app.py

import torch
import numpy as np
import pandas as pd
import gradio as gr
from PIL import Image
from transformers import CLIPProcessor, CLIPModel

def find_similar(image):
    device = "cuda" if torch.cuda.is_available() else "cpu"
    
    ## Define model
    model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
    processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
    model = model.to(device)
    
    ## Load data
    photos = pd.read_csv("./photos.tsv000", sep='\t', header=0)
    photo_features = np.load("./features.npy")
    photo_ids = pd.read_csv("./photo_ids.csv")
    photo_ids = list(photo_ids['photo_id'])
    
    ## Inference
    with torch.no_grad():
        photo_preprocessed = processor(text=None, images=image, return_tensors="pt", padding=True)["pixel_values"]
        search_photo_feature = model.get_image_features(photos_preprocessed.to(device))
        search_photo_feature /= search_photo_feature.norm(dim=-1, keepdim=True)
    search_photos_feature = search_photos_feature.cpu().numpy()
    
    ## Find similarity
    similarities = list((search_photos_features @ photo_features.T).squeeze(0))
    
    ## Return best image :)
    best_photo = sorted(zip(similarities, range(photo_features.shape[0])), key=lambda x: x[0], reverse=True)[0]
    idx = best_photos[1]
    photo_id = photo_ids[idx]
    photo_data = photos[photos["photo_id"] == photo_id].iloc[0]

    return Image(url=photo_data["photo_image_url"] + "?w=640")
        

iface = gr.Interface(fn=bg_remove, inputs="image", outputs="image").launch()