Update app.py

app.py

@@ -5,8 +5,26 @@ import numpy as np
 from PIL import Image
 import base64
 
-hide_streamlit_style = """
+st.set_page_config(
+        page_title="Pneumonia Detection App",
+        page_icon=":microscope:",
+        layout="wide"
+    )
+
+custom_style = """
                 <style>
+                .st-bw {
+                border-width: 2px !important;
+                border-color: #3498db !important;
+                border-radius: 10px !important;
+                padding: 10px !important;
+                box-shadow: 0px 0px 10px #888888 !important;
+            }
+            .stButton>button {
+                background-color: #3498db !important;
+                color: white !important;
+                border-radius: 5px !important;
+            }
                 div[data-testid="stToolbar"] {
                 visibility: hidden;
                 height: 0%;
@@ -36,7 +54,7 @@ hide_streamlit_style = """
                 }
                 </style>
                 """
-st.markdown(hide_streamlit_style, unsafe_allow_html=True)
+st.markdown(custom_style, unsafe_allow_html=True)
 
 
 # Load the pre-trained model
@@ -75,11 +93,6 @@ def display_image_with_download(image_path, caption, download_text):
 
 # Streamlit app
 def main():
-    st.set_page_config(
-        page_title="Pneumonia Detection App",
-        page_icon=":microscope:",
-        layout="wide"
-    )
 
         
     st.title("Pneumonia Detection")
@@ -109,7 +122,10 @@ def main():
         display_image_with_download(normal_image_path, "Normal Image", "Normal Image")
 
     if uploaded_file is not None:
+        st.markdown('<div class="st-bw">', unsafe_allow_html=True)
         st.image(uploaded_file, caption="Uploaded Image", use_column_width=True)
+        st.markdown('</div>', unsafe_allow_html=True)
+
 
         # Make predictions
         prediction = predict_image(uploaded_file)