Update src/streamlit_app.py

src/streamlit_app.py

@@ -3,27 +3,59 @@ import numpy as np
 from PIL import Image
 import tensorflow as tf
 import os
+from tensorflow.keras import layers, models
+from tensorflow.keras.applications import EfficientNetB3
 
 # -----------------------------------
 # CONFIG
 # -----------------------------------
 IMG_SIZE = 300
 CLASS_NAMES = ['cup', 'fork', 'glass', 'knife', 'plate', 'spoon']
+NUM_CLASSES = len(CLASS_NAMES)
 
 # -----------------------------------
-# MODEL LOADING (KERAS 3)
+# MODEL DEFINITION
+# -----------------------------------
+def create_model():
+    base_model = EfficientNetB3(
+        weights=None, 
+        include_top=False, 
+        input_shape=(IMG_SIZE, IMG_SIZE, 3)
+    )
+    
+    base_model.trainable = True 
+    
+    model = models.Sequential([
+        base_model,
+        layers.GlobalAveragePooling2D(),
+        layers.BatchNormalization(),
+        layers.Dropout(0.3),
+        layers.Dense(256, activation='relu'),
+        layers.Dropout(0.2),
+        layers.Dense(NUM_CLASSES, activation='softmax') 
+    ])
+    
+    return model
+
+# -----------------------------------
+# MODEL LOADING (WEIGHTS ONLY)
 # -----------------------------------
 @st.cache_resource
-def load_model_file():
-    # Uzantıyı .h5 olarak değiştirdik
-    model_path = os.path.join(os.path.dirname(__file__), "kitchen.h5")
+def load_model_from_weights():
+    model = create_model()
+    
+    weights_path = os.path.join(os.path.dirname(__file__), "kitchen_weights.weights.h5")
+    
     try:
-        return tf.keras.models.load_model(model_path, compile=False)
+        model.build((None, IMG_SIZE, IMG_SIZE, 3))
+        
+        model.load_weights(weights_path)
+        return model
     except Exception as e:
-        st.error(f"Model yüklenemedi: {e}")
+        st.error(f"Ağırlıklar yüklenemedi: {e}")
         return None
-        
-model = load_model_file()
+
+model = load_model_from_weights()
 
 # -----------------------------------
 # IMAGE PREPROCESS
@@ -32,18 +64,12 @@ def process_image(img):
     img = img.resize((IMG_SIZE, IMG_SIZE))
     img = np.array(img)
     
-    # Boyut kontrolleri
     if img.ndim == 2:
         img = np.stack([img]*3, axis=-1)
     elif img.shape[-1] == 4:
         img = img[..., :3]
     
-    # Batch boyutu ekle: (1, 300, 300, 3)
     img = np.expand_dims(img, 0)
-    
-    # EfficientNet B3 Preprocess
-    # Keras 3'te bu fonksiyon bazen farklı path'te olabilir,
-    # ama tf.keras.applications genelde güvenlidir.
     img = tf.keras.applications.efficientnet.preprocess_input(img)
     return img
 
@@ -58,7 +84,6 @@ uploaded = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
 if uploaded and model is not None:
     try:
         img = Image.open(uploaded).convert("RGB")
-        
         col1, col2 = st.columns(2)
         
         with col1:
@@ -66,7 +91,6 @@ if uploaded and model is not None:
             
         with col2:
             st.write("Classifying...")
-            
             image_tensor = process_image(img)
             preds = model.predict(image_tensor)