import open_clip
import torch

do_load = True
if do_load:

    #model_name = 'hf-hub:timm/ViT-L-16-SigLIP2-512'
#    model, preprocess = open_clip.create_model_from_pretrained('hf-hub:timm/ViT-L-16-SigLIP2-512')


    model_name = 'ViT-SO400M-14-SigLIP-384'
    pretrained_name = 'webli'
    model, _, preprocess = open_clip.create_model_and_transforms(model_name, pretrained=pretrained_name)
    device = 'cpu'
    model.eval()
    tokenizer = open_clip.get_tokenizer(model_name)

from bottle import route, run, template, request, debug
@route('/encode')
def get_matches():
    query = request.query.get('query','A large bird eating corn')
    with torch.no_grad():
        text_tokenized = tokenizer(query)
        vec = model.encode_text(text_tokenized).detach().cpu().tolist()

    return {'vector':vec}



debug(True)
run(host='0.0.0.0', port=53004, server='bjoern')