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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")
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