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| import gradio as gr | |
| import clip,torch | |
| import requests | |
| from PIL import Image | |
| import numpy as np | |
| import torch | |
| import torch.nn as nn | |
| from io import BytesIO | |
| import urllib.request | |
| # https://hhp-item-resource.s3.ap-northeast-2.amazonaws.com/magazine-resource/magazine/20221017154717/jin._s2.png | |
| # girl bag skirt eye beauty pretty | |
| from selenium import webdriver | |
| from selenium.webdriver.common.by import By | |
| def test2(): | |
| driver = webdriver.Chrome() #웹드라이버가 있는 경로에서 Chrome을 가져와 실행-> driver변수 | |
| driver.get('https://www.hiphoper.com/') #driver변수를 이용해 원하는 url 접속 | |
| imgs = driver.find_elements(By.CSS_SELECTOR,'img.card__image') #css selector를 이용해서 'tag이름.class명'의 순으로 인자를 전달 | |
| result = [] #웹 태그에서 attribute 중 src만 담을 리스트 | |
| for img in imgs: #모든 이미지들을 탐색 | |
| # print(img.get_attribute('src')) #이미지 주소를 print | |
| result.append(img.get_attribute('src')) #이미지 src만 모아서 리스트에 저장 | |
| driver.quit() | |
| return result | |
| def similarity(v1,v2,type=0): | |
| if type ==0: | |
| v1_norm = np.linalg.norm(v1) | |
| v2_norm = np.linalg.norm(v2) | |
| return np.dot(v1,v2)/(v1_norm*v2_norm) | |
| else: | |
| return np.sqrt(np.sum((v1-v2)**2)) | |
| def democlip(url ,texts): | |
| if url =='': | |
| print('SYSTEM : alternative url') | |
| url = 'https://i.pinimg.com/564x/47/b5/5d/47b55de6f168db65cf46d7d1f0451b64.jpg' | |
| else: | |
| print('SYSTEM : URL progressed') | |
| if texts =='': | |
| texts ='black desk room girl flower' | |
| else: | |
| print('SYSTEM : TEXT progressed') | |
| response = requests.get(url) | |
| image_bytes = response.content | |
| texts = list(texts.split(' ')) | |
| """Gets the embedding values for the image.""" | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| model, preprocess = clip.load("ViT-B/32", device=device) | |
| # image = preprocess(Image.open("CLIP.png")).unsqueeze(0).to(device)s | |
| text_token = clip.tokenize(texts).to(device) | |
| image = preprocess(Image.open(BytesIO(image_bytes))).unsqueeze(0).to(device) | |
| with torch.no_grad(): | |
| image_features = model.encode_image(image) | |
| text_features = model.encode_text(text_token) | |
| logits_per_image, logits_per_text = model(image,text_token) | |
| probs = logits_per_image.softmax(dim=-1).cpu().numpy() | |
| word_dict = {'image':{},'text':{}} | |
| ### text | |
| for i,text in enumerate(texts): | |
| word_dict['text'][text] = text_features[i].cpu().numpy() | |
| ### iamge | |
| for i,img in enumerate(image): | |
| word_dict['image'][img] = image_features[i].cpu().numpy() | |
| ###################### PCA of embeddings ######################## | |
| ## pca of text | |
| tu,ts,tv = torch.pca_lowrank(text_features,center=True) | |
| text_pca = torch.matmul(text_features,tv[:,:3]) | |
| ### pca of image | |
| imgu,imgs,imgv = torch.pca_lowrank(image_features,center=True) | |
| image_pca = torch.matmul(image_features,imgv[:,:3]) | |
| # return word_dict | |
| print(text_pca.shape,image_pca.shape) | |
| return text_pca,image_pca | |
| def PCA(img_emb, text_emb,n_components = 3): | |
| x = torch.tensor([[1.,2.,3.,7.],[4.,5.,3.,6.],[7.,9.,8.,9.],[11.,13.,17.,11.]]) | |
| # plz change data type to float or complex | |
| print(x.shape) | |
| u,s,v = torch.pca_lowrank(x,q=None, center=False,niter=2) | |
| u.shape,s.shape,v.shape | |
| u@torch.diag(s)@v.T | |
| # torch.matmul(x,v[:,:3]) | |
| pass | |
| # NODE type | |
| # PCA type. | |
| # ELSE type. | |
| demo = gr.Interface( | |
| fn=democlip, | |
| # inputs = [gr.Image(),gr.Textbox(lable='input prediction')], | |
| inputs = ['text',gr.Textbox(label='input prediction')], | |
| # outputs='label' | |
| outputs = [gr.Textbox(label='text pca Box'),gr.Textbox(label='image pca Box')] | |
| ) | |
| demo.launch() |