app.py

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")