gambar tidak tersedia."}
+
+        t0 = time.perf_counter()
         processed_input = preprocess_image(image)
-        preds = self.model.predict(processed_input)[0]
-        confidences = {label: float(score) for label, score in zip(LABELS, preds)}
+        preds = self.model.predict(processed_input, verbose=0)[0]
+        confidences: Dict[str, float] = {label: float(score) for label, score in zip(LABELS, preds)}
+        # Logging ringan
+        dt = (time.perf_counter() - t0) * 1000
+        print(f"predict_image: took {dt:.1f} ms")
         return confidences
 
 # Buat instance dari model kita
@@ -72,3 +86,33 @@ def predict(image_pil: Image.Image) -> Dict[str, float]:
         return model_instance.predict_image(image_pil)
     return {"Error": "Instance model tidak tersedia."}
 
+
+# --- Batch prediction opsional ---
+def _dict_to_payload(d: Dict[str, float]) -> Dict[str, object]:
+    # Konversi dict label->score menjadi payload {label, confidences:[{label,confidence}]}
+    if not d or any(k == "Error" for k in d.keys()):
+        return {"label": "", "confidences": []}
+    items: List[Tuple[str, float]] = sorted(d.items(), key=lambda kv: kv[1], reverse=True)
+    top = items[0][0] if items else ""
+    confs = [{"label": k, "confidence": float(v)} for k, v in items]
+    return {"label": top, "confidences": confs}
+
+
+def predict_batch(images: List[Image.Image]) -> Dict[str, object]:
+    """Prediksi batch: pilih hasil terbaik, kembalikan payload seragam untuk klien JSON."""
+    if not model_instance:
+        return {"error": "Instance model tidak tersedia."}
+    if not images:
+        return {"error": "Tidak ada gambar."}
+
+    best_payload: Dict[str, object] = {"label": "", "confidences": []}
+    best_conf = -1.0
+    for img in images:
+        d = model_instance.predict_image(img)
+        payload = _dict_to_payload(d)
+        conf = float(payload.get("confidences", [{"confidence": 0.0}])[0]["confidence"]) if payload.get("confidences") else 0.0
+        if conf > best_conf:
+            best_conf = conf
+            best_payload = payload
+    return best_payload
+