Update src/streamlit_app.py

src/streamlit_app.py

@@ -4,68 +4,68 @@ os.environ["TF_USE_LEGACY_KERAS"] = "1"
 import streamlit as st
 import numpy as np
 from PIL import Image
-from tensorflow import keras
+import tensorflow as tf
 
-from tensorflow.keras.models import load_model
-from tensorflow.keras.applications.efficientnet import preprocess_input
-
-# -----------------------------------
 # CONFIG
-# -----------------------------------
 IMG_SIZE = 300
 CLASS_NAMES = ['cup', 'fork', 'glass', 'knife', 'plate', 'spoon']
 
-# -----------------------------------
 # MODEL LOADING
-# -----------------------------------
 @st.cache_resource
 def load_model_file():
-    return keras.models.load_model("src/kitchen1.keras", compile=False)
+    model_path = os.path.join(os.path.dirname(__file__), "kitchen1.keras")
+    
+    return tf.keras.models.load_model(model_path, compile=False)
 
-model = load_model_file()
+try:
+    model = load_model_file()
+except Exception as e:
+    st.error(f"Error: {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 = preprocess_input(img)
+    
+    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.")
 
 uploaded = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
 
 if uploaded:
-    img = Image.open(uploaded).convert("RGB")
-
-    col1, col2 = st.columns(2)
-
-    with col1:
-        st.image(img, caption="Uploaded Image", use_container_width=True)
-
-    with col2:
-        st.write("Classifying...")
-
-        image_tensor = process_image(img)
-        preds = model.predict(image_tensor)
-
-        predicted_index = np.argmax(preds)
-        predicted_class = CLASS_NAMES[predicted_index]
-        confidence = float(np.max(preds))
-
-        st.success(f"Prediction: **{predicted_class.upper()}**")
-        st.metric("Confidence", f"{confidence * 100:.2f}%")
-        st.progress(int(confidence * 100))
+    try:
+        img = Image.open(uploaded).convert("RGB")
+        
+        col1, col2 = st.columns(2)
+        
+        with col1:
+            st.image(img, caption="Uploaded Image", use_container_width=True)
+            
+        with col2:
+            st.write("Classifying...")
+            
+            image_tensor = process_image(img)
+            preds = model.predict(image_tensor)
+            
+            predicted_index = np.argmax(preds)
+            predicted_class = CLASS_NAMES[predicted_index]
+            confidence = float(np.max(preds))
+            
+            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}")