Spaces:
Running
Running
File size: 5,023 Bytes
7262628 6afda1a 7262628 6afda1a 7262628 6afda1a 7262628 6afda1a 7262628 bf68054 6afda1a 7262628 6afda1a 7262628 6afda1a 7262628 71e2e6b 7262628 71e2e6b 7262628 6afda1a 7262628 6afda1a 7262628 6afda1a 7262628 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 |
import pinecone
# init connection to pinecone
pinecone.init(
api_key="0898750a-ee05-44f1-ac8a-98c5fef92f4a", # app.pinecone.io
environment="asia-southeast1-gcp-free" # find next to api key
)
index_name = "hybrid-image-search"
index = pinecone.GRPCIndex(index_name)
from datasets import load_dataset
# load the dataset from huggingface datasets hub
fashion = load_dataset(
"ashraq/fashion-product-images-small",
split='train[:1000]'
)
images = fashion["image"]
metadata = fashion.remove_columns("image")
import requests
with open('pinecone_text.py' ,'w') as fb:
fb.write(requests.get('https://storage.googleapis.com/gareth-pinecone-datasets/pinecone_text.py').text)
from transformers import BertTokenizerFast
import pinecone_text
# load bert tokenizer from huggingface
tokenizer = BertTokenizerFast.from_pretrained(
'bert-base-uncased'
)
def tokenize_func(text):
token_ids = tokenizer(
text,
add_special_tokens=False
)['input_ids']
return tokenizer.convert_ids_to_tokens(token_ids)
bm25 = pinecone_text.BM25(tokenize_func)
bm25.fit(metadata['productDisplayName'])
from sentence_transformers import SentenceTransformer
import transformers.models.clip.image_processing_clip
import torch
device = 'cuda' if torch.cuda.is_available() else 'cpu'
# load a CLIP model from huggingface
model = SentenceTransformer(
'sentence-transformers/clip-ViT-B-32',
device=device
)
def hybrid_scale(dense, sparse, alpha: float):
if alpha < 0 or alpha > 1:
raise ValueError("Alpha must be between 0 and 1")
# scale sparse and dense vectors to create hybrid search vecs
hsparse = {
'indices': sparse['indices'],
'values': [v * (1 - alpha) for v in sparse['values']]
}
hdense = [v * alpha for v in dense]
return hdense, hsparse
def text_to_image(query, alpha, k_results):
sparse = bm25.transform_query(query)
dense = model.encode(query).tolist()
# scale sparse and dense vectors
hdense, hsparse = hybrid_scale(dense, sparse, alpha=alpha)
# search
result = index.query(
top_k=k_results,
vector=hdense,
sparse_vector=hsparse,
include_metadata=True
)
# used returned product ids to get images
imgs = [images[int(r["id"])] for r in result["matches"]]
description = []
for x in result["matches"]:
description.append( x["metadata"]['productDisplayName'] )
return imgs, description
counter = {"dir_num": 1}
img_files = {'x':[]}
def img_to_file_list(imgs):
path = "searches"
sub_path = './' + path + '/' + 'search' + '_' + str(counter["dir_num"])
# Check whether the specified path exists or not
isExist = os.path.exists('.'+'/'+path)
if not isExist:
print("Directory does not exists")
# Create a new directory because it does not exist
os.makedirs('.'+'/'+path, exist_ok = True)
print("The new directory is created!")
# Check whether the specified path exists or not
isExist = os.path.exists(sub_path)
if isExist:
shutil.rmtree(sub_path)
os.makedirs(sub_path, exist_ok = True)
img_files = {'search'+str(counter["dir_num"]):[]}
i = 0
for img in imgs:
img.save(sub_path+"/img_" + str(i) + ".png","PNG")
img_files['search'+str(counter["dir_num"])].append(sub_path + '/' + 'img_'+ str(i) + ".png")
i+=1
counter["dir_num"]+=1
return img_files['search'+str(counter["dir_num"]-1)]
import gradio as gr
from deep_translator import GoogleTranslator
counter = {"dir_num": 1}
img_files = {'x':[]}
def fake_gan(text, alpha):
text_eng=GoogleTranslator(source='iw', target='en').translate(text)
imgs, descr = text_to_image(text_eng, alpha, 3)
img_files = img_to_file_list(imgs)
return img_files
def fake_text(text, alpha):
en_text = GoogleTranslator(source='iw', target='en').translate(text)
img , descr = text_to_image(en_text, alpha, 3)
return descr
with gr.Blocks() as demo:
with gr.Row():
text = gr.Textbox(
value = "ื'ืื ืก ืืืื ืืืืจืื",
label="Enter the product characteristics:",
)
alpha = gr.Slider(0, 1, step=0.01, label='Choose alpha:', value = 0.05)
with gr.Row():
btn = gr.Button("Generate image")
with gr.Row():
gallery = gr.Gallery(
label="Generated images", show_label=False, elem_id="gallery"
).style(columns=[8], rows=[2], object_fit='scale-down', height='auto')
with gr.Row():
selected = gr.Textbox(label="Product description: ", interactive=False, value = "-----> Description <-------",placeholder="Selected")
btn.click(fake_gan, inputs=[text, alpha], outputs=gallery)
def get_select_index(evt: gr.SelectData,text,alpha):
print(evt.index)
eng_text = fake_text(text, alpha)[evt.index]
#heb_text = GoogleTranslator(source='en', target='iw').translate(eng_text)
return eng_text
gallery.select( fn=get_select_index, inputs=[text,alpha], outputs=selected )
demo.launch()
|