import streamlit as st
import numpy as np
from PIL import Image

# Load your similarity search system and define a function to find similar images
def find_similar_images(image):
    # Your code here
    return similar_images

# Define the Streamlit app
def app():
    st.title('Image Similarity Search')
    
    # Define the test dataset and allow the user to choose an image
    test_images = ['image1.jpg', 'image2.jpg', 'image3.jpg']
    image_choice = st.selectbox('Select an image:', test_images)
    
    # Load the selected image
    image = Image.open(image_choice)
    
    # Display the selected image
    st.image(image, caption='Selected Image', use_column_width=True)
    
    # Allow the user to upload an image
    uploaded_file = st.file_uploader('Upload an image:', type=['jpg', 'png'])
    
    if uploaded_file is not None:
        # Load the uploaded image
        uploaded_image = Image.open(uploaded_file)
        
        # Display the uploaded image
        st.image(uploaded_image, caption='Uploaded Image', use_column_width=True)
        
        # Find the most similar images
        similar_images = find_similar_images(uploaded_image)
        
        # Display the similar images
        for i in range(len(similar_images)):
            st.image(similar_images[i], caption='Similar Image ' + str(i+1), use_column_width=True)

# Run the Streamlit app
if __name__ == '__main__':
    app()