Update app.py

app.py

@@ -104,32 +104,31 @@ explanation_text = {
     'Others': "Non-specific features detected, marked as Others."
 }
 
-# --- Display LIME only (Grad-CAM not possible with TFSMLayer) ---
+# --- Display LIME only ---
 def display_lime_visualization(img, true_label, pred_label, pred_idx):
-    st.info("⚠️ Grad-CAM is disabled because the model is loaded as a TFSMLayer (inference-only).")
-
-    explanation = explainer.explain_instance(
-        image=img,
-        classifier_fn=predict_fn,
-        top_labels=1,
-        hide_color=0,
-        num_samples=1000
-    )
-    temp, mask = explanation.get_image_and_mask(label=pred_idx, positive_only=True, num_features=10, hide_rest=False)
-
-    fig, axs = plt.subplots(1, 2, figsize=(12, 5))
-    axs[0].imshow(img)
-    axs[0].set_title(f"Original\nTrue: {true_label}")
-    axs[1].imshow(mark_boundaries(temp, mask))
-    axs[1].set_title(f"LIME Explanation\nPred: {pred_label}")
-    for ax in axs:
-        ax.axis('off')
-
-    summary = explanation_text.get(pred_label, "Model detected features matching this class.")
-    plt.figtext(0.5, 0.01, summary, wrap=True, ha='center', fontsize=10)
-    plt.tight_layout(rect=[0, 0.03, 1, 1])
-    st.pyplot(fig)
-    plt.close()
+    with st.spinner("🟡 LIME Explanation is Loading..."):
+        explanation = explainer.explain_instance(
+            image=img,
+            classifier_fn=predict_fn,
+            top_labels=1,
+            hide_color=0,
+            num_samples=1000
+        )
+        temp, mask = explanation.get_image_and_mask(label=pred_idx, positive_only=True, num_features=10, hide_rest=False)
+
+        fig, axs = plt.subplots(1, 2, figsize=(12, 5))
+        axs[0].imshow(img)
+        axs[0].set_title(f"Original\nTrue: {true_label}")
+        axs[1].imshow(mark_boundaries(temp, mask))
+        axs[1].set_title(f"LIME Explanation\nPred: {pred_label}")
+        for ax in axs:
+            ax.axis('off')
+
+        summary = explanation_text.get(pred_label, "Model detected features matching this class.")
+        plt.figtext(0.5, 0.01, summary, wrap=True, ha='center', fontsize=10)
+        plt.tight_layout(rect=[0, 0.03, 1, 1])
+        st.pyplot(fig)
+        plt.close()
 
 # --- Streamlit UI ---
 st.set_page_config(page_title="🧠 Retina Disease Classifier with LIME", layout="centered")
@@ -153,6 +152,6 @@ if uploaded_file:
     pred_label = CLASS_NAMES[pred_idx]
     confidence = np.max(preds) * 100
 
-    st.success(f"Prediction: **{pred_label}** with confidence {confidence:.2f}%")
+    st.success(f"✅ Prediction: **{pred_label}** with confidence **{confidence:.2f}%**")
 
     display_lime_visualization(processed_img, "Uploaded Image", pred_label, pred_idx)