Update app.py

app.py

@@ -77,9 +77,9 @@ model.eval()
 uploaded_img = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
 
 if uploaded_img is not None:
-    # Display uploaded image
+    # Display uploaded image in a smaller size
     image = Image.open(uploaded_img)
-    st.image(image, caption="Uploaded Image", use_container_width =True)
+    st.image(image, caption="Uploaded Image", width=200)  # Set width to reduce image size
 
     # Image transformations
     sample_transform = transforms.Compose([
@@ -94,6 +94,13 @@ if uploaded_img is not None:
     # Model inference
     with torch.no_grad():
         pred = model(transformed_img).argmax(dim=1).item()
-
-    # Display prediction
-    st.success(f"Predicted Class: {class_names[pred]}")
+    
+    # Stylish output box
+    st.markdown(
+        f"""
+        <div class="output-container">
+            🧠 <strong>Predicted Class:</strong> {class_names[pred]}
+        </div>
+        """,
+        unsafe_allow_html=True
+    )