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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)
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