Update app.py

app.py

@@ -4,14 +4,14 @@ from tensorflow.keras.models import load_model
 from tensorflow.keras.preprocessing.image import img_to_array
 from PIL import Image, UnidentifiedImageError
 
-# 🔧 Configure Streamlit
+# Configure Streamlit
 st.set_page_config(
     page_title="Animal Classifier",
     layout="centered",
     initial_sidebar_state="auto"
 )
 
-# 📦 Load model and class labels
+# Load model and class labels
 @st.cache_resource
 def load_model_once():
     return load_model("animal_model.keras")
@@ -24,11 +24,11 @@ model = load_model_once()
 class_indices = load_labels()
 class_labels = list(class_indices.keys())
 
-# 🏷 App Title
+# Title
 st.title("🐾 Animal Image Classifier")
-st.markdown("Upload up to **3 images** and get predictions")
+st.markdown("Upload up to **3 images** to get predictions")
 
-# 📤 Upload Images
+# File uploader
 uploaded_files = st.file_uploader(
     "Upload animal images",
     type=["jpg", "jpeg", "png"],
@@ -36,43 +36,37 @@ uploaded_files = st.file_uploader(
 )
 
 if uploaded_files:
-    uploaded_files = uploaded_files[:3]  # Limit to 3
+    uploaded_files = uploaded_files[:3]  # Limit to 3 files
     cols = st.columns(len(uploaded_files))
 
     for idx, uploaded_file in enumerate(uploaded_files):
         with cols[idx]:
             st.markdown(f"### Image {idx + 1}")
             try:
-                # 🖼️ Load & preview image
                 image = Image.open(uploaded_file).convert("RGB")
                 preview = image.copy()
                 preview.thumbnail((150, 150))
-                st.image(preview, caption="📷 Preview", use_container_width=True)
-
-                # 🧪 Preprocess image
-                resized = image.resize((128, 128))  # Adjust size as per your model
-                st.image(resized, caption="🧪 Resized for Model (128x128)", use_container_width=False)
+                st.image(preview, caption="Preview", use_container_width=False)
 
+                # Resize and preprocess
+                resized = image.resize((128, 128))
                 img_array = img_to_array(resized) / 255.0
                 img_array = np.expand_dims(img_array, axis=0)
 
-                # 🔮 Predict
+                # 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 Predictions
-                st.success(f"**🔮 Top Prediction:** {top_labels[0].capitalize()}")
-                st.markdown("##### 📊 Top 3 Predictions:")
+                # Display predictions
+                st.success(f"Top Prediction: **{top_labels[0].capitalize()}**")
+                st.markdown("##### Top 3 Predictions:")
                 for label, score in zip(top_labels, top_scores):
                     st.write(f"**{label.capitalize()}** — {score:.4f}")
 
             except UnidentifiedImageError:
-                st.error("❌ Error: Invalid image file.")
+                st.error("Error: Invalid image file.")
             except Exception as e:
-                st.error(f"⚠️ Unexpected error: {str(e)}")
+                st.error(f"Unexpected error: {str(e)}")
 
-# 📌 Footer message
-st.sidebar.markdown("---")
-st.sidebar.markdown("Built with ❤️ using Streamlit and Keras.")