import streamlit as st import pandas as pd, numpy as np import os from transformers import CLIPProcessor, CLIPTextModel, CLIPModel @st.cache(show_spinner=False, hash_funcs={CLIPModel: lambda _: None, CLIPTextModel: lambda _: None, CLIPProcessor: lambda _: None, dict: lambda _: None}) def load(): model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") text_model = CLIPTextModel.from_pretrained("openai/clip-vit-base-patch32") processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") df = {0: pd.read_csv('data.csv'), 1: pd.read_csv('data2.csv')} embeddings = {0: np.load('embeddings.npy'), 1: np.load('embeddings2.npy')} for k in [0, 1]: embeddings[k] = np.divide(embeddings[k], np.sqrt(np.sum(embeddings[k]**2, axis=1, keepdims=True))) return model, text_model, processor, df, embeddings model, text_model, processor, df, embeddings = load() source = {0: '\nSource: Unsplash', 1: '\nSource: The Movie Database (TMDB)'} def get_html(url_list, height=200): html = "
" for url, title, link in url_list: html2 = f"" if len(link) > 0: html2 = f"" + html2 + "" html = html + html2 html += "
" return html def compute_text_embeddings(list_of_strings): inputs = processor(text=list_of_strings, return_tensors="pt", padding=True) return model.text_projection(text_model(**inputs).pooler_output) st.cache(show_spinner=False) def image_search(query, corpus, n_results=24): text_embeddings = compute_text_embeddings([query]).detach().numpy() k = 0 if corpus == 'Unsplash' else 1 results = np.argsort((embeddings[k]@text_embeddings.T)[:, 0])[-1:-n_results-1:-1] return [(df[k].iloc[i]['path'], df[k].iloc[i]['tooltip'] + source[k], df[k].iloc[i]['link']) for i in results] description = ''' # Semantic image search **Enter your query and hit enter** *Built with OpenAI's [CLIP](https://openai.com/blog/clip/) model, 🤗 Hugging Face's [transformers library](https://huggingface.co/transformers/), [Streamlit](https://streamlit.io/) and images from [Unsplash](https://unsplash.com/) and [The Movie Database (TMDB)](https://www.themoviedb.org/)* ''' def main(): st.markdown(''' ''', unsafe_allow_html=True) st.sidebar.markdown(description) _, c, _ = st.beta_columns((1, 3, 1)) query = c.text_input('') corpus = st.radio('', ["Unsplash","Movies"]) if len(query) > 0: results = image_search(query, corpus) st.markdown(get_html(results), unsafe_allow_html=True) if __name__ == '__main__': main()