Use ONNX

main.py

@@ -2,7 +2,9 @@
 from os import PathLike, listdir, remove
 from os.path import isfile, join, exists
 from mimetypes import guess_type
-from base64 import b64encode
+from base64 import b64encode, b64decode
+from io import BytesIO
+import re
 
 import pandas as pd
 import numpy as np
@@ -10,7 +12,7 @@ from PIL import Image
 from PIL import ImageFile
 from tqdm import tqdm
 
-from uform import get_model
+from uform import get_model_onnx
 from usearch.index import Index, MetricKind
 from usearch.io import save_matrix, load_matrix
 
@@ -32,67 +34,82 @@ 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}'
+        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
 
 
 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'):
-    model = get_model('unum-cloud/uform-vl-multilingual')
+        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",
+    )
     vectors = []
 
-    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()
+    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)
         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.txt'):
+if not exists("images.base64.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.txt', 'w') as f:
-        f.write('\n'.join(datas))
+    with open("images.base64.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")