Spaces:
Sleeping
Sleeping
| import streamlit as st | |
| import pandas as pd, numpy as np | |
| import os | |
| from transformers import CLIPProcessor, CLIPTextModel, CLIPModel | |
| 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 = "<div style='margin-top: 20px; max-width: 1200px; display: flex; flex-wrap: wrap; justify-content: space-evenly'>" | |
| for url, title, link in url_list: | |
| html2 = f"<img title='{title}' style='height: {height}px; margin: 5px' src='{url}'>" | |
| if len(link) > 0: | |
| html2 = f"<a href='{link}' target='_blank'>" + html2 + "</a>" | |
| html = html + html2 | |
| html += "</div>" | |
| 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 | |
| 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(''' | |
| <style> | |
| .block-container{ | |
| max-width: 1200px; | |
| } | |
| stTextInput{ | |
| max-width: 600px; | |
| } | |
| #MainMenu { | |
| visibility: hidden; | |
| } | |
| footer { | |
| visibility: hidden; | |
| } | |
| </style>''', | |
| unsafe_allow_html=True) | |
| st.sidebar.markdown(description) | |
| _, col1, col2, _ = st.beta_columns([2, 10, 2, 2]) | |
| query = col1.text_input('') | |
| col2.text('') | |
| col2.text('') | |
| corpus = col2.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() | |