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import gradio as gr
from PIL import Image
from datasets import load_dataset, Dataset
import random
import numpy as np
import time
# Dataset
ds = load_dataset("tonyassi/finesse1-embeddings", split='train')
id_to_row = {row['id']: row for row in ds}
remaining_ds = None
preference_embedding = []
###################################################################################
def get_random_images(dataset, num):
# Select 4 random indices from the dataset
random_indices = random.sample(range(len(dataset)), num)
# Get the 4 random images
random_images = dataset.select(random_indices)
# Create a new dataset with the remaining images
remaining_indices = [i for i in range(len(dataset)) if i not in random_indices]
new_dataset = dataset.select(remaining_indices)
return random_images, new_dataset
def find_similar_images(dataset, num, embedding):
# Ensure FAISS index exists and search for similar images
dataset.add_faiss_index(column='embeddings')
scores, retrieved_examples = dataset.get_nearest_examples('embeddings', np.array(embedding), k=num)
# Drop FAISS index after use to avoid re-indexing
dataset.drop_index('embeddings')
# Extract all dataset IDs and use a set to find remaining indices
dataset_ids = dataset['id']
retrieved_ids_set = set(retrieved_examples['id'])
# Use a list comprehension with enumerate for faster indexing
remaining_indices = [i for i, id in enumerate(dataset_ids) if id not in retrieved_ids_set]
# Create a new dataset without the retrieved images
new_dataset = dataset.select(remaining_indices)
return retrieved_examples, new_dataset
def average_embedding(embedding1, embedding2):
embedding1 = np.array(embedding1)
embedding2 = np.array(embedding2)
return (embedding1 + embedding2) / 2
###################################################################################
def load_images():
print("ds", ds.num_rows)
global remaining_ds
remaining_ds = ds
global preference_embedding
preference_embedding = []
# Get random images
rand_imgs, remaining_ds = get_random_images(ds, 10)
# Create a list of tuples [(img1,caption1),(img2,caption2)...]
result = list(zip(rand_imgs['image'], [str(id) for id in rand_imgs['id']]))
return result
def select_image(evt: gr.SelectData, gallery, preference_gallery):
global remaining_ds
print("remaining_ds", remaining_ds.num_rows)
# Selected image
selected_id = int(evt.value['caption'])
selected_row = id_to_row[selected_id]
selected_embedding = selected_row['embeddings']
selected_image = selected_row['image']
# Update preference embedding
global preference_embedding
if len(preference_embedding) == 0:
preference_embedding = selected_embedding
else:
preference_embedding = average_embedding(preference_embedding, selected_embedding)
# Find images which are most similar to the preference embedding
simlar_images, remaining_ds = find_similar_images(remaining_ds, 5, preference_embedding)
# Create a list of tuples [(img1,caption1),(img2,caption2)...]
result = list(zip(simlar_images['image'], [str(id) for id in simlar_images['id']]))
# Get random images
rand_imgs, remaining_ds = get_random_images(remaining_ds, 5)
# Create a list of tuples [(img1,caption1),(img2,caption2)...]
random_result = list(zip(rand_imgs['image'], [str(id) for id in rand_imgs['id']]))
final_result = result + random_result
# Update prefernce gallery
if (preference_gallery==None):
final_preference_gallery = [selected_image]
else:
final_preference_gallery = [selected_image] + preference_gallery
return gr.Gallery(value=final_result, selected_index=None), final_preference_gallery
###################################################################################
with gr.Blocks() as demo:
gr.Markdown("""
<center><h1> Product Recommendation using Image Similarity </h1></center>
<center>by <a href="https://www.tonyassi.com/" target="_blank">Tony Assi</a></center><br>
<center><i> This is a demo of product recommendation using image similarity of user preferences. </i><a href="https://huggingface.co/blog/tonyassi/product-recommendation-using-image-similarity/" target="_blank">Read the article.</a></center> <br>
The the user selects their favorite product which then gets added to the user preference group. Each of the image embeddings in the user preference products get averaged into a preference embedding. Each round some products are displayed: 5 products most similar to user preference embedding and 5 random products. Embeddings are generated with [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224). The dataset used is [tonyassi/finesse1-embeddings](https://huggingface.co/datasets/tonyassi/finesse1-embeddings).
""")
product_gallery = gr.Gallery(columns=5, object_fit='contain', allow_preview=False, label='Products')
preference_gallery = gr.Gallery(columns=5, object_fit='contain', allow_preview=False, label='Preference', interactive=False)
demo.load(load_images, inputs=None, outputs=[product_gallery])
product_gallery.select(select_image, inputs=[product_gallery, preference_gallery], outputs=[product_gallery, preference_gallery])
demo.launch() |