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162175a 052f26d 162175a 052f26d 162175a 052f26d 40fca10 dfad1ba 40fca10 052f26d | 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 | import os
import numpy as np
import gradio as gr
from PIL import Image
from pathlib import Path
from config import settings
from utils.model_loading_util import load_model_from_hf
from src.processing.embedder import EmbeddingGenerator
from src.search.pinecone_indexer import PineconeImageSimilaritySearch
print("Initializing model and search index...")
model = load_model_from_hf(
repo_id=settings.model_repo_id,
device=settings.device,
token=settings.hf_token
)
generator = EmbeddingGenerator(
model=model,
device=settings.device,
batch_size=settings.batch_size,
num_workers=2,
layer_strategy="last_four_concat"
)
searcher = PineconeImageSimilaritySearch(
index_name=settings.pinecone_index_name,
api_key=settings.pinecone_api_key,
dimension=settings.embedding_dim,
metric="cosine",
)
def search_similar_images(input_img):
if input_img is None:
return None
# Convert Gradio input (can be numpy array) to PIL Image
if isinstance(input_img, np.ndarray):
input_img = Image.fromarray(input_img)
query_embedding = generator.generate_single_embedding(input_img)
results = searcher.search(query_embedding, k=5, return_scores=True)
gallery_items = []
for path, score in results:
img_path = os.path.join(settings.mount_path, path.lstrip("/"))
img_path = Path(img_path)
if img_path.exists():
gallery_items.append((str(img_path), f"Similarity: {score:.4f}"))
else:
print(f"Warning: Image path not found: {img_path}")
return gallery_items
custom_css = """
.container {
max-width: 1000px;
margin: auto;
padding: 20px;
}
.header {
text-align: center;
margin-bottom: 30px;
}
.header h1 {
font-size: 2.5rem;
font-weight: 800;
background: linear-gradient(90deg, #4F46E5, #EC4899);
-webkit-background-clip: text;
-webkit-text-fill-color: transparent;
margin-bottom: 10px;
}
.header p {
color: #6B7280;
font-size: 1.1rem;
}
.gradio-container {
background-color: #F9FAFB !important;
}
.gallery-container {
border-radius: 12px;
overflow: hidden;
box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1), 0 2px 4px -1px rgba(0, 0, 0, 0.06);
}
"""
with gr.Blocks() as demo:
with gr.Column(elem_classes="container"):
with gr.Column(elem_classes="header"):
gr.Markdown("# Meme Similarity Search")
gr.Markdown(
"Upload an image to find the top 5 most similar memes in our database.")
with gr.Row():
with gr.Column(scale=1):
input_image = gr.Image(
label="Upload Image",
type="pil",
elem_id="input-img"
)
search_btn = gr.Button("Find Similar Memes", variant="primary")
with gr.Column(scale=2):
output_gallery = gr.Gallery(
label="Top 5 Similar Memes",
show_label=True,
elem_id="gallery",
columns=2,
rows=3,
object_fit="contain",
height="600px"
)
search_btn.click(
fn=search_similar_images,
inputs=input_image,
outputs=output_gallery
)
input_image.upload(
fn=search_similar_images,
inputs=input_image,
outputs=output_gallery
)
if __name__ == "__main__":
demo.launch(
server_name="0.0.0.0",
server_port=7860,
css=custom_css,
allowed_paths=["/data"],
show_error=True,
debug=True
)
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