app.py

import gradio as gr
import numpy as np
from transformers import AutoFeatureExtractor, AutoModel
from datasets import load_dataset
from PIL import Image, ImageDraw
import os

    

# Load model for computing embeddings of the candidate images
print('Load model for computing embeddings of the candidate images')
model_ckpt = "google/vit-base-patch16-224"
extractor = AutoFeatureExtractor.from_pretrained(model_ckpt)
model = AutoModel.from_pretrained(model_ckpt)
hidden_dim = model.config.hidden_size


dataset_with_embeddings = load_dataset("LucyintheSky/24-1-8-ds-embeddings", split="train", token=os.environ.get('TOKEN'))
dataset_with_embeddings.add_faiss_index(column='embeddings')


def get_neighbors(query_image, top_k=8):
    qi_embedding = model(**extractor(query_image, return_tensors="pt"))
    qi_embedding = qi_embedding.last_hidden_state[:, 0].detach().numpy().squeeze()
    scores, retrieved_examples = dataset_with_embeddings.get_nearest_examples('embeddings', qi_embedding, k=top_k)
    return scores, retrieved_examples

    

def search(image_dict):
    
    query_image = Image.open(image_dict['composite']).convert(mode='RGB')

    print('search')
    scores, retrieved_examples = get_neighbors(query_image)

    print('return example')

    result = []
    for i in range(len(retrieved_examples["image"])):
        id = str(retrieved_examples["text"][i]) + ' ' + str(scores[i])
        print('id', id)
        #label = dataset_with_embeddings.features["label"].names[id]
        #print('label', label)
        result.append((retrieved_examples["image"][i], id))
    
    return result

iface = gr.Interface(fn=search,
                     description="""
                     <center><a href="https://www.lucyinthesky.com/"><img width="500" src="https://cdn.discordapp.com/attachments/1120417968032063538/1201666647157657640/LucyITS-2022-blk.png?ex=65caa646&is=65b83146&hm=09ad6fe279edc3a32981306d563e63af815d760fc0d8d0a3fbef4e4553c0a83a&"> </a> </center>
                     <center> Sketch to find your favorite Lucy in the Sky dress! </center>
                     """,
                     inputs=gr.ImageEditor(type='filepath', value={'background': './template.JPG', 'layers': None, 'composite': None}), 
                     outputs=gr.Gallery(label='Similar', object_fit='contain'),
                     theme = gr.themes.Base(primary_hue="teal",secondary_hue="teal",neutral_hue="slate"),)
iface.launch()