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import ripple | |
import streamlit as stl | |
from tqdm.auto import tqdm | |
# streamlit app | |
stl.set_page_config( | |
page_title="Ripple", | |
) | |
stl.title("ripple search") | |
stl.write( | |
"An app that uses text input to search for described images, using embeddings of selected image datasets. Uses contrastive learning models(CLIP) and the sentence transformers library" | |
) | |
stl.link_button( | |
label="link to github and full library code", | |
url="https://github.com/kelechi-c/ripple_net", | |
) | |
dataset = stl.selectbox( | |
"choose huggingface dataset(bgger datasets take more time to embed..)", | |
options=[ | |
"huggan/wikiart(1k)", | |
"huggan/wikiart(11k)", | |
"zh-plus/tiny-imagenet(110k)", | |
"lambdalabs/naruto-blip-captions(1k)", | |
"detection-datasets/fashionpedia(45k)", | |
], | |
) | |
# initalized global variables | |
embedded_data = None | |
embedder = None | |
text_search = None | |
ret_images = [] | |
scores = [] | |
if dataset and stl.button("embed image dataset"): | |
with stl.spinner("Initializing and creating image embeddings from dataset"): | |
embedder = ripple.ImageEmbedder( | |
dataset, retrieval_type="text-image", dataset_type="huggingface" | |
) | |
embedded_data = embedder.create_embeddings(device="cpu") | |
stl.success("Sucessfully embedded and dcreated image index") | |
if embedded_data is not None: | |
text_search = ripple.TextSearch(embedded_data, embedder.embed_model) | |
stl.success("Initialized text search class") | |
search_term = stl.text_input("Text description/search for image") | |
if search_term: | |
with stl.spinner("retrieving images with description.."): | |
scores, ret_images = text_search.get_similar_images( | |
search_term, k_images=4) | |
stl.success(f"sucessfully retrieved {len(ret_images)}") | |
for count, score, image in tqdm(zip(range(len(ret_images)), scores, ret_images)): | |
stl.image(image["image"][count]) | |
stl.write(score) |