Update app.py

app.py

@@ -27,9 +27,6 @@ CLASS_NAMES = [
     'tacos', 'takoyaki', 'tiramisu', 'tuna_tartare', 'waffles'
 ]
 
-
-
-# Define the function to predict the image
 # Define the function to predict the image
 def predict_image(img_path):
     # Load and preprocess the image
@@ -49,25 +46,57 @@ def predict_image(img_path):
     return predicted_class
 
 
-
 # Streamlit UI components
-st.title("Food-101 Classification Model")
-st.write("Upload an image of food to predict its class.")
+st.set_page_config(page_title="Food-101 Classification", page_icon="🍕", layout="wide")
+st.markdown("""
+    <style>
+    .stApp {
+        background-color: #f0f8ff;
+    }
+    .stButton>button {
+        background-color: #4CAF50;
+        color: white;
+        padding: 10px 20px;
+        border-radius: 10px;
+        font-size: 16px;
+    }
+    .stFileUploader>label {
+        color: #4CAF50;
+    }
+    .stImage>img {
+        border-radius: 10px;
+        box-shadow: 0px 4px 12px rgba(0, 0, 0, 0.1);
+    }
+    </style>
+""", unsafe_allow_html=True)
+
+# Title
+st.title("🍕 Food-101 Classification Model")
+st.subheader("Upload an image of food to predict its class. 🍽️")
 
 # Upload image
 uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
 
 if uploaded_file is not None:
-    # Display the uploaded image
-    st.image(uploaded_file, caption="Uploaded Image", use_column_width=True)
+    # Display the uploaded image with a transition effect
+    st.image(uploaded_file, caption="Uploaded Image", use_column_width=True, width=500)
 
     # Save the image temporarily
     img_path = "uploaded_image.jpg"
     with open(img_path, "wb") as f:
         f.write(uploaded_file.getbuffer())
 
-    # Make prediction
-    predicted_class = predict_image(img_path)
+    # Show the processing animation
+    with st.spinner("Classifying the image... Please wait. 🕒"):
+        # Make prediction
+        predicted_class = predict_image(img_path)
+
+    # Display the predicted class with a colorful box
+    st.markdown(f"""
+        <div style="padding: 10px; border-radius: 10px; background-color: #ffeb3b; color: #0f4b5f; text-align: center; font-size: 20px;">
+            <strong>Predicted Class: {predicted_class.capitalize()} 🥳</strong>
+        </div>
+    """, unsafe_allow_html=True)
 
-    # Display the predicted class
-    st.write(f"Predicted Class: {predicted_class}")
+    # Show an icon with a success message
+    st.balloons()