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ampehta
Revert "Merge branch 'main' of https://huggingface.co/spaces/flax-community/koclip into main"
503acf7
| import os | |
| import streamlit as st | |
| from utils import load_model, load_index | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| def app(model_name): | |
| images_directory = 'images/val2017' | |
| features_directory = f'features/val2017/{model_name}.tsv' | |
| files, index = load_index(features_directory) | |
| model, processor = load_model(f'koclip/{model_name}') | |
| st.title("Text to Image Search Engine") | |
| st.markdown(""" | |
| This demonstration explores capability of KoCLIP as a Korean-language Image search engine. Embeddings for each of | |
| 5000 images from [MSCOCO](https://cocodataset.org/#home) 2017 validation set was generated using trained KoCLIP | |
| vision model. They are ranked based on cosine similarity distance from input Text query embeddings and top 10 images | |
| are displayed below. | |
| KoCLIP is a retraining of OpenAI's CLIP model using 82,783 images from [MSCOCO](https://cocodataset.org/#home) dataset and | |
| Korean caption annotations. Korean translation of caption annotations were obtained from [AI Hub](https://aihub.or.kr/keti_data_board/visual_intelligence). | |
| Base model `koclip` uses `klue/roberta` as text encoder and `openai/clip-vit-base-patch32` as image encoder. | |
| Larger model `koclip-large` uses `klue/roberta` as text encoder and bigger `google/vit-large-patch16-224` as image encoder. | |
| Example Queries : ์ํํธ(Apartment), ์๋์ฐจ(Car), ์ปดํจํฐ(Computer) | |
| """) | |
| query = st.text_input("ํ๊ธ ์ง๋ฌธ์ ์ ์ด์ฃผ์ธ์ (Korean Text Query) :", value="์ํํธ") | |
| if st.button("์ง๋ฌธ (Query)"): | |
| proc = processor(text=[query], images=None, return_tensors="jax", padding=True) | |
| vec = np.asarray(model.get_text_features(**proc)) | |
| ids, dists = index.knnQuery(vec, k=10) | |
| result_files = map(lambda id: files[id], ids) | |
| result_imgs, result_captions = [], [] | |
| for file, dist in zip(result_files, dists): | |
| result_imgs.append(plt.imread(os.path.join(images_directory, file))) | |
| result_captions.append("{:s} (์ ์ฌ๋: {:.3f})".format(file, 1.0 - dist)) | |
| st.image(result_imgs[:3], caption=result_captions[:3], width=200) | |
| st.image(result_imgs[3:6], caption=result_captions[3:6], width=200) | |
| st.image(result_imgs[6:9], caption=result_captions[6:9], width=200) | |
| st.image(result_imgs[9:], caption=result_captions[9:], width=200) | |