Update app.py

app.py

@@ -0,0 +1,103 @@
+import streamlit as st
+import numpy as np
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing.image import img_to_array
+from PIL import Image
+
+# 🔧 Configure Streamlit
+st.set_page_config(
+    page_title="WildVision 🐾 | Animal Identifier",
+    layout="centered",
+    initial_sidebar_state="auto"
+)
+
+st.title("🤖 Smart Animal Identifier")
+st.markdown("Upload up to **3 animal images** and discover what species they are!")
+
+# ✅ Debug Check
+st.write("🔧 App started successfully")
+
+# 📦 Load model and class labels with error handling
+@st.cache_resource
+def load_model_once():
+    return load_model("animal_model.keras")
+
+@st.cache_data
+def load_labels():
+    return np.load("class_labels.npy", allow_pickle=True).item()
+
+try:
+    model = load_model_once()
+    st.success("✅ Model loaded successfully")
+except Exception as e:
+    st.error(f"❌ Failed to load model: {e}")
+    st.stop()
+
+try:
+    class_indices = load_labels()
+    st.success("✅ Labels loaded successfully")
+except Exception as e:
+    st.error(f"❌ Failed to load label file: {e}")
+    st.stop()
+
+# 🔄 Reverse index to get labels from prediction index
+index_to_label = {v: k for k, v in class_indices.items()}
+
+# 📤 Upload Images
+uploaded_files = st.file_uploader(
+    "📸 Upload your animal photos",
+    type=["jpg", "jpeg", "png"],
+    accept_multiple_files=True
+)
+
+if uploaded_files and len(uploaded_files) > 0:
+    uploaded_files = uploaded_files[:3]  # Limit to 3 images
+    cols = st.columns(len(uploaded_files))
+
+    for idx, uploaded_file in enumerate(uploaded_files):
+        with cols[idx]:
+            st.markdown(f"#### 📷 Image {idx + 1}")
+
+            # 🖼️ Load and preview
+            try:
+                image = Image.open(uploaded_file).convert("RGB")
+                preview = image.copy()
+                preview.thumbnail((150, 150))
+                st.image(preview, caption="Image Preview", use_container_width=True)
+            except Exception as e:
+                st.error(f"⚠️ Could not load image: {e}")
+                continue
+
+            # 🧪 Preprocess image
+            resized = image.resize((128, 128))
+            img_array = img_to_array(resized) / 255.0
+            img_array = np.expand_dims(img_array, axis=0)
+
+            # 🔮 Make prediction
+            try:
+                preds = model.predict(img_array)[0]
+            except Exception as e:
+                st.error(f"❌ Prediction failed: {e}")
+                continue
+
+            if len(preds) != len(index_to_label):
+                st.error("⚠️ Mismatch between model output and class labels.")
+                continue
+
+            # 📊 Display predictions
+            top_indices = preds.argsort()[-3:][::-1]
+            top_labels = [index_to_label[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()}*")
+
+else:
+    st.info("📥 Please upload 1 to 3 animal images to get started.")
+
+# 📝 Footer
+st.markdown("---")
+st.caption("🐾 Developed by Neena | Powered by TensorFlow & Streamlit")