Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,44 +1,48 @@
-import os
-os.environ["TF_USE_LEGACY_KERAS"] = "1"
-
 import streamlit as st
 import numpy as np
 from PIL import Image
 import tensorflow as tf
+import os
 
+# -----------------------------------
 # CONFIG
+# -----------------------------------
 IMG_SIZE = 300
 CLASS_NAMES = ['cup', 'fork', 'glass', 'knife', 'plate', 'spoon']
 
+# -----------------------------------
 # MODEL LOADING
+# -----------------------------------
 @st.cache_resource
 def load_model_file():
     model_path = os.path.join(os.path.dirname(__file__), "kitchen1.keras")
-    
+    # TF 2.14'te de bu yöntem çalışır
     return tf.keras.models.load_model(model_path, compile=False)
 
 try:
     model = load_model_file()
 except Exception as e:
-    st.error(f"Error: {e}")
+    st.error(f"Model yüklenirken hata: {e}")
     st.stop()
 
+# -----------------------------------
 # IMAGE PREPROCESS
+# -----------------------------------
 def process_image(img):
     img = img.resize((IMG_SIZE, IMG_SIZE))
     img = np.array(img)
-    
     if img.ndim == 2:
         img = np.stack([img]*3, axis=-1)
     elif img.shape[-1] == 4:
         img = img[..., :3]
     
     img = np.expand_dims(img, 0)
-    
     img = tf.keras.applications.efficientnet.preprocess_input(img)
     return img
 
+# -----------------------------------
 # UI LAYOUT
+# -----------------------------------
 st.title("🍽️ Kitchenware Classifier")
 st.write("Upload an image to classify kitchen items using EfficientNetB3.")
 
@@ -47,7 +51,6 @@ uploaded = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
 if uploaded:
     try:
         img = Image.open(uploaded).convert("RGB")
-        
         col1, col2 = st.columns(2)
         
         with col1:
@@ -55,7 +58,6 @@ if uploaded:
             
         with col2:
             st.write("Classifying...")
-            
             image_tensor = process_image(img)
             preds = model.predict(image_tensor)
             
@@ -66,6 +68,5 @@ if uploaded:
             st.success(f"Prediction: **{predicted_class.upper()}**")
             st.metric("Confidence", f"{confidence * 100:.2f}%")
             st.progress(int(confidence * 100))
-            
     except Exception as e:
-        st.error(f"Error: {e}")
+        st.error(f"Hata: {e}")