uodated formatting and make it standardize

app.py

@@ -3,35 +3,89 @@ from PIL import Image
 import numpy as np
 from tensorflow.keras.models import load_model
 
+# Custom CSS for styling
+custom_css = """
+<style>
+body {
+    background-color: #f0f2f6;
+}
+.stButton>button {
+    background-color: #4CAF50;
+    color: white;
+    padding: 10px 15px;
+    text-align: center;
+    text-decoration: none;
+    display: inline-block;
+    font-size: 16px;
+    margin: 4px 2px;
+    cursor: pointer;
+    border-radius: 8px;
+    border: none;
+}
+.stButton>button:hover {
+    background-color: #45a049;
+}
+.stTextInput>div>div>input {
+    border-radius: 8px;
+}
+.stTextInput>div>div>input:focus {
+    border-color: #4CAF50;
+    box-shadow: none;
+}
+</style>
+"""
+
+st.markdown(custom_css, unsafe_allow_html=True)
+
+# Set page configuration
+st.set_page_config(
+    page_title="Face Mask Detection App",
+    page_icon=":mask:",
+    layout="wide"
+)
+
 # Define a function to make predictions on a single image
 def predict_single_image(image, model):
-    # Resize and preprocess the image
-    img = image.resize((128, 128))
-    img = np.array(img) / 255.0  # Normalization
-    img = np.expand_dims(img, axis=0)  # Add batch dimension
+    try:
+        # Resize and preprocess the image
+        img = image.resize((128, 128))
+        img = np.array(img) / 255.0  # Normalization
+        img = np.expand_dims(img, axis=0)  # Add batch dimension
 
-    # Make prediction using the provided model
-    prediction = model.predict(img)
+        # Make prediction using the provided model
+        prediction = model.predict(img)
 
-    # Thresholding prediction
-    threshold = 0.5
-    prediction_class = (prediction > threshold).astype(int)
+        # Thresholding prediction
+        threshold = 0.5
+        prediction_class = (prediction > threshold).astype(int)
 
-    # Interpret prediction
-    if prediction_class == 1:
-        return "With Mask"
-    else:
-        return "Without Mask"
+        # Interpret prediction
+        if prediction_class == 1:
+            return "With Mask"
+        else:
+            return "Without Mask"
+    except Exception as e:
+        st.error(f"Error occurred during prediction: {str(e)}")
 
 # Load the model from .h5 file
 @st.cache(allow_output_mutation=True)
 def load_model_from_h5():
-    return load_model('model.h5')
+    try:
+        return load_model('model.h5')
+    except Exception as e:
+        st.error(f"Error occurred while loading the model: {str(e)}")
 
 # Streamlit app
 def main():
-    st.title("Mask Detection App")
-    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])
+    # App title and description
+    st.title("Face Mask Detection App")
+    st.write("Upload an image and click 'Predict' to see the prediction.")
+
+    # Header Image
+    header_image = Image.open("header_image.jpg")
+    st.image(header_image, use_column_width=True)
+
+    uploaded_file = st.file_uploader("", type=["jpg", "png", "jpeg"])
 
     if uploaded_file is not None:
         # Load the image
@@ -39,12 +93,14 @@ def main():
         st.image(image, caption='Uploaded Image.', use_column_width=True)
 
         # Button to make prediction
-        if st.button('Predict'):
+        if st.button('Predict', key='predict_button'):
             # Load the model from .h5 file
             model_h5 = load_model_from_h5()
-            # Make predictions using the provided model
-            prediction = predict_single_image(image, model_h5)
-            st.write("Prediction:", prediction)
+            if model_h5:
+                # Make predictions using the provided model
+                prediction = predict_single_image(image, model_h5)
+                if prediction:
+                    st.success(f"Prediction: {prediction}")
 
 if __name__ == '__main__':
-    main()
+    main()