app.py

import streamlit as st
from PIL import Image
import numpy as np
import cv2
from tensorflow.keras.models import load_model
import os
# Ensure the 'upload' directory exists
upload_folder = 'uploads'
if not os.path.exists(upload_folder):
    os.makedirs(upload_folder)
    
# Load the pre-trained model
model = load_model("catVsdogs.keras")

def get_result(img_path):
    img = cv2.imread(img_path)
    img_resize = cv2.resize(img, (150, 150))
    img_resize = np.array(img_resize, dtype=np.float32)
    img_resize /= 255.0
    img_input = img_resize.reshape(1, 150, 150, 3)
    prediction = model.predict(img_input)

    if prediction[0][0] > 0.5:
        return "It's a Dog 🐶"
    else:
        return "It's a Cat 🐱"
    
           
st.title('Is it a Cat or Dog 🐶🐱')
uploaded_image = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
if uploaded_image is not None:
    image = Image.open(uploaded_image)
    
    image_path = os.path.join(upload_folder, uploaded_image.name)
    image.save(image_path)
    output = get_result(image_path)
    
    st.write(output)
    st.image(image, use_container_width=True)