Update app.py

app.py

@@ -38,6 +38,9 @@ clf_encoder = None
 class TextRequest(BaseModel):
     text: str
 
+# =========================================================
+# LAZY LOAD NER MODEL (Manual Architecture Bypass)
+# =========================================================
 def build_manual_model(vocab_size=7929, max_len=64, num_classes=8):
     inputs = tf.keras.Input(shape=(max_len,), dtype='int32', name='tokens')
     x = tf.keras.layers.Embedding(input_dim=vocab_size, output_dim=64, mask_zero=True, name='embedding')(inputs)
@@ -70,12 +73,39 @@ def get_ner_model():
         ner_model.load_weights("receipt_ner_model.keras")
     return ner_model, vocab
 
+# =========================================================
+# LAZY LOAD CLASSIFICATION MODEL (Manual Architecture Bypass)
+# =========================================================
+def build_manual_clf_model():
+    # Meniru arsitektur dari log error Anda
+    inputs = tf.keras.Input(shape=(1,), dtype=tf.string, name='input_layer')
+    
+    x = tf.keras.layers.TextVectorization(
+        max_tokens=5000, 
+        output_sequence_length=50, 
+        name='text_vectorization'
+    )(inputs)
+    
+    x = tf.keras.layers.Embedding(input_dim=5000, output_dim=64, name='embedding')(x)
+    x = tf.keras.layers.GlobalAveragePooling1D(name='global_average_pooling1d')(x)
+    x = tf.keras.layers.Dense(64, activation='relu', name='dense')(x)
+    x = tf.keras.layers.Dropout(0.2, name='dropout')(x)
+    
+    # Output 7 classes
+    outputs = tf.keras.layers.Dense(7, activation='softmax', name='dense_1')(x)
+    
+    return tf.keras.Model(inputs=inputs, outputs=outputs, name='functional')
+
 def get_clf_model():
     global clf_model, clf_encoder
     if clf_model is None:
         with open('penerjemah_kategori.pkl', 'rb') as file:
             clf_encoder = pickle.load(file)
-        clf_model = tf.keras.models.load_model('model_strukly.keras', compile=False)
+            
+        # Bypass JSON yang rusak, langsung load bobotnya!
+        clf_model = build_manual_clf_model()
+        clf_model.load_weights('model_strukly.keras')
+        
     return clf_model, clf_encoder
 
 def normalize_token(token: str):
@@ -95,7 +125,7 @@ def predict_ner(request: TextRequest):
         if not raw_text.strip():
             return {"success": True, "entities": entities, "classes": kategori_struk}
 
-        # --- PROSES NER ---
+        # --- 1. PROSES NER ---
         model, vcb = get_ner_model()
         tokens = [t for line in raw_text.split("\n") for t in line.strip().split() if t]
         
@@ -125,7 +155,7 @@ def predict_ner(request: TextRequest):
                     span = []
             if current_type: entities[current_type].append(" ".join(span))
 
-        # --- PROSES KLASIFIKASI ---
+        # --- 2. PROSES KLASIFIKASI ---
         items_list = entities.get("ITEM", [])
         teks_untuk_klasifikasi = " ".join(items_list)
         
@@ -138,12 +168,12 @@ def predict_ner(request: TextRequest):
                 indeks_tertinggi = np.argmax(hasil_prediksi[0])
                 kategori_struk = c_encoder.inverse_transform([indeks_tertinggi])[0]
             except Exception as e:
-                print(f"Error Klasifikasi: {e}")
+                print(f"Error Klasifikasi internal: {e}")
 
         return {
             "success": True, 
             "entities": entities, 
-            "classes": kategori_struk 
+            "classes": kategori_struk
         }
         
     except Exception as e: