Update app.py

app.py

@@ -1,4 +1,5 @@
 import os
+import zipfile
 import numpy as np
 import tensorflow as tf
 from fastapi import FastAPI
@@ -25,15 +26,50 @@ class ItemRequest(BaseModel):
     items_text: str
 
 # =========================================================
-# LOAD MODEL SECARA NORMAL
+# JURUS PAMUNGKAS: MANUAL BYPASS + ZIP EXTRACT
 # =========================================================
+def build_manual_clf_model():
+    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)
+    
+    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
     if clf_model is None:
-        print("Memuat model klasifikasi secara normal...")
-        # Load langsung file .keras bawaan Anda
-        clf_model = tf.keras.models.load_model("model_strukly.keras", compile=False)
-        print("Model berhasil dimuat!")
+        print("Membangun arsitektur manual dan memuat bobot...")
+        # 1. Bangun arsitektur manual untuk menghindari quantization_config
+        clf_model = build_manual_clf_model()
+        # 2. Hanya muat bobot angka
+        clf_model.load_weights("model_strukly.keras")
+        
+        # 3. Suntikkan kamus TextVectorization langsung dari dalam file .keras
+        print("Mengekstrak kamus TextVectorization...")
+        tv_layer = clf_model.get_layer('text_vectorization')
+        try:
+            with zipfile.ZipFile("model_strukly.keras", 'r') as z:
+                # Cari file txt di folder assets
+                vocab_files = [n for n in z.namelist() if 'assets/' in n and n.endswith('.txt')]
+                if vocab_files:
+                    with z.open(vocab_files[0]) as f:
+                        vocab_list = f.read().decode('utf-8').splitlines()
+                        tv_layer.set_vocabulary(vocab_list)
+                        print(f"Kamus berhasil disuntikkan dari: {vocab_files[0]}")
+        except Exception as e:
+            print(f"Gagal menyuntikkan kamus: {e}")
+            
     return clf_model
 
 @app.post("/classify/")
@@ -41,7 +77,7 @@ def classify_items(request: ItemRequest):
     try:
         raw_items = request.items_text
         
-        # Jika Space A mengirim string kosong (tidak ada item terdeteksi)
+        # Jika Space A mengirim string kosong
         if not raw_items.strip():
              return {"success": True, "class_id": None}
              
@@ -58,5 +94,5 @@ def classify_items(request: ItemRequest):
         
     except Exception as e:
         import traceback
-        print(f"Error AI Klasifikasi: {traceback.format_exc()}")
+        print(f"Error AI Klasifikasi detail: {traceback.format_exc()}")
         return {"success": False, "error": str(e), "class_id": None}