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| #Importing all the necessary libraries | |
| import requests | |
| import numpy as np | |
| import pandas as pd | |
| from io import BytesIO | |
| import pickle | |
| import gradio as gr | |
| from PIL import Image as PILIMAGE | |
| from IPython.display import Image | |
| from IPython.core.display import HTML | |
| from embedding_model import EmbeddingModel | |
| # Load model | |
| embed_model = EmbeddingModel() | |
| # Load csv info data | |
| imgs_info_df = pd.read_csv('15K Nocturna Valencia Banco Mediolanum_info.csv') | |
| # Load embeddings data | |
| with open('15K_Nocturna_Valencia_Banco_Mediolanum_embeddings.pkl', 'rb') as handle: | |
| embed_dict = pickle.load(handle) | |
| img_names_list = list(embed_dict.keys()) | |
| img_embeddings_np = np.array(list(embed_dict.values())) | |
| def find_best_matches(text_search, num_imgs=9): | |
| # Compute the similarity between the descrption and each photo using the Cosine similarity | |
| indexes = embed_model.get_similar_images_indexes(img_embeddings_np, text_search, n=num_imgs) | |
| target_img_filenames = [img_names_list[index] for index in indexes] | |
| target_urls = [] | |
| for img_name in target_img_filenames: | |
| item = imgs_info_df[imgs_info_df['filename'] == img_name.rsplit('.', 1)[0]] | |
| target_urls.append(item['url'].values[0]) | |
| matched_images = [] | |
| for i, url in enumerate(target_urls): | |
| # Display the images | |
| response = requests.get(url + "?w=640") | |
| img = PILIMAGE.open(BytesIO(response.content)) | |
| matched_images.append(img) | |
| return matched_images | |
| # gr.Interface(fn=find_best_matches, | |
| # inputs=[ | |
| # gr.inputs.Textbox(lines=1, label="Text query", placeholder="Introduce the search text...", | |
| # )], | |
| # theme="grass", | |
| # outputs=gr.outputs.Carousel([gr.outputs.Image(type="pil")]), enable_queue=True, title="CLIP Image Search", | |
| # description="This application displays TOP THREE images from Unsplash dataset that best match the search query provided by the user. Moreover, the input can be provided via two modes ie text or image form.").launch() | |
| iface = gr.Interface( | |
| title = "15K Nocturna valencia image search engine 📸", | |
| description = "Image search engine from a text input for the photo gallery of 15k Nocturna Valencia running race based on their semantic content.", | |
| article = "You find more information about this demo on my ✨ github repository [davertor](https://github.com/davertor/image-search-engine-for-flickr-photos-gallery)", | |
| fn=find_best_matches, | |
| inputs=[ | |
| gr.Textbox( | |
| lines=1, | |
| label="Write what you are looking for in an image...", | |
| placeholder="Text Here..."), | |
| gr.Slider(1, 9, step=1, value=3) | |
| ], | |
| outputs=gr.Gallery().style(grid=[1], height="auto"), | |
| examples=[ | |
| [("persons in the podium"), 3], | |
| [("a man with orange t-shirt"), 3], | |
| ] | |
| ).launch() |