import sklearn.decomposition
import numpy as np
import sqlite3
import asyncio
import aiohttp
import base64
meme_search_backend = "http://localhost:1707/"
memes_url = "https://i.osmarks.net/memes-or-something/"
meme_search_url = "https://mse.osmarks.net/?e="
db = sqlite3.connect("/srv/mse/data.sqlite3")
db.row_factory = sqlite3.Row
def fetch_all_files():
csr = db.execute("SELECT embedding FROM files WHERE embedding IS NOT NULL")
x = [ np.frombuffer(row[0], dtype="float16").copy() for row in csr.fetchall() ]
csr.close()
return np.array(x)
embeddings = fetch_all_files()
print("loaded")
pca = sklearn.decomposition.PCA()
pca.fit(embeddings)
print(pca.explained_variance_ratio_)
print(pca.components_)
def emb_url(embedding):
return meme_search_url + base64.urlsafe_b64encode(embedding.astype(np.float16).tobytes()).decode("utf-8")
async def get_exemplars():
with open("components.html", "w") as f:
f.write("""
Embeddings PCA
Embeddings PCA
""")
async with aiohttp.ClientSession():
async def lookup(embedding):
async with aiohttp.request("POST", meme_search_backend, json={
"terms": [{ "embedding": list(float(x) for x in embedding) }], # sorry
"k": 10
}) as res:
return (await res.json())["matches"]
for i, (component, explained_variance_ratio) in enumerate(zip(pca.components_, pca.explained_variance_ratio_)):
f.write(f"""