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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()
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