Update app.py

app.py

@@ -20,19 +20,47 @@ model_path = hf_hub_download(repo_id=repo_id, filename=filename, cache_dir=cache
 # Load the model
 model = tf.keras.models.load_model(model_path) 
 
+# Streamlit UI with Christmas Theme
+st.markdown(
+    """
+    <style>
+    body {
+        background-color: #f0f0f0; /* Light gray background */
+    }
+    .stApp {
+        background-color: #f8f8f8; /* Slightly lighter background for the app container */
+        padding: 20px;
+        border-radius: 10px;
+        box-shadow: 0 2px 5px rgba(0, 0, 0, 0.1);
+    }
+    .stButton>button {
+        background-color: #c0392b; /* Christmas red for buttons */
+        color: white;
+        border: none;
+        padding: 10px 20px;
+        border-radius: 5px;
+        cursor: pointer;
+    }
+    .stButton>button:hover {
+        background-color: #e74c3c; /* Slightly darker red on hover */
+    }
+    h1 {
+        color: #27ae60; /* Christmas green for the title */
+    }
+    </style>
+    """,
+    unsafe_allow_html=True,
+)
 
-# Streamlit UI
 st.title("Christmas Tree Classifier")
 st.write("Upload an image of a Christmas tree to classify it:")
 
-    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
-    if uploaded_file is not None:
-        # ... (Rest of the code for image processing and prediction) ...
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
 
 if uploaded_file is not None:
     # Display the uploaded image
     image = Image.open(uploaded_file)
-    st.image(image, caption="Uploaded Image.", use_column_width=True)
+    st.image(image, caption="Uploaded Image.", use_container_width=True)  
     st.write("")
     st.write("Classifying...")
 
@@ -48,4 +76,4 @@ if uploaded_file is not None:
     predicted_class = "Decorated" if prediction[0][0] >= 0.5 else "Undecorated"
 
     # Display the prediction
-    st.write(f"Prediction: {predicted_class}")
+    st.write(f"Prediction: {predicted_class}")