app.py

import streamlit as st
import tensorflow as tf
import numpy as np
import pickle
from tensorflow.keras.preprocessing import image

model = tf.keras.models.load_model("animal_mobilenet_model.keras")

labels = pickle.load(open("labels.pkl","rb"))

class_names = list(labels.keys())

st.title("🐾 Animal Classifier (MobileNetV2)")

uploaded_file = st.file_uploader("Upload Animal Image")

if uploaded_file:

    img = image.load_img(uploaded_file, target_size=(224,224))

    img_array = image.img_to_array(img)/255

    img_array = np.expand_dims(img_array, axis=0)

    prediction = model.predict(img_array)

    predicted_class = class_names[np.argmax(prediction)]

    st.success(f"Predicted Animal: {predicted_class}")