Multilingual index

.gitattributes

@@ -56,4 +56,3 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.usearch filter=lfs diff=lfs merge=lfs -text
 *.fbin filter=lfs diff=lfs merge=lfs -text
 images.txt filter=lfs diff=lfs merge=lfs -text
-images.base64.txt filter=lfs diff=lfs merge=lfs -text

images.uform-vl-english-small.fbin → images.fbin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:f36e6ec615d4ef679aaa46fae4da034d37d9814c1976ce6917c779a0724277ee
+oid sha256:6e673d5acf7f4a30f67b91d2cd3fcec73ed9b8891c61c22a3e4cda053ba8c7b6
 size 24875016

images.uform-vl-multilingual-v1.fbin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:055a9edbee6d33ac2bd0b402fda4c4db1af8f7a61f54632b4f0eafaefb76c7a7
-size 24875016

images.uform-vl-multilingual-v2.fbin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:20f6dc3d38f0dce81063a8102e18d906bfde4a125dd96f0a456c369291b7c820
-size 24875016

images.usearch

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:5f1733524594e565c6ad01dc3254c660a2b16e75e4ab7607addf5e4fc948b9ba
+oid sha256:238d84a238da044eabec8f9bd9a61ff5ab9963c45f5af81faea6deadeace7505
 size 28584936

images.xlm-roberta-base-ViT-B-32.fbin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:a840042e6b3e2eb74e9167a305846cdf3ba6016f77b72646549571b4fedec717
-size 49750024

main.py

@@ -2,9 +2,7 @@
 from os import PathLike, listdir, remove
 from os.path import isfile, join, exists
 from mimetypes import guess_type
-from base64 import b64encode, b64decode
-from io import BytesIO
-import re
+from base64 import b64encode
 
 import pandas as pd
 import numpy as np
@@ -12,7 +10,7 @@ from PIL import Image
 from PIL import ImageFile
 from tqdm import tqdm
 
-from uform import get_model_onnx
+from uform import get_model
 from usearch.index import Index, MetricKind
 from usearch.io import save_matrix, load_matrix
 
@@ -34,82 +32,67 @@ def image_to_data(path: PathLike) -> str:
     if not exists(path):
         raise FileNotFoundError
     mime, _ = guess_type(path)
-    with open(path, "rb") as fp:
+    with open(path, 'rb') as fp:
         data = fp.read()
-        data64 = b64encode(data).decode("utf-8")
-        return f"data:{mime}/jpg;base64,{data64}"
-
-
-def data_to_image(data_uri: str) -> Image:
-    """Convert a base64-encoded data URI to a Pillow Image."""
-    base64_str = re.search(r"base64,(.*)", data_uri).group(1)
-    image_data = b64decode(base64_str)
-    image = Image.open(BytesIO(image_data))
-    return image
+        data64 = b64encode(data).decode('utf-8')
+        return f'data:{mime}/jpg;base64,{data64}'
 
 
 def trim_extension(filename: str) -> str:
-    return filename.rsplit(".", 1)[0]
+    return filename.rsplit('.', 1)[0]
 
 
-names = sorted(f for f in listdir("images") if is_image(join("images", f)))
+names = sorted(f for f in listdir('images') if is_image(join('images', f)))
 names = [trim_extension(f) for f in names]
 
-table = (
-    pd.read_table("images.tsv") if exists("images.tsv") else pd.read_table("images.csv")
-)
-table = table[table["photo_id"].isin(names)]
-table = table.sort_values("photo_id")
+table = pd.read_table('images.tsv') if exists(
+    'images.tsv') else pd.read_table('images.csv')
+table = table[table['photo_id'].isin(names)]
+table = table.sort_values('photo_id')
 table.reset_index()
-table.to_csv("images.csv", index=False)
+table.to_csv('images.csv', index=False)
 
-names = list(set(table["photo_id"]).intersection(names))
-names_to_delete = [f for f in listdir("images") if trim_extension(f) not in names]
-names = list(table["photo_id"])
+names = list(set(table['photo_id']).intersection(names))
+names_to_delete = [f for f in listdir(
+    'images') if trim_extension(f) not in names]
+names = list(table['photo_id'])
 
 if len(names_to_delete) > 0:
-    print(f"Plans to delete: {len(names_to_delete)} images without metadata")
+    print(f'Plans to delete: {len(names_to_delete)} images without metadata')
     for name in names_to_delete:
-        remove(join("images", name))
-
-if not exists("images.fbin") and 0:
-    model, processor = get_model_onnx(
-        "unum-cloud/uform-vl-english-small",
-        device="cpu",
-        dtype="fp32",
-    )
+        remove(join('images', name))
+
+if not exists('images.fbin'):
+    model = get_model('unum-cloud/uform-vl-english')
     vectors = []
 
-    for name in tqdm(names, desc="Vectorizing images"):
-        image = Image.open(join("images", name + ".jpg"))
-        image_data = processor.preprocess_image(image)
-        image_embedding = model.encode_image(image_data)
+    for name in tqdm(names, desc='Vectorizing images'):
+        image = Image.open(join('images', name + '.jpg'))
+        image_data = model.preprocess_image(image)
+        image_embedding = model.encode_image(image_data).detach().numpy()
         vectors.append(image_embedding)
 
     image_mat = np.vstack(vectors)
-    save_matrix(image_mat, "images.fbin")
+    save_matrix(image_mat, 'images.fbin')
 
-if not exists("images.base64.txt"):
+if not exists('images.txt'):
 
     datas = []
-    for name in tqdm(names, desc="Encoding images"):
-        data = image_to_data(join("images", name + ".jpg"))
+    for name in tqdm(names, desc='Encoding images'):
+        data = image_to_data(join('images', name + '.jpg'))
         datas.append(data)
 
-    with open("images.base64.txt", "w") as f:
-        f.write("\n".join(datas))
+    with open('images.txt', 'w') as f:
+        f.write('\n'.join(datas))
 
-if not exists("images.names.txt"):
-    with open("images.names.txt", "w") as f:
-        f.write("\n".join(names))
 
-if not exists("images.usearch"):
-    image_mat = load_matrix("images.fbin")
+if not exists('images.usearch'):
+    image_mat = load_matrix('images.fbin')
     count = image_mat.shape[0]
     ndim = image_mat.shape[1]
     index = Index(ndim=ndim, metric=MetricKind.Cos)
 
-    for idx in tqdm(range(count), desc="Indexing vectors"):
+    for idx in tqdm(range(count), desc='Indexing vectors'):
         index.add(idx, image_mat[idx, :].flatten())
 
-    index.save("images.usearch")
+    index.save('images.usearch')