Update app.py

app.py

@@ -18,21 +18,16 @@ st.write("This app uses a YOLO model to detect benthic megafauna in images.")
 # Load example images
 examples = glob.glob("images/*.png")
 
-# Display list of example images to choose from
+# Sidebar for selecting example images
 st.sidebar.title("Example Images")
 selected_example = st.sidebar.selectbox("Select an example image", examples)
 
-# Display the selected example image
-if selected_example:
-    st.image(selected_example, caption="Selected Example Image", use_column_width=True)
-
 # File uploader for custom images
 uploaded_file = st.file_uploader("Or upload an image", type=["png", "jpg", "jpeg"])
 
 # Select which image to use for prediction
 if uploaded_file is not None:
     image = Image.open(uploaded_file)
-    st.image(image, caption="Uploaded Image", use_column_width=True)
     image_path = uploaded_file
 elif selected_example:
     image = Image.open(selected_example)
@@ -41,9 +36,27 @@ else:
     image = None
     image_path = None
 
-# Run the YOLO model on the selected image
+# Run the YOLO model and display the results side by side
 if image_path is not None:
     results = model.predict(image_path, **PREDICT_KWARGS)
-    st.image(results[0].plot()[:, :, ::-1], caption="Predicted Image", use_column_width=True)
+    prediction_image = results[0].plot()[:, :, ::-1]  # Processed image with predictions
+
+    # Create two columns for side-by-side display
+    col1, col2 = st.columns(2)
+
+    with col1:
+        st.image(image, caption="Selected Image", use_column_width=True)
+    
+    with col2:
+        st.image(prediction_image, caption="Predicted Image", use_column_width=True)
 else:
     st.write("Please upload an image or select an example to proceed.")
+
+# Footer with copyright and image credit
+st.markdown(
+    """
+    ---
+    **© 2024 Atticus Carter, [https://oceancv.org/](https://oceancv.org/)**  
+    **Image Credit: Ocean Observatories Initiative 2022**
+    """
+)