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| import json | |
| import numpy as np | |
| import streamlit as st | |
| from st_clickable_images import clickable_images | |
| from clip_multilingual.search import MultiLingualSearch | |
| from clip_multilingual.models import Tokenizer | |
| def load_model(): | |
| unsplash_base_folder = './' | |
| all_embeddings = np.load(f'{unsplash_base_folder}/embeddings.npy') | |
| with open(f'{unsplash_base_folder}/urls.json') as f: | |
| all_urls = json.load(f) | |
| return MultiLingualSearch(all_embeddings, all_urls) | |
| semantic_search = load_model() | |
| description = ''' | |
| # Multilingual Semantic Search | |
| **Search images in any language powered by OpenAI's [CLIP](https://openai.com/blog/clip/) and XMLRoberta.** | |
| ''' | |
| st.sidebar.markdown(description) | |
| examples = [ | |
| 'pessoas sorrindo', | |
| '微笑的人', | |
| 'насмејани људи', | |
| ] | |
| for example in examples: | |
| if st.sidebar.button(example): | |
| st.session_state.query = example | |
| _, c, _ = st.columns((1, 3, 1)) | |
| if "query" in st.session_state: | |
| query = c.text_input("", value=st.session_state["query"]) | |
| else: | |
| query = c.text_input("", value="people smiling") | |
| if len(query) > 0: | |
| results = semantic_search.search(query) | |
| clicked = clickable_images( | |
| [result['image'] for result in results], | |
| titles=[f'Prob: {result["prob"]}' for result in results], | |
| div_style={ | |
| "display": "flex", | |
| "justify-content": "center", | |
| "flex-wrap": "wrap", | |
| }, | |
| img_style={"margin": "2px", "height": "200px"}, | |
| ) | |