mirror of
https://github.com/osmarks/meme-search-engine.git
synced 2024-11-10 22:09:54 +00:00
63 lines
2.1 KiB
Python
63 lines
2.1 KiB
Python
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import sklearn.decomposition
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import numpy as np
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import sqlite3
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import asyncio
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import aiohttp
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import base64
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meme_search_backend = "http://localhost:1707/"
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memes_url = "https://i.osmarks.net/memes-or-something/"
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meme_search_url = "https://mse.osmarks.net/?e="
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db = sqlite3.connect("/srv/mse/data.sqlite3")
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db.row_factory = sqlite3.Row
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def fetch_all_files():
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csr = db.execute("SELECT embedding FROM files WHERE embedding IS NOT NULL")
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x = [ np.frombuffer(row[0], dtype="float16").copy() for row in csr.fetchall() ]
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csr.close()
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return np.array(x)
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embeddings = fetch_all_files()
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print("loaded")
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pca = sklearn.decomposition.PCA()
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pca.fit(embeddings)
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print(pca.explained_variance_ratio_)
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print(pca.components_)
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def emb_url(embedding):
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return meme_search_url + base64.urlsafe_b64encode(embedding.astype(np.float16).tobytes()).decode("utf-8")
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async def get_exemplars():
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with open("components.html", "w") as f:
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f.write("""<!DOCTYPE html>
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<title>Embeddings PCA</title>
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<style>
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div img {
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width: 20%
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}
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</style>
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<body><h1>Embeddings PCA</h1>""")
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async with aiohttp.ClientSession():
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async def lookup(embedding):
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async with aiohttp.request("POST", meme_search_backend, json={
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"terms": [{ "embedding": list(float(x) for x in embedding) }], # sorry
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"k": 10
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}) as res:
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return (await res.json())["matches"]
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for i, (component, explained_variance_ratio) in enumerate(zip(pca.components_, pca.explained_variance_ratio_)):
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f.write(f"""
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<h2>Component {i}</h2>
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<h3>Explained variance {explained_variance_ratio*100:0.2}%</h3>
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<div>
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<h4><a href="{emb_url(component)}">Max</a></h4>
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""")
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for match in await lookup(component):
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f.write(f'<img loading="lazy" src="{memes_url+match[1]}">')
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f.write(f'<h4><a href="{emb_url(-component)}">Min</a></h4>')
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for match in await lookup(-component):
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f.write(f'<img loading="lazy" src="{memes_url+match[1]}">')
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f.write("</div>")
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asyncio.run(get_exemplars())
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