Update app.py

app.py

@@ -92,26 +92,45 @@ model = load_model()
 
 with st.sidebar:
     uploaded_files = st.file_uploader("📂 Upload retinal images", type=["jpg", "jpeg", "png"], accept_multiple_files=True)
+    selected_filename = None
+    if uploaded_files:
+        filenames = [f.name for f in uploaded_files]
+        selected_filename = st.selectbox("🎯 Select an image to preprocess and predict", filenames)
 
-# -- Optional: Show preprocessing for any selected image --
-if uploaded_files:
-    st.markdown("## 🧪 View Preprocessing of a Selected Image")
-    selected_file_name = st.selectbox("Select an image to view preprocessing", [f.name for f in uploaded_files])
-    if selected_file_name:
-        selected_file = next(f for f in uploaded_files if f.name == selected_file_name)
-        bgr = cv2.imdecode(np.frombuffer(selected_file.read(), np.uint8), cv2.IMREAD_COLOR)
-        rgb = cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB)
+# --- Process selected image ---
+if uploaded_files and selected_filename:
+    file = next(f for f in uploaded_files if f.name == selected_filename)
+
+    # Read bytes once and reset pointer for later use
+    file_bytes = file.read()
+    file.seek(0)
+
+    bgr = cv2.imdecode(np.frombuffer(file_bytes, np.uint8), cv2.IMREAD_COLOR)
+    rgb = cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB)
+
+    st.subheader(f"🔍 Preprocessing & Prediction for: {selected_filename}")
+    preprocessed = preprocess_with_steps(rgb)
+    input_tensor = np.expand_dims(preprocessed, axis=0)
 
-        st.subheader(f"🔍 Preprocessing Steps for {selected_file_name}")
-        _ = preprocess_with_steps(rgb)
+    preds = predict(input_tensor, model)
+    pred_idx = np.argmax(preds)
+    pred_label = CLASS_NAMES[pred_idx]
+    confidence = np.max(preds) * 100
 
-# -- Predict & Show LIME for All Uploaded Images --
+    st.success(f"✅ Prediction: **{pred_label}** ({confidence:.2f}%)")
+
+    show_lime(preprocessed, model, pred_idx, pred_label)
+
+# --- Show LIME for all images ---
 if uploaded_files:
-    st.markdown("## 🧪 Predictions and LIME Explanations for All Images")
+    st.markdown("## 🧪 LIME Explanations for All Images")
     cols = st.columns(min(4, len(uploaded_files)))
-
     for i, file in enumerate(uploaded_files):
-        bgr = cv2.imdecode(np.frombuffer(file.read(), np.uint8), cv2.IMREAD_COLOR)
+        # Read bytes once and reset pointer
+        file_bytes = file.read()
+        file.seek(0)
+
+        bgr = cv2.imdecode(np.frombuffer(file_bytes, np.uint8), cv2.IMREAD_COLOR)
         rgb = cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB)
         img = cv2.resize(rgb, IMG_SIZE) / 255.0
         input_tensor = np.expand_dims(img, axis=0)
@@ -119,17 +138,15 @@ if uploaded_files:
         preds = predict(input_tensor, model)
         pred_idx = np.argmax(preds)
         pred_label = CLASS_NAMES[pred_idx]
-        confidence = np.max(preds) * 100
 
         with cols[i % len(cols)]:
-            st.markdown(f"**{file.name}**<br>🧠 *{pred_label}* ({confidence:.2f}%)", unsafe_allow_html=True)
-            with st.spinner("🟡 LIME explanation is loading..."):
-                explanation = LIME_EXPLAINER.explain_instance(
-                    image=img,
-                    classifier_fn=lambda imgs: predict(imgs, model),
-                    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)
-                st.image(mark_boundaries(temp, mask), use_column_width=True)
+            st.markdown(f"**{file.name}**<br>🧠 *{pred_label}*", unsafe_allow_html=True)
+            explanation = LIME_EXPLAINER.explain_instance(
+                image=img,
+                classifier_fn=lambda imgs: predict(imgs, model),
+                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)
+            st.image(mark_boundaries(temp, mask), use_column_width=True)