src/streamlit_app.py

import streamlit as st
from tensorflow.keras.models import load_model
from PIL import Image
import numpy as np

model = load_model('src/dog_cat.h5')

def process_image(img):
    img = img.resize((80, 80))
    img = np.array(img)
    img = img / 255.0
    img = np.expand_dims(img, axis=0)
    return img

st.title("Dog vs Cat Classification")
st.write("Upload an image to detect if it is a Dog or a Cat.")

file = st.file_uploader('Select an image', type=['jpg', 'jpeg', 'png'])

if file is not None:
    img = Image.open(file)
    st.image(img, caption='Uploaded Image', width=200)
    
    image = process_image(img)
    prediction = model.predict(image)
    
    if prediction[0][0] > 0.5:
        st.write("Prediction: **Dog**")
    else:
        st.write("Prediction: **Cat**")