Update app.py

app.py

@@ -6,7 +6,7 @@ from PIL import Image
 
 # 🔧 Configure Streamlit
 st.set_page_config(
-    page_title="Animal Classifier",
+    page_title="WildVision 🐾 | Animal Identifier",
     layout="centered",
     initial_sidebar_state="auto"
 )
@@ -24,42 +24,50 @@ model = load_model_once()
 class_indices = load_labels()
 class_labels = list(class_indices.keys())
 
-# 🏷 App Title
-st.title("🐾 Animal Image Classifier")
-st.markdown("Upload up to **3 images** and get predictions")
+# 🏷 App Header
+st.title("🦁 WildVision - Smart Animal Identifier")
+st.markdown("Upload up to **3 animal images** and discover what species they are!")
 
 # 📤 Upload Images
 uploaded_files = st.file_uploader(
-    "Upload animal images",
+    "📸 Upload your animal photos",
     type=["jpg", "jpeg", "png"],
     accept_multiple_files=True
 )
 
 if uploaded_files:
-    # Limit to 3 images for clarity
+    # Limit to 3 images
     uploaded_files = uploaded_files[:3]
-    cols = st.columns(len(uploaded_files))  
+    cols = st.columns(len(uploaded_files))
 
     for idx, uploaded_file in enumerate(uploaded_files):
         with cols[idx]:
-            st.markdown(f"### Image {idx+1}")
+            st.markdown(f"#### 📷 Image {idx + 1}")
 
-            # 🖼️ Load & visually shrink image
+            # 🖼️ Load and preview
             image = Image.open(uploaded_file).convert("RGB")
             preview = image.copy()
-            preview.thumbnail((150, 150))  
-            st.image(preview, caption="Preview", use_container_width=True)
+            preview.thumbnail((150, 150))
+            st.image(preview, caption="Image Preview", use_container_width=True)
 
-            # 🧪 Preprocess image for prediction
-            resized = image.resize((128, 128))  
+            # 🧪 Preprocess for prediction
+            resized = image.resize((128, 128))
             img_array = img_to_array(resized) / 255.0
             img_array = np.expand_dims(img_array, axis=0)
 
-            # 🔮 Model Prediction
+            # 🔮 Make prediction
             preds = model.predict(img_array)[0]
-            top_indices = preds.argsort()[-3:][::-1]
-            top_labels = [class_labels[i] for i in top_indices]
-            top_scores = [preds[i] for i in top_indices]
 
-            # ✅ Display Top Prediction
-            st.success(f"**🔮Prediction:** {top_labels[0].capitalize()}")
+            # ✅ Safe check
+            if len(preds) != len(class_labels):
+                st.error("⚠️ Mismatch between model outputs and label classes.")
+            else:
+                top_indices = preds.argsort()[-3:][::-1]
+                top_labels = [class_labels[i] for i in top_indices]
+                top_scores = [preds[i] for i in top_indices]
+
+                st.markdown("#### 🧠 Top Predictions:")
+                for label, score in zip(top_labels, top_scores):
+                    st.write(f"➡️ **{label.capitalize()}**: {score:.2%}")
+
+                st.success(f"🎯 **Most Likely:** *{top_labels[0].capitalize()}* 🐾")