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+ "cells": [
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+ {
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+ "source": [
1740
+ "#@title 1. Install dependencies\n",
1741
+ "!pip install -q ftfy regex tqdm\n",
1742
+ "!pip install -q git+https://github.com/openai/CLIP.git\n",
1743
+ "!pip install -q scikit-learn matplotlib pillow umap-learn open_clip_torch huggingface_hub\n",
1744
+ "\n",
1745
+ "#@title 2. Mount Drive + set paths + HF token\n",
1746
+ "from google.colab import drive\n",
1747
+ "drive.mount('/content/drive')\n",
1748
+ "\n",
1749
+ "import os\n",
1750
+ "from pathlib import Path\n",
1751
+ "from google.colab import userdata\n",
1752
+ "\n",
1753
+ "# === USER SETTINGS ===\n",
1754
+ "CAPTIONS_ZIP = \"/content/drive/MyDrive/sm_captions.zip\" # change if needed\n",
1755
+ "TAGS_ZIP = \"/content/drive/MyDrive/sm_tags.zip\" # change if needed\n",
1756
+ "OUTPUT_ZIP = \"/content/drive/MyDrive/sm_merged.zip\" # where the result will be saved\n",
1757
+ "\n",
1758
+ "try:\n",
1759
+ " HF_TOKEN = userdata.get('HF_TOKEN')\n",
1760
+ "except Exception as e:\n",
1761
+ " print(\"Warning: Could not retrieve 'HF_TOKEN' from secrets. Setting to empty.\")\n",
1762
+ " HF_TOKEN = \"\"\n",
1763
+ "\n",
1764
+ "# Working directories\n",
1765
+ "WORK = Path(\"/content/work\")\n",
1766
+ "CAPTIONS_DIR = WORK / \"captions\"\n",
1767
+ "TAGS_DIR = WORK / \"tags\"\n",
1768
+ "MERGED_DIR = WORK / \"merged\"\n",
1769
+ "\n",
1770
+ "for d in [WORK, CAPTIONS_DIR, TAGS_DIR, MERGED_DIR]:\n",
1771
+ " d.mkdir(parents=True, exist_ok=True)\n",
1772
+ "\n",
1773
+ "#@title 3. Unzip the two archives\n",
1774
+ "import zipfile\n",
1775
+ "import shutil\n",
1776
+ "\n",
1777
+ "def unzip_safe(zip_path, dest):\n",
1778
+ " if dest.exists():\n",
1779
+ " shutil.rmtree(dest)\n",
1780
+ " dest.mkdir(parents=True)\n",
1781
+ " with zipfile.ZipFile(zip_path, 'r') as z:\n",
1782
+ " z.extractall(dest)\n",
1783
+ " print(f\"Unzipped {zip_path} → {dest}\")\n",
1784
+ "\n",
1785
+ "unzip_safe(CAPTIONS_ZIP, CAPTIONS_DIR)\n",
1786
+ "unzip_safe(TAGS_ZIP, TAGS_DIR)\n",
1787
+ "\n",
1788
+ "#@title 4. Load OpenCLIP model (with optional HF token)\n",
1789
+ "import torch\n",
1790
+ "import open_clip\n",
1791
+ "from PIL import Image\n",
1792
+ "import numpy as np\n",
1793
+ "from sklearn.metrics.pairwise import cosine_similarity\n",
1794
+ "from tqdm.auto import tqdm\n",
1795
+ "import glob\n",
1796
+ "\n",
1797
+ "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
1798
+ "print(f\"Using device: {device}\")\n",
1799
+ "\n",
1800
+ "model_name = \"ViT-B-32\"\n",
1801
+ "pretrained = \"laion400m_e32\"\n",
1802
+ "\n",
1803
+ "# Login to HF if token provided (needed for some gated models / private repos)\n",
1804
+ "if HF_TOKEN and HF_TOKEN.strip():\n",
1805
+ " from huggingface_hub import login\n",
1806
+ " login(token=HF_TOKEN.strip())\n",
1807
+ " print(\"Logged in to Hugging Face\")\n",
1808
+ "\n",
1809
+ "model, _, preprocess = open_clip.create_model_and_transforms(\n",
1810
+ " model_name,\n",
1811
+ " pretrained=pretrained,\n",
1812
+ " device=device\n",
1813
+ ")\n",
1814
+ "model.eval()\n",
1815
+ "print(f\"Loaded {model_name} / {pretrained}\")\n",
1816
+ "\n",
1817
+ "#@title 5. Helper functions\n",
1818
+ "IMAGE_EXTS = {'.jpg', '.jpeg', '.png', '.webp', '.bmp', '.tiff', '.tif', '.gif'}\n",
1819
+ "\n",
1820
+ "def get_image_files(folder: Path):\n",
1821
+ " files = []\n",
1822
+ " for p in folder.rglob(\"*\"):\n",
1823
+ " if p.suffix.lower() in IMAGE_EXTS:\n",
1824
+ " files.append(p)\n",
1825
+ " return sorted(files)\n",
1826
+ "\n",
1827
+ "def get_stem(path: Path):\n",
1828
+ " return path.stem # filename without extension\n",
1829
+ "\n",
1830
+ "@torch.no_grad()\n",
1831
+ "def extract_features(image_paths, batch_size=32):\n",
1832
+ " features = []\n",
1833
+ " valid_paths = []\n",
1834
+ "\n",
1835
+ " for i in tqdm(range(0, len(image_paths), batch_size), desc=\"Extracting features\"):\n",
1836
+ " batch_paths = image_paths[i:i+batch_size]\n",
1837
+ " batch_imgs = []\n",
1838
+ " batch_valid = []\n",
1839
+ "\n",
1840
+ " for p in batch_paths:\n",
1841
+ " try:\n",
1842
+ " img = Image.open(p).convert(\"RGB\")\n",
1843
+ " batch_imgs.append(preprocess(img))\n",
1844
+ " batch_valid.append(p)\n",
1845
+ " except Exception as e:\n",
1846
+ " print(f\"Skipping {p}: {e}\")\n",
1847
+ "\n",
1848
+ " if not batch_imgs:\n",
1849
+ " continue\n",
1850
+ "\n",
1851
+ " batch_tensor = torch.stack(batch_imgs).to(device)\n",
1852
+ " feats = model.encode_image(batch_tensor)\n",
1853
+ " feats = feats / feats.norm(dim=-1, keepdim=True) # L2 normalize\n",
1854
+ " features.append(feats.cpu().numpy())\n",
1855
+ " valid_paths.extend(batch_valid)\n",
1856
+ "\n",
1857
+ " if features:\n",
1858
+ " return np.vstack(features), valid_paths\n",
1859
+ " return np.array([]), []\n",
1860
+ "\n",
1861
+ "#@title 6. Extract features from both sets\n",
1862
+ "print(\"\\n--- Captions set ---\")\n",
1863
+ "cap_images = get_image_files(CAPTIONS_DIR)\n",
1864
+ "print(f\"Found {len(cap_images)} images\")\n",
1865
+ "cap_feats, cap_paths = extract_features(cap_images)\n",
1866
+ "\n",
1867
+ "print(\"\\n--- Tags set ---\")\n",
1868
+ "tag_images = get_image_files(TAGS_DIR)\n",
1869
+ "print(f\"Found {len(tag_images)} images\")\n",
1870
+ "tag_feats, tag_paths = extract_features(tag_images)\n",
1871
+ "\n",
1872
+ "if len(cap_feats) == 0 or len(tag_feats) == 0:\n",
1873
+ " raise RuntimeError(\"No valid images found in one of the sets!\")\n",
1874
+ "\n",
1875
+ "#@title 7. Match images by cosine similarity\n",
1876
+ "SIM_THRESHOLD = 0.98 #@param {type:\"number\"}\n",
1877
+ "\n",
1878
+ "print(f\"\\nMatching with cosine similarity ≥ {SIM_THRESHOLD} ...\")\n",
1879
+ "\n",
1880
+ "# Compute full similarity matrix\n",
1881
+ "sim_matrix = cosine_similarity(cap_feats, tag_feats)\n",
1882
+ "\n",
1883
+ "matches = [] # list of (cap_path, tag_path, similarity)\n",
1884
+ "used_tag_indices = set()\n",
1885
+ "\n",
1886
+ "for i, cap_path in enumerate(cap_paths):\n",
1887
+ " # Find best match that hasn't been used yet\n",
1888
+ " best_j = -1\n",
1889
+ " best_sim = -1.0\n",
1890
+ " for j in range(len(tag_paths)):\n",
1891
+ " if j in used_tag_indices:\n",
1892
+ " continue\n",
1893
+ " sim = sim_matrix[i, j]\n",
1894
+ " if sim > best_sim:\n",
1895
+ " best_sim = sim\n",
1896
+ " best_j = j\n",
1897
+ "\n",
1898
+ " if best_j != -1 and best_sim >= SIM_THRESHOLD:\n",
1899
+ " matches.append((cap_path, tag_paths[best_j], best_sim))\n",
1900
+ " used_tag_indices.add(best_j)\n",
1901
+ "\n",
1902
+ "print(f\"Found {len(matches)} high-confidence matches\")\n",
1903
+ "\n",
1904
+ "#@title 8. Merge captions + tags (new formatting rules) and build the new folder\n",
1905
+ "import shutil\n",
1906
+ "import random\n",
1907
+ "import re\n",
1908
+ "\n",
1909
+ "def read_text(path: Path):\n",
1910
+ " try:\n",
1911
+ " text = path.read_text(encoding=\"utf-8\")\n",
1912
+ " except:\n",
1913
+ " text = path.read_text(encoding=\"latin-1\")\n",
1914
+ " # Remove all newlines and extra whitespace\n",
1915
+ " return \" \".join(text.split())\n",
1916
+ "\n",
1917
+ "def format_tags(tags_raw: str) -> str:\n",
1918
+ " \"\"\"\n",
1919
+ " - Split on commas\n",
1920
+ " - Clean each tag\n",
1921
+ " - Group randomly into 3–5 tags\n",
1922
+ " - Join with ' , '\n",
1923
+ " - Capitalize first letter of each group\n",
1924
+ " - Add a period at the end of each group\n",
1925
+ " \"\"\"\n",
1926
+ " if not tags_raw.strip():\n",
1927
+ " return \"\"\n",
1928
+ "\n",
1929
+ " # Split and clean tags\n",
1930
+ " tags = [t.strip() for t in tags_raw.split(\",\") if t.strip()]\n",
1931
+ " if not tags:\n",
1932
+ " return \"\"\n",
1933
+ "\n",
1934
+ " formatted_groups = []\n",
1935
+ " i = 0\n",
1936
+ " while i < len(tags):\n",
1937
+ " # Random group size between 3 and 5 (or remaining tags)\n",
1938
+ " group_size = random.randint(3, 5)\n",
1939
+ " group = tags[i : i + group_size]\n",
1940
+ " i += group_size\n",
1941
+ "\n",
1942
+ " # Join with space-before-and-after comma\n",
1943
+ " group_str = \" , \".join(group)\n",
1944
+ "\n",
1945
+ " # Capitalize first letter and add period\n",
1946
+ " if group_str:\n",
1947
+ " group_str = group_str[0].upper() + group_str[1:] + \".\"\n",
1948
+ " formatted_groups.append(group_str)\n",
1949
+ "\n",
1950
+ " # Join all sentence-groups with a space\n",
1951
+ " return \" \".join(formatted_groups)\n",
1952
+ "\n",
1953
+ "# ---------- Main merging loop ----------\n",
1954
+ "merged_count = 0\n",
1955
+ "for cap_img, tag_img, sim in tqdm(matches, desc=\"Merging\"):\n",
1956
+ " stem = get_stem(cap_img)\n",
1957
+ "\n",
1958
+ " cap_txt = cap_img.with_suffix(\".txt\")\n",
1959
+ " tag_txt = tag_img.with_suffix(\".txt\")\n",
1960
+ "\n",
1961
+ " caption = read_text(cap_txt) if cap_txt.exists() else \"\"\n",
1962
+ " tags_raw = read_text(tag_txt) if tag_txt.exists() else \"\"\n",
1963
+ "\n",
1964
+ " # Format the tags according to the new rules\n",
1965
+ " tags_formatted = format_tags(tags_raw)\n",
1966
+ "\n",
1967
+ " # Merge caption + formatted tags\n",
1968
+ " if tags_formatted:\n",
1969
+ " merged_text = f\"{caption} {tags_formatted}\".strip() if caption else tags_formatted\n",
1970
+ " else:\n",
1971
+ " merged_text = caption\n",
1972
+ "\n",
1973
+ " # Final cleanup – remove any remaining newlines / excess spaces\n",
1974
+ " merged_text = \" \".join(merged_text.split())\n",
1975
+ "\n",
1976
+ " # Write to output\n",
1977
+ " out_img = MERGED_DIR / cap_img.name\n",
1978
+ " out_txt = MERGED_DIR / f\"{stem}.txt\"\n",
1979
+ "\n",
1980
+ " shutil.copy2(cap_img, out_img)\n",
1981
+ " out_txt.write_text(merged_text, encoding=\"utf-8\")\n",
1982
+ " merged_count += 1\n",
1983
+ "\n",
1984
+ "print(f\"\\nCreated {merged_count} image+text pairs in {MERGED_DIR}\")\n",
1985
+ "\n",
1986
+ "#@title 9. Create the final zip and save to Drive\n",
1987
+ "print(\"Creating zip archive...\")\n",
1988
+ "shutil.make_archive(\n",
1989
+ " str(WORK / \"sm_merged\"), # will create sm_merged.zip\n",
1990
+ " 'zip',\n",
1991
+ " root_dir=MERGED_DIR\n",
1992
+ ")\n",
1993
+ "\n",
1994
+ "# Move to Drive\n",
1995
+ "final_zip = WORK / \"sm_merged.zip\"\n",
1996
+ "shutil.copy2(final_zip, OUTPUT_ZIP)\n",
1997
+ "print(f\"\\n✅ Done! Merged archive saved to:\\n{OUTPUT_ZIP}\")\n",
1998
+ "print(f\"Total matched pairs: {merged_count}\")"
1999
+ ],
2000
+ "metadata": {
2001
+ "colab": {
2002
+ "base_uri": "https://localhost:8080/",
2003
+ "height": 683,
2004
+ "referenced_widgets": [
2005
+ "b1667878b7514558a790e5e6dc7d1ae6",
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+ "73763af04c84477a8b3f0043dc6a3d95",
2007
+ "0247c9bc01d042ef842cc1f0354eff3e",
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+ "dabdd62af2374715a1551ddd04f36bc2",
2009
+ "70ea2fffce724c5b8ce074de1eb8f53d",
2010
+ "ff372f09d39249d298c86dc9aa7c28e7",
2011
+ "e6c7bfabbb834d15833f06a17725606e",
2012
+ "e9a560a66736425b9e31367a93a17290",
2013
+ "24d374b6e1b8409aa9f5926074f0e7e4",
2014
+ "f1f68aa4a72f4ad88b51183eaf971216",
2015
+ "31875eca9c6442d6ab256b94c1243c95",
2016
+ "fb8e36a847ec45fb928ef5ad414430bc",
2017
+ "e4d5cd5d13ac406d94b942fa6807c59f",
2018
+ "8c93885a658e4323add8936a4dd291fb",
2019
+ "f9ad354ba8194f6c910b7490d467661b",
2020
+ "a645b84dd80c4559bc1aacfbd3d47fde",
2021
+ "f99325fdad954d708eca5d0f1b6ceb27",
2022
+ "c860d66ad46146649e46fc4bab48def5",
2023
+ "193679647714495dad3a316f8690c61b",
2024
+ "68f0a3d28d954d8db6e69fb463c247d2",
2025
+ "60ed0e21bfc74433b5a447a257e1d054",
2026
+ "f0e6cc4c14944795a38e6b6084df3c81",
2027
+ "4850956ebe5440e9afc6dc5c24f91355",
2028
+ "54b9ecde4e5c4361839fd9ead1f53768",
2029
+ "18a4d733abb842d3983ec231d815e441",
2030
+ "ae65acfce17d4e92a0ac857794280711",
2031
+ "611bac4ff2174242a4f83955580bce7d",
2032
+ "fc5b7f7226bd4c609eca9ad064e93960",
2033
+ "2a17046fd14e41e68151a43769160932",
2034
+ "f05a834fc16b438490322a73a8890042",
2035
+ "61aeab46c99744d8bb5e449860403f37",
2036
+ "b56e0be5842f4b5295cc415859f23005",
2037
+ "15bf2f5ad3e1480db5a4a2b0f7397f9b",
2038
+ "6f71f2ab042343d8bad7f5d91ce5b9a8",
2039
+ "6316959c854347f68af113d4df5d0f27",
2040
+ "9967f84f2a09409cbebcb69ab0e9d624",
2041
+ "4b303b1d92d34a6f86cef7f535143159",
2042
+ "8b10cbb663ea43fd89f46adbc92d52bf",
2043
+ "4ed2de1d7ec7414d9f26c10f3d8cb6e5",
2044
+ "ef47bf389e664325b8576a1b126bc4c6",
2045
+ "969bdda9b4d747f0973c9056a20f5954",
2046
+ "4a6708f7124944e882a9add110d4f322",
2047
+ "3ead8b58fdda4bdd8bf21fff60ad01c1",
2048
+ "ff21a2a26fbd41abb1fcac8a0c96f648",
2049
+ "4612ceff0cf347d6a35bfefc2b2abdf5",
2050
+ "2cb3230d8cd245ad8e85599932d99b3e",
2051
+ "45ed9d2e12c94e3d8f60265243bc94cd",
2052
+ "36d30b40013e486c90e86b507a622a6c",
2053
+ "480ae6c245ef4e29b4ac32f9791c1034",
2054
+ "c2a55d0767cd46019c605411fe94e837",
2055
+ "5cc994e5e71e4e8fbbe343373b7c67bf",
2056
+ "cc4e08908520477e992a0229e314d97d",
2057
+ "efe5d5f1b1004bd6874bea8375e055b6",
2058
+ "383db2a2ec024c4c8760238c8f4ec098",
2059
+ "7f5abcbe508a4611b347bd90f3a4f234"
2060
+ ]
2061
+ },
2062
+ "id": "O1_KXcSITT3n",
2063
+ "outputId": "fb62f48a-2484-4597-b805-d2ad1f10c6fe"
2064
+ },
2065
+ "execution_count": 1,
2066
+ "outputs": [
2067
+ {
2068
+ "output_type": "stream",
2069
+ "name": "stdout",
2070
+ "text": [
2071
+ " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
2072
+ " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
2073
+ " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
2074
+ " Building wheel for clip (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
2075
+ "Mounted at /content/drive\n",
2076
+ "Unzipped /content/drive/MyDrive/sm_captions.zip → /content/work/captions\n",
2077
+ "Unzipped /content/drive/MyDrive/sm_tags.zip → /content/work/tags\n",
2078
+ "Using device: cuda\n",
2079
+ "Logged in to Hugging Face\n"
2080
+ ]
2081
+ },
2082
+ {
2083
+ "output_type": "display_data",
2084
+ "data": {
2085
+ "text/plain": [
2086
+ "open_clip_model.safetensors: reconstructing file: 0%| | 0.00B / 605MB "
2087
+ ],
2088
+ "application/vnd.jupyter.widget-view+json": {
2089
+ "version_major": 2,
2090
+ "version_minor": 0,
2091
+ "model_id": "b1667878b7514558a790e5e6dc7d1ae6"
2092
+ }
2093
+ },
2094
+ "metadata": {}
2095
+ },
2096
+ {
2097
+ "output_type": "display_data",
2098
+ "data": {
2099
+ "text/plain": [
2100
+ "open_clip_model.safetensors: downloading bytes: | 0.00B "
2101
+ ],
2102
+ "application/vnd.jupyter.widget-view+json": {
2103
+ "version_major": 2,
2104
+ "version_minor": 0,
2105
+ "model_id": "fb8e36a847ec45fb928ef5ad414430bc"
2106
+ }
2107
+ },
2108
+ "metadata": {}
2109
+ },
2110
+ {
2111
+ "output_type": "stream",
2112
+ "name": "stderr",
2113
+ "text": [
2114
+ "/usr/local/lib/python3.13/dist-packages/open_clip/factory.py:450: UserWarning: QuickGELU mismatch between final model config (quick_gelu=False) and pretrained tag 'laion400m_e32' (quick_gelu=True).\n",
2115
+ " warnings.warn(\n"
2116
+ ]
2117
+ },
2118
+ {
2119
+ "output_type": "stream",
2120
+ "name": "stdout",
2121
+ "text": [
2122
+ "Loaded ViT-B-32 / laion400m_e32\n",
2123
+ "\n",
2124
+ "--- Captions set ---\n",
2125
+ "Found 50 images\n"
2126
+ ]
2127
+ },
2128
+ {
2129
+ "output_type": "display_data",
2130
+ "data": {
2131
+ "text/plain": [
2132
+ "Extracting features: 0%| | 0/2 [00:00<?, ?it/s]"
2133
+ ],
2134
+ "application/vnd.jupyter.widget-view+json": {
2135
+ "version_major": 2,
2136
+ "version_minor": 0,
2137
+ "model_id": "4850956ebe5440e9afc6dc5c24f91355"
2138
+ }
2139
+ },
2140
+ "metadata": {}
2141
+ },
2142
+ {
2143
+ "output_type": "stream",
2144
+ "name": "stdout",
2145
+ "text": [
2146
+ "\n",
2147
+ "--- Tags set ---\n",
2148
+ "Found 50 images\n"
2149
+ ]
2150
+ },
2151
+ {
2152
+ "output_type": "display_data",
2153
+ "data": {
2154
+ "text/plain": [
2155
+ "Extracting features: 0%| | 0/2 [00:00<?, ?it/s]"
2156
+ ],
2157
+ "application/vnd.jupyter.widget-view+json": {
2158
+ "version_major": 2,
2159
+ "version_minor": 0,
2160
+ "model_id": "6f71f2ab042343d8bad7f5d91ce5b9a8"
2161
+ }
2162
+ },
2163
+ "metadata": {}
2164
+ },
2165
+ {
2166
+ "output_type": "stream",
2167
+ "name": "stdout",
2168
+ "text": [
2169
+ "\n",
2170
+ "Matching with cosine similarity ≥ 0.98 ...\n",
2171
+ "Found 50 high-confidence matches\n"
2172
+ ]
2173
+ },
2174
+ {
2175
+ "output_type": "display_data",
2176
+ "data": {
2177
+ "text/plain": [
2178
+ "Merging: 0%| | 0/50 [00:00<?, ?it/s]"
2179
+ ],
2180
+ "application/vnd.jupyter.widget-view+json": {
2181
+ "version_major": 2,
2182
+ "version_minor": 0,
2183
+ "model_id": "4612ceff0cf347d6a35bfefc2b2abdf5"
2184
+ }
2185
+ },
2186
+ "metadata": {}
2187
+ },
2188
+ {
2189
+ "output_type": "stream",
2190
+ "name": "stdout",
2191
+ "text": [
2192
+ "\n",
2193
+ "Created 50 image+text pairs in /content/work/merged\n",
2194
+ "Creating zip archive...\n",
2195
+ "\n",
2196
+ "✅ Done! Merged archive saved to:\n",
2197
+ "/content/drive/MyDrive/sm_merged.zip\n",
2198
+ "Total matched pairs: 50\n"
2199
+ ]
2200
+ }
2201
+ ]
2202
+ }
2203
+ ]
2204
+ }