Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,21 +1,28 @@
 import streamlit as st
-from PIL import Image
 import numpy as np
-import io
+from PIL import Image
 import base64
+import io
 from tensorflow.keras.models import load_model
 
 st.set_page_config(page_title="Hurma Sınıflandırıcı", layout="centered")
 
-# Model yükle
-model = load_model("src/dates_classifier_model.h5")
+st.title("📷 Hurma Resmi Sınıflandırma")
+st.write("Lütfen bir hurma resmi yükleyin ve hangi tür olduğunu tahmin edelim.")
+
+# --- MODEL ---
+try:
+    model = load_model("src/dates_classifier_model.h5")
+except Exception as e:
+    st.error(f"Model yüklenemedi: {e}")
+    st.stop()
 
 class_names = [
     'Rutab', 'Meneifi', 'Sokari', 'Galaxy', 'Shaishe',
     'Medjool', 'Ajwa', 'Nabtat Ali', 'Sugaey'
 ]
 
-# Base64 saklama
+# --- IMAGE SESSION ---
 def image_to_base64(image_bytes):
     return base64.b64encode(image_bytes).decode("utf-8")
 
@@ -28,29 +35,23 @@ def process_image(img):
     img = np.expand_dims(img, axis=0)
     return img
 
-st.title("📷 Hurma Resmi Sınıflandırma")
-st.write("Lütfen bir hurma resmi yükleyin.")
-
-# Session state ile güvenli saklama
 if "image_data" not in st.session_state:
     st.session_state.image_data = None
 
-uploaded_file = st.file_uploader("Resim Seçin (.jpg, .png)", type=["jpg", "jpeg", "png"])
+uploaded_file = st.file_uploader("Resim Seç (.jpg, .jpeg, .png)", type=["jpg", "jpeg", "png"])
 
-# Yeni yükleme varsa base64 sakla
 if uploaded_file is not None:
     st.session_state.image_data = image_to_base64(uploaded_file.read())
 
-# Görsel işleme
 if st.session_state.image_data:
     try:
         img = base64_to_image(st.session_state.image_data)
         st.image(img, caption="Yüklenen Resim", use_column_width=True)
 
-        processed = process_image(img)
-        prediction = model.predict(processed)
+        processed_img = process_image(img)
+        prediction = model.predict(processed_img)
         predicted_class = np.argmax(prediction)
 
         st.success(f"Tahmin: **{class_names[predicted_class]}**")
     except Exception as e:
-        st.error(f"Hata oluştu: {e}")
+        st.error(f"Fotoğraf işlenemedi: {e}")