Datasets:

kavyabhand
/

spacerot

{
 "space": "CVPR/Text2Human",
 "likes": 46,
 "hf_exception": "ModuleNotFoundError: No module named 'huggingface_hub.snapshot_download'",
 "revived": true,
 "tier": "api",
 "gpu": false,
 "attempts": [
  {
   "effort": "medium",
   "turns": [],
   "raw": "{\"diagnosis\": \"The rebuild fails because sentence-transformers 2.2.0 imports removed/changed huggingface_hub APIs; the decisive error is `ImportError: cannot import name 'cached_download' from 'huggingface_hub'` (and the related `from huggingface_hub.snapshot_download import REPO_ID_SEPARATOR`). The installed huggingface_hub 0.36.2 is too new for sentence-transformers 2.2.0.\", \"category\": \"hf-hub-api-removed\", \"verdict\": \"fixable\", \"edits\": [{\"path\": \"requirements.txt\", \"search\": \"sentence-transformers==2.2.0\\n\", \"replace\": \"sentence-transformers==2.2.0\\nhuggingface_hub==0.11.1\\n\"}], \"expected_effect\": \"On rebuild, huggingface_hub is pinned to a version compatible with sentence-transformers 2.2.0, so `cached_download` and `snapshot_download.REPO_ID_SEPARATOR` are available and the app can import sentence_transformers successfully.\"}",
   "reasoning": "We need solve. Need respond JSON. Need analyze problem. Space rebuild fails because sentence-transformers 2.2.0 imports cached_download from huggingface_hub 0.36.2, removed. Need fix. Could pin huggingface_hub<1.0? But package versions? Existing resolved huggingface_hub 0.36.2 (>=1? Actually 0.36.2 is <1.0, but cached_download removed maybe in 0.19? Let's recall huggingface_hub: cached_download deprecated then removed in 0.19? Actually 0.19? In 0.19 cached_download removed? Migration notes: 1.0 removed cached_download, but log says cannot import name 'cached_download' from huggingface_hub 0.36.2. Wait huggingface_hub 0.36.2 is current 0.x? It seems maybe cached_download removed earlier in 0.19? Let's check: huggingface_hub 0.19.0 removed cached_download? The migration note says 1.0 removed cached_download, but maybe incorrect? Actually huggingface_hub version 0.36.2 definitely no cached_download. sentence-transformers 2.2.0 expects old huggingface_hub. Need either upgrade sentence-transformers or pin huggingface_hub to old version containing cached_download. But other deps? Let's inspect. Requirements has sentence-transformers==2.2.0. It imports all-MiniLM-L6-v2. Could upgrade sentence-transformers to version compatible with huggingface_hub 0.36.2 and transformers 4.19.2? But sentence-transformers 2.2.0 requires transformers>=4.6? and huggingface_hub>=0.4.0. Newer sentence-transformers may require newer transformers, conflicting. Could pin huggingface_hub to version old enough with cached_download but new enough compatible. Need determine exact. huggingface_hub 0.16? cached_download existed until 0.19? Let's recall.\n\nhuggingface_hub changelog:\n- 0.11? \n- 0.12?\n- 0.13 maybe cached_download deprecated? \n- 0.14? \n- 0.16.4?\n- 0.17.0? removed? \nActually in huggingface_hub, `cached_download` was deprecated in 0.13? Removed in 0.19? Let's memory: In 0.19.0 \"Remove deprecated cached_download\" maybe yes. But sentence-transformers 2.2.0 released Sep 2022, huggingface_hub maybe 0.9. Need pin huggingface_hub<0.19? But installed gradio 3.0.17 maybe requires huggingface_hub? Gradio 3.0.17 likely requires huggingface_hub>=0.13? Let's check. Could pin huggingface_hub==0.16.4? But gradio maybe needs >=0.13? likely okay. However rebuild environment includes torch 1.11 etc. Need avoid dependency conflicts. Could add huggingface_hub pin to requirements. But requirements currently no huggingface_hub. The pip freeze shows 0.36.2. We can pin huggingface_hub to older version. But is there a version compatible with gradio 3.0.17 and sentence-transformers 2.2.0? Let's investigate mentally.\n\nAlternative code edit: avoid sentence-transformers entirely? But functionality uses SentenceTransformer('all-MiniLM-L6-v2') and util.dot_score. Could implement using transformers directly? But smallest change? Could upgrade sentence-transformers to a version compatible with huggingface_hub 0.36.2, but then transformers conflict. Let's check sentence-transformers versions:\n- 2.2.0 (Oct 2022) supports transformers 4.6-4.25? huggingface_hub>=0.4.0\n- 2.2.2 (Dec 2022)\n- 2.3.0 (Jul 2023?) requires transformers>=4.6.0, huggingface_hub>=0.11.0\n- 2.5.1 maybe requires transformers>=4.34? \n- 2.6? \n- 2.7? \n- 3.0? \nNewer sentence-transformers likely remove cached_download usage and support huggingface_hub 0.36. But may require newer transformers, potentially incompatible with code? sentence-transformers itself just encoding; transformers 4.19.2 maybe too old for newer. Could pin sentence-transformers to 2.7.0 and transformers 4.38? But app's transformers used elsewhere? Model code uses transformers? Let's grep maybe no direct transformers except requirements. Models maybe no. Could bump transformers. But risk.\n\nAnother option: pin huggingface_hub to older version with cached_download. This keeps all original pins. Need ensure old huggingface_hub works with gradio 3.0.17 and sentence-transformers 2.2.0. Let's identify version. huggingface_hub 0.16.4 is around July 2023? Wait sentence-transformers 2.2.0 Oct 2022; huggingface_hub 0.10? Let's timeline:\n- huggingface_hub 0.4.0 Feb 2022\n- 0.5.1 Apr 2022\n- 0.7 May 2022\n- 0.8 Jun 2022\n- 0.9 Aug 2022\n- 0.10 Sep 2022\n- 0.11 Oct 2022\n- 0.12 Dec 2022\n- 0.13 Feb 2023\n- 0.14 Apr 2023\n- 0.15 May 2023\n- 0.16 Jun 2023\n- 0.17 Aug 2023\n- 0.18 Sep 2023\n- 0.19 Oct 2023\n- 0.20 Dec 2023\n- ...\ncached_download removed in 0.19? Let's verify from memory: huggingface_hub 0.19.0 release notes: \"Remove deprecated `cached_download` function\" likely. So pin `huggingface_hub<0.19` or `==0.18.0`. But gradio 3.0.17 (Aug 2022) maybe requires huggingface_hub>=0.13? Actually gradio 3.0.17 might require huggingface-hub>=0.13? Let's check typical gradio 3.0 requirements: `huggingface-hub>=0.13.0`? It may be okay with 0.18. But Spaces installs gradio 3.0.17 after requirements; if huggingface_hub pinned 0.18, gradio may upgrade? pip install gradio could upgrade huggingface_hub to latest if not constrained? Since requirements lock? In Docker build, requirements first, then gradio install. Pip may upgrade huggingface_hub to satisfy gradio's constraint if gradio requires newer. But if requirements pin huggingface_hub==0.18.0, pip might still upgrade if gradio requires >0.18? Need check. Gradio 3.0.17 likely requires `huggingface-hub>=0.13.0` not >0.18, so pin remains. But gradio's transitive maybe `huggingface-hub>=0.13,<1.0`? likely okay.\n\nHowever there is another issue: sentence-transformers 2.2.0 may require `huggingface-hub>=0.4.0` and `torch>=1.6`. Fine.\n\nBut the rebuild log shows rebuild uses huggingface_hub 0.36.2 because not pinned. Adding pin should fix. Need maybe also remove? The task says prefer pinning versions. So edit requirements.txt to add `huggingface_hub==0.18.0` (or `<0.19`). Which exact version safest? Need choose a version that still has cached_download and supports Python 3.8. huggingface_hub 0.18.0 supports py3.8? likely yes. Could use 0.16.4 to be safe? Let's think compatibility with gradio 3.0.17. Gradio 3.0.17 might import `HfFolder` etc. huggingface_hub 0.16 has them. sentence-transformers 2.2.0 uses `HfFolder`, `cached_download`, `hf_hub_url`? In log first error line 407 `from huggingface_hub.snapshot_download import REPO_ID_SEPARATOR` in huggingface_hub 0.36? Actually that's new huggingface_hub util trying import from snapshot_download; old huggingface_hub wouldn't have that path? Wait error in rebuild first run: `from huggingface_hub.snapshot_download import REPO_ID_SEPARATOR` ModuleNotFoundError. That's installed sentence-transformers 2.2.0's util.py? Let's parse: File `/home/user/.local/lib/python3.8/site-packages/sentence_transformers/util.py`, line 407, `from huggingface_hub.snapshot_download import REPO_ID_SEPARATOR`. This is sentence-transformers 2.2.0 code expecting huggingface_hub API where `snapshot_download` module exports `REPO_ID_SEPARATOR`. In huggingface_hub 0.36.2, `snapshot_download` may be in `huggingface_hub` not submodule? Actually in newer versions, `snapshot_download` moved? Let's check. In huggingface_hub 0.36, `from huggingface_hub import snapshot_download` works, but `huggingface_hub.snapshot_download` module? Maybe not. In older huggingface_hub, there is submodule `huggingface_hub.snapshot_download` with `REPO_ID_SEPARATOR`. So pinning to older version where submodule exists is needed. Which version? Need find. `REPO_ID_SEPARATOR` likely introduced around 0.10? Let's recall. Sentence-transformers 2.2.0 expects huggingface_hub with `snapshot_download` module. In huggingface_hub 0.10? There is `huggingface_hub.snapshot_download` module. So pin to 0.10.x maybe? But gradio 3.0.17 may require >=0.13. Hmm.\n\nLet's inspect actual errors:\nFirst environment (original) huggingface_hub version unknown? pip freeze not shown for original? Rebuild freeze shows 0.36.2. Original error: `from huggingface_hub.snapshot_download import REPO_ID_SEPARATOR` ModuleNotFoundError. This is because sentence-transformers 2.2.0's util.py assumes huggingface_hub has snapshot_download submodule. In huggingface_hub 0.36, maybe snapshot_download is a function in package, not submodule, so import fails. The rebuild second error changes to `cannot import name 'cached_download'` from huggingface_hub (because sentence-transformers SentenceTransformer.py imports cached_download). Wait why different? In first run, maybe sentence-transformers version? Actually both same 2.2.0. First failure happens later in util.py after SentenceTransformer import? Let's read: first log:\n```\nfrom sentence_transformers import SentenceTransformer, util\n...\nFile .../sentence_transformers/SentenceTransformer.py line 25 from .evaluation...\n...\nFile .../sentence_transformers/util.py line 407 from huggingface_hub.snapshot_download import REPO_ID_SEPARATOR\nModuleNotFoundError\n```\nSecond rebuild log:\n```\nFile .../sentence_transformers/SentenceTransformer.py line 12 from huggingface_hub import HfApi, HfFolder, Repository, hf_hub_url, cached_download\nImportError: cannot import name 'cached_download'\n```\nDifference because huggingface_hub version? Both 0.36.2? First maybe `.local` different? But likely same. Wait first error line 407 snapshot_download import; second line 12 cached_download. Could be sentence-transformers version difference? No.\n\nMaybe because huggingface_hub 0.36.2 has both? Let's not overthink. Need choose pin that contains both `cached_download` and `snapshot_download.REPO_ID_SEPARATOR`. These are older APIs. Let's find version where both exist. `cached_download` removed in 0.19? `REPO_ID_SEPARATOR` maybe in snapshot_download module until? Let's search memory.\n\nIn huggingface_hub source:\n- `huggingface_hub/snapshot_download.py` exists in 0.10? It defines `REPO_ID_SEPARATOR`? \n- `cached_download` exists in `huggingface_hub/file_download.py` until 0.19.\nSentence-transformers 2.2.0 likely tested with huggingface_hub 0.10 or 0.11. Let's pin `huggingface_hub==0.10.1`? But gradio 3.0.17 may require >=? Let's check gradio 3.0.17 requirements. I can infer from pip: gradio 3.0.17 depends on `huggingface-hub>=0.13.0`? Actually gradio 3.1? Let's recall gradio 3.0 requirements from PyPI:\n```\nhuggingface-hub>=0.13.0\n```\nMaybe yes. If so huggingface_hub 0.10 would conflict with gradio. But Spaces installs gradio after requirements; pip might upgrade huggingface_hub to 0.36 despite pin? If requirements has `huggingface_hub==0.10.1`, then gradio install `pip install gradio==3.0.17` will see conflict and upgrade huggingface_hub to satisfy gradio, breaking. Unless gradio's constraint is lower. Need check actual. Could use `huggingface_hub==0.13.0` or 0.14, which has cached_download and snapshot_download? Need determine.\n\nLet's reason from gradio. Gradio 3.0.17 released 2022-09? Hugging Face hub 0.13 released Feb 2023, so gradio 3.0.17 cannot require 0.13 if released earlier. Actually gradio 3.0.17 release date? Gradio 3.0 series around Aug/Sep 2022. So likely requires `huggingface-hub>=0.8` or `>=0.9`. Thus huggingface_hub 0.10/0.11 okay. But rebuild installs gradio 3.0.17 after requirements; if pin 0.10, gradio may allow. Good.\n\nNeed pick version with `REPO_ID_SEPARATOR`. Let's search memory of huggingface_hub code. In version 0.10.1, `huggingface_hub/snapshot_download.py`:\n```\nREPO_ID_SEPARATOR = \"--\"\n...\ndef snapshot_download(...):\n...\n```\nLikely yes. `cached_download` in file_download. So pin `huggingface_hub==0.10.1` maybe. But sentence-transformers 2.2.0 may require `huggingface-hub>=0.4.0`, okay. gradio 3.0.17 may require `>=0.8` or `>=0.9`; 0.10 okay. Good.\n\nBut there could be other incompatibilities: `httpx` etc. But not asked? We need minimal fix to make boot. Pinning huggingface_hub older should resolve. Let's decide exact version. Maybe `0.11.1`? Need ensure REPO_ID_SEPARATOR exists. Let's see timeline: snapshot_download module introduced in 0.8? `REPO_ID_SEPARATOR` maybe introduced later. Sentence-transformers 2.2.0 code references it, so the version it was developed against likely had it. Let's pin to `0.11.1`? Actually sentence-transformers 2.2.0 released 2022-10-18. huggingface_hub 0.10.1 released 2022-09-20; 0.11.0 2022-10-? Let's pick 0.11.1 maybe. Does 0.11 have cached_download? yes. snapshot_download module? yes. Gradio 3.0.17? likely okay.\n\nBut if gradio requires >=0.13, then pin 0.11 conflict. Let's check gradio 3.0.17 dependencies more accurately. I can approximate from known: Gradio 3.0.2 requires `huggingface-hub>=0.13.0`? Wait gradio 3.0.2 released Aug 2022? Hugging Face hub 0.13 not until Feb 2023, impossible. So no. Gradio 3.2 maybe requires >=0.14? Hmm. Gradio 3.0.x likely requires `huggingface-hub` without strict. Let's not need exact; pip will resolve.\n\nCould use constraint `huggingface_hub>=0.10.0,<0.12.0`? But requirements pin exact better. Use `huggingface_hub==0.11.1`. Need ensure Python 3.8 and no SSL etc. Good.\n\nHowever, there is another issue in language_utils.py: imports `from curses import A_ATTRIBUTES`, `from pip import main` weird but not fatal. SentenceTransformer model loading 'all-MiniLM-L6-v2' may require network. Fine.\n\nThe user asks root cause from logs. We can output edit requirements.txt adding pin. Also maybe edit model.py `use_auth_token=token` to `token=token`? Wait huggingface_hub 0.11 supports `use_auth_token`. Fine. No need.\n\nBut if pinning huggingface_hub to 0.11, then sentence-transformers 2.2.0 import `snapshot_download.REPO_ID_SEPARATOR` should work. Let's verify by maybe known: In huggingface_hub 0.11, `snapshot_download` module exists. Yes.\n\nLet's formulate JSON. diagnosis quote decisive log line. category hf-hub-api-removed or dependency conflict. verdict fixable. edits requirements.txt. Expected effect.\n\nNeed ensure search exact. requirements.txt content shown. We can add line. Use full content maybe easier. The instructions say use search/replace for code files; requirements small but can use content? \"Use full content only for small files.\" requirements small. We can use content full. But to be safe search/replace. Add `huggingface_hub==0.11.1` maybe after sentence-transformers. Search `sentence-transformers==2.2.0\\n` replace with `sentence-transformers==2.2.0\\nhuggingface_hub==0.11.1\\n`. Exact once.\n\nNeed decide version. Let's maybe use `0.10.1`? Let's verify which version has `REPO_ID_SEPARATOR`. Search memory: In huggingface_hub 0.10.0, `snapshot_download.py` has:\n```\nREPO_ID_SEPARATOR = \"--\"\ndef snapshot_download(..., ...):\n```\nI think yes. In 0.11 too. Which is more compatible with gradio? Gradio 3.0.17 maybe requires huggingface-hub>=0.8.0? both okay. Pin 0.11.1 is later and maybe includes fixes. Let's use 0.11.1.\n\nBut wait: sentence-transformers 2.2.0's `SentenceTransformer.py` line 12 imports `cached_download`; in huggingface_hub 0.11, cached_download exists. `util.py` line 407 imports `REPO_ID_SEPARATOR`; exists. Good.\n\nPotential issue: huggingface_hub 0.11 requires `filelock`, `requests`, etc; okay.\n\nLet's final.",
   "usage": {
    "prompt_tokens": 62865,
    "completion_tokens": 4677,
    "total_tokens": 67542,
    "prompt_tokens_details": {
     "cached_tokens": 512
    },
    "completion_tokens_details": {
     "reasoning_tokens": 4446
    }
   },
   "diagnosis": "The rebuild fails because sentence-transformers 2.2.0 imports removed/changed huggingface_hub APIs; the decisive error is `ImportError: cannot import name 'cached_download' from 'huggingface_hub'` (and the related `from huggingface_hub.snapshot_download import REPO_ID_SEPARATOR`). The installed huggingface_hub 0.36.2 is too new for sentence-transformers 2.2.0.",
   "category": "hf-hub-api-removed",
   "verdict": "fixable",
   "diff": "--- a/README.md\n+++ b/README.md\n@@ -1,4 +1,5 @@\n ---\n+python_version: \"3.8\"\n title: Text2Human\n emoji: \ud83c\udfc3\n colorFrom: purple\n--- a/requirements.txt\n+++ b/requirements.txt\n@@ -6,6 +6,7 @@\n openmim==0.1.5\n Pillow==9.1.1\n sentence-transformers==2.2.0\n+huggingface_hub==0.11.1\n tokenizers==0.12.1\n torch==1.11.0\n torchvision==0.12.0\n",
   "stage": "runtime_error",
   "logs": "Found existing installation: mmcv-full 1.5.2\nUninstalling mmcv-full-1.5.2:\n  Successfully uninstalled mmcv-full-1.5.2\ninstalling mmcv-full from wheel.\nLooking in indexes: https://pypi.org/simple, https://download.pytorch.org/whl/cpu\nLooking in links: https://download.openmmlab.com/mmcv/dist/cpu/torch1.11.0/index.html\nCollecting mmcv-full==1.5.2\n  Downloading https://download.openmmlab.com/mmcv/dist/cpu/torch1.11.0/mmcv_full-1.5.2-cp38-cp38-manylinux1_x86_64.whl (21.4 MB)\n     \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 21.4/21.4 MB 15.7 MB/s eta 0:00:00\nRequirement already satisfied: addict in /usr/local/lib/python3.8/site-packages (from mmcv-full==1.5.2) (2.4.0)\nRequirement already satisfied: numpy in /usr/local/lib/python3.8/site-packages (from mmcv-full==1.5.2) (1.22.3)\nRequirement already satisfied: packaging in /usr/local/lib/python3.8/site-packages (from mmcv-full==1.5.2) (26.2)\nRequirement already satisfied: Pillow in /usr/local/lib/python3.8/site-packages (from mmcv-full==1.5.2) (9.1.1)\nRequirement already satisfied: pyyaml in /usr/local/lib/python3.8/site-packages (from mmcv-full==1.5.2) (6.0.3)\nRequirement already satisfied: yapf in /usr/local/lib/python3.8/site-packages (from mmcv-full==1.5.2) (0.43.0)\nRequirement already satisfied: opencv-python>=3 in /usr/local/lib/python3.8/site-packages (from mmcv-full==1.5.2) (5.0.0.93)\nRequirement already satisfied: platformdirs>=3.5.1 in /usr/local/lib/python3.8/site-packages (from yapf->mmcv-full==1.5.2) (4.3.6)\nRequirement already satisfied: tomli>=2.0.1 in /usr/local/lib/python3.8/site-packages (from yapf->mmcv-full==1.5.2) (2.5.0)\nInstalling collected packages: mmcv-full\nSuccessfully installed mmcv-full-1.5.2\nSuccessfully installed mmcv-full.\npatching file models/hierarchy_inference_model.py\npatching file models/hierarchy_vqgan_model.py\npatching file models/parsing_gen_model.py\npatching file models/sample_model.py\npatching file models/transformer_model.py\npatching file models/vqgan_model.py\n/usr/local/lib/python3.8/site-packages/gradio/tunneling.py:13: CryptographyDeprecationWarning: Python 3.8 is no longer supported by the Python core team and support for it is deprecated in cryptography. The next release of cryptography will remove support for Python 3.8.\n  from cryptography.utils import CryptographyDeprecationWarning\nWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.\nWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.\nTraceback (most recent call last):\n  File \"app.py\", line 21, in <module>\n    from model import Model\n  File \"/home/user/app/model.py\", line 16, in <module>\n    from utils.language_utils import (generate_shape_attributes,\n  File \"/home/user/app/Text2Human/utils/language_utils.py\", line 6, in <module>\n    from sentence_transformers import SentenceTransformer, util\n  File \"/usr/local/lib/python3.8/site-packages/sentence_transformers/__init__.py\", line 3, in <module>\n    from .datasets import SentencesDataset, ParallelSentencesDataset\n  File \"/usr/local/lib/python3.8/site-packages/sentence_transformers/datasets/__init__.py\", line 3, in <module>\n    from .ParallelSentencesDataset import ParallelSentencesDataset\n  File \"/usr/local/lib/python3.8/site-packages/sentence_transformers/datasets/ParallelSentencesDataset.py\", line 4, in <module>\n    from .. import SentenceTransformer\n  File \"/usr/local/lib/python3.8/site-packages/sentence_transformers/SentenceTransformer.py\", line 25, in <module>\n    from .evaluation import SentenceEvaluator\n  File \"/usr/local/lib/python3.8/site-packages/sentence_transformers/evaluation/__init__.py\", line 5, in <module>\n    from .InformationRetrievalEvaluator import InformationRetrievalEvaluator\n  File \"/usr/local/lib/python3.8/site-packages/sentence_transformers/evaluation/InformationRetrievalEvaluator.py\", line 6, in <module>\n    from ..util import cos_sim, dot_score\n  File \"/usr/local/lib/python3.8/site-packages/sentence_transformers/util.py\", line 407, in <module>\n    from huggingface_hub.snapshot_download import REPO_ID_SEPARATOR\nModuleNotFoundError: No module named 'huggingface_hub.snapshot_download'\n",
   "api": null,
   "probe": null
  },
  {
   "effort": "medium",
   "turns": [],
   "raw": "{\n  \"diagnosis\": \"The rebuild fails because sentence-transformers 2.2.0 imports huggingface_hub APIs that no longer exist in the installed huggingface_hub 0.36.2, e.g. `ImportError: cannot import name 'cached_download' from 'huggingface_hub'`. Pinning an old huggingface_hub did not work because Spaces installs `spaces` after requirements.txt and upgrades it again.\",\n  \"category\": \"huggingface_hub-api-removed\",\n  \"verdict\": \"fixable\",\n  \"edits\": [\n    {\n      \"path\": \"requirements.txt\",\n      \"search\": \"sentence-transformers==2.2.0\",\n      \"replace\": \"sentence-transformers>=2.5.0\"\n    },\n    {\n      \"path\": \"requirements.txt\",\n      \"search\": \"tokenizers==0.12.1\",\n      \"replace\": \"tokenizers>=0.14.0\"\n    },\n    {\n      \"path\": \"requirements.txt\",\n      \"search\": \"transformers==4.19.2\",\n      \"replace\": \"transformers>=4.34.0\"\n    }\n  ],\n  \"lock\": {\n    \"date\": \"2024-03-15\",\n    \"python\": \"3.8\"\n  },\n  \"expected_effect\": \"Dependencies resolve to a sentence-transformers release compatible with modern huggingface_hub, while torch remains 1.11.0 and the original Text2Human inference path remains unchanged.\"\n}",
   "reasoning": "We need answer JSON only. Need solve. We need analyze logs. Space old code gradio 3.0.17, torch 1.11, sentence-transformers 2.2.0, huggingface_hub modern. Previous attempt pinned huggingface_hub==0.11.1 but still ModuleNotFoundError snapshot_download, meaning installed huggingface_hub likely upgraded by spaces/SDK after requirements? Wait requirements pip install then gradio and spaces installed after requirements can upgrade huggingface_hub. In Dockerfile:\nRUN pip install -r requirements.txt\nRUN pip install \"gradio[oauth]==3.0.17\" \"uvicorn>=0.14.0\" spaces\nspaces may depend on newer huggingface_hub, upgrading. Pin in requirements doesn't hold because subsequent install. They added huggingface_hub==0.11.1 but spaces/gradio upgrade it. Need lock transitive or avoid spaces upgrading? Could pin huggingface_hub<1 maybe but spaces requires newer? Let's inspect. Need propose edits. We can use lock tool to freeze all deps at older date/python. But need be careful. The task: Space broken. Need smallest change. Root cause: huggingface_hub API removed. sentence-transformers 2.2.0 incompatible with huggingface_hub 0.36.2 installed by spaces. Options:\n1. Pin huggingface_hub<1 in requirements AND ensure spaces doesn't upgrade. But spaces package may require huggingface_hub>=? Let's check known: spaces 0.46.0 requires huggingface_hub? It might require >=0.20. Maybe gradio 3.0.17 requires huggingface_hub? Older gradio maybe <0.12? Actually gradio 3.0.17 dependencies maybe huggingface_hub>=0.8.1,<0.9? Let's recall. Gradio 3.0.17 requires huggingface_hub>=0.8.1? It might install huggingface_hub 0.10? But spaces 0.46.0 definitely newer and can upgrade. If we pin huggingface_hub==0.11.1 in requirements, subsequent `pip install spaces` upgrades to 0.36.2. Could pin in second install? Can't edit Dockerfile? README front matter only. sdk_version controls gradio version? Dockerfile generated. We can't modify install order. Could add constraints? requirements.txt with `huggingface_hub==0.11.1` then spaces upgrade. Maybe add `huggingface_hub==0.11.1` as direct and also `spaces`? But spaces likely incompatible. Could avoid sentence-transformers entirely? Need model loading? language_utils uses SentenceTransformer for text similarity. Could replace with transformers? But smallest? The app needs original functionality. Could perhaps use a local sentence-transformers version compatible? sentence-transformers 2.2.0 requires huggingface_hub? Let's check its metadata: sentence-transformers 2.2.0 (Aug 2022) likely requires huggingface_hub>=0.4.0? It imports cached_download? Actually old huggingface_hub 0.10 had cached_download. The modern spaces requires huggingface_hub 0.36. Maybe conflict unavoidable. Could pin spaces older? But Dockerfile installs spaces latest. README sdk_version 3.0.17. Maybe setting python? No.\n\nCould use lock with older date and python 3.8/3.10 to resolve all deps compatible. But spaces package is installed after requirements by Dockerfile; lock only requirements.txt. If requirements include a compatible huggingface_hub and spaces later upgrades, lock doesn't prevent. However maybe `uv pip compile` lock would pin huggingface_hub, but Dockerfile still runs `pip install spaces` after, which can upgrade. Unless spaces version in lock? spaces is not in requirements? It is installed by Dockerfile separately. The migration notes say SDK and spaces installed after requirements and can upgrade what it pins. So to keep old huggingface_hub, must avoid spaces upgrading it. Could pin `huggingface_hub` in requirements with upper bound, but pip install spaces may still upgrade if its dependency requires newer. Need know spaces dependency. Maybe spaces 0.46.0 requires huggingface_hub>=0.20? If so impossible to keep 0.11.1. Could install an older spaces? But Dockerfile fixed spaces latest. Could add `spaces==?` in requirements? Dockerfile still installs spaces after, likely upgrades to latest. Can't prevent.\n\nAlternative code edit: update sentence-transformers import to work with modern huggingface_hub. But sentence-transformers 2.2.0's util.py line 407 imports `REPO_ID_SEPARATOR` from `huggingface_hub.snapshot_download`. In huggingface_hub 0.36, snapshot_download moved? Let's check. `huggingface_hub.snapshot_download` module still exists? Error says no module named 'huggingface_hub.snapshot_download' maybe in 0.36? Actually huggingface_hub 0.36 maybe moved snapshot_download to `huggingface_hub` top-level and removed submodule? Let's verify mentally: huggingface_hub has `snapshot_download` function in `__init__`, and module `huggingface_hub.file_download` maybe. Old `huggingface_hub.snapshot_download` module may have been removed. Could monkeypatch? We can edit language_utils to avoid sentence_transformers? Or patch huggingface_hub module before import. But \"never wrap imports try/except to hide failures\"; could create compatibility shim. But smallest honest fix maybe upgrade sentence-transformers to a version compatible with huggingface_hub 0.36 and torch 1.11? sentence-transformers newer requires torch >=? Maybe sentence-transformers 2.7.0 supports torch 1.11? Let's see. Newer sentence-transformers may require transformers >=4.34, torch >=1.11? Could upgrade. But model all-MiniLM-L6-v2. Requirements pins transformers==4.19.2; newer sentence-transformers may require newer transformers. Could update both. But code uses util.dot_score only. We can use transformers directly? Simpler: replace SentenceTransformer with `sentence_transformers` newer version and bump transformers. But model loading maybe okay. Need test? We can't run. We need choose robust.\n\nLet's inspect previous attempt: pinned huggingface_hub 0.11.1 but spaces upgraded. The decisive log from previous rebuild still same snapshot_download missing, meaning huggingface_hub ended 0.36.2. So pin alone ineffective. Need either code compatibility with modern huggingface_hub or lock + maybe pin spaces? The tool lock can set python and date but not spaces. However lock result replaces requirements.txt; Dockerfile installs gradio and spaces after. If lock includes `huggingface-hub==0.11.1`, spaces install upgrades. Could include `spaces==0.46.0`? Still Dockerfile `pip install spaces` (unpinned) after requirements, so latest. Could include `gradio==3.0.17` in requirements? Dockerfile still installs gradio[oauth]==3.0.17 after; same. For huggingface_hub, no way to force unless spaces doesn't require newer. Let's check spaces 0.46.0 dependency likely `huggingface-hub>=0.20.0`. So old hub incompatible with spaces. Thus code must support modern hub. Could shim huggingface_hub.snapshot_download module. But sentence-transformers also imports `cached_download` from huggingface_hub top-level (newer error in first log? Actually first rebuild: sentence_transformers 2.2.0 SentenceTransformer imports `cached_download` from huggingface_hub, removed in 1.0). So even if snapshot_download shim, cached_download missing. Need provide compatibility shims for both `cached_download` and `snapshot_download.REPO_ID_SEPARATOR`. Could implement minimal shims mapping to `hf_hub_download`/`snapshot_download`. But sentence-transformers 2.2.0's usage of cached_download maybe just download. Need inspect installed sentence_transformers 2.2.0 code? We have trace: SentenceTransformer line 12 imports cached_download. Could patch module huggingface_hub to add `cached_download` alias to `hf_hub_download` before importing sentence_transformers. And create fake submodule `huggingface_hub.snapshot_download` with REPO_ID_SEPARATOR. What is REPO_ID_SEPARATOR? Likely \"/\"? Let's find. In old huggingface_hub, `REPO_ID_SEPARATOR = \"--\"`? Need know. Could search memory. huggingface_hub.snapshot_download module had `REPO_ID_SEPARATOR = \"--\"` used for cache dir? Actually `snapshot_download` uses `REPO_ID_SEPARATOR = \"--\"` to convert repo id to cache folder? Let's recall huggingface_hub constants: `REPO_ID_SEPARATOR = \"--\"` maybe yes. In newer huggingface_hub, maybe `HF_HUB_CACHE` etc. Could set. But sentence-transformers util.py line 407 only imports constant, doesn't use? It does `from huggingface_hub.snapshot_download import REPO_ID_SEPARATOR`; then maybe uses in function. Need exact. Could inspect installed? Not available here. Could grep? We can't run. But likely constant string. Let's search memory: In huggingface_hub 0.10, file `snapshot_download.py`:\n```\nREPO_ID_SEPARATOR = \"--\"\ndef snapshot_download(...):\n...\n```\nMaybe. We can set to \"--\". But if used for path, wrong separator could cause issues. However sentence-transformers 2.2.0 only uses REPO_ID_SEPARATOR maybe in cache path? Let's think.\n\nAlternative upgrade sentence-transformers to modern version. Requirements has sentence-transformers==2.2.0 and transformers==4.19.2. If we bump sentence-transformers to e.g. 2.7.0, it may require transformers>=4.34, torch>=1.11.0? Actually sentence-transformers 2.7.0 supports Python 3.8? It requires torch>=1.11.0? Let's check memory: sentence-transformers 2.6.1 requires transformers>=4.34? Maybe. Upgrading transformers from 4.19.2 to 4.38 could break other code? The app only uses sentence-transformers, not transformers directly except maybe model? Text2Human models don't import transformers. So safe. But torch 1.11.0 with newer transformers? Transformers 4.38 supports torch >=1.11 maybe. Could work. But larger dependency changes may have conflicts with mmcv etc. Lock could resolve. However Dockerfile installs spaces after and may upgrade; lock requirements could include all. But spaces separate. We can use lock date around 2024 maybe to resolve modern compatible versions. Yet app code written for old transformers? Not used. Sentence-transformers newer API same SentenceTransformer, util.dot_score. So code unchanged. This is honest: upgrade library. But requirements lock might choose torch 2.x? We need pin torch==1.11.0? Lock with exclude-newer and python 3.8; if requirements has torch==1.11.0, uv will keep. transformers can upgrade. huggingface_hub modern. sentence-transformers modern. This likely works. But will newer sentence-transformers download all-MiniLM-L6-v2 okay? yes.\n\nBut task says prefer pinning versions or bumping sdk_version when honest; edit code when API no longer exists. Upgrading sentence-transformers is acceptable. Could simply change requirements:\nsentence-transformers==2.7.0 (or latest compatible)\ntransformers>=4.34\nhuggingface_hub maybe modern\nBut need avoid spaces upgrading torch? spaces doesn't require torch. Fine.\n\nYet there may be hidden issue: newer sentence-transformers imports `torch` etc. Should work with torch 1.11? Let's verify version compatibility. sentence-transformers 2.2.2? Actually sentence-transformers 2.2.0 is from 2022. The next versions 2.2.1, 2.2.2 still old. To support huggingface_hub 0.36, need sentence-transformers >=2.3? Let's recall:\n- sentence-transformers 2.3.0 (Dec 2023?) maybe uses huggingface_hub >=0.19? \n- 2.5.0? \n- 2.7.0 (Apr 2024) maybe requires torch>=1.11.0, transformers>=4.34? \nWe can use lock with date 2024-06-01 and python 3.8, keep torch==1.11.0, transformers maybe 4.41? uv compile will choose compatible. But sentence-transformers version selected might be latest as of date, e.g. 3.0? Python 3.8 support? sentence-transformers 3.0 requires Python >=3.8? Maybe. But 3.0 might require torch >=1.11? Could be. Let's use lock with date 2024-04-01 and python 3.8 maybe sentence-transformers 2.6/2.7. Need not specify exact; lock resolves. But requirements.txt currently has many pins. We can provide lock object. The grader likely runs rebuild with lock. We need decide.\n\nCould combine code edit and lock. The previous attempt's README python_version addition perhaps not needed; lock handles python. We can return edits removing huggingface_hub pin? Actually lock uses requirements. We can keep requirements as is but add lock. But if lock resolves sentence-transformers 2.2.0 with huggingface_hub 0.11? But spaces upgrades. The root issue in rebuild is spaces upgrade. Lock doesn't fix spaces. Hmm.\n\nMaybe better to shim huggingface_hub in code so old sentence-transformers works with modern hub. Add to Text2Human/utils/language_utils.py before import:\n```\nimport huggingface_hub\nif not hasattr(huggingface_hub, \"cached_download\"):\n    huggingface_hub.cached_download = huggingface_hub.hf_hub_download\nimport types\nmod = types.ModuleType(\"huggingface_hub.snapshot_download\")\nmod.REPO_ID_SEPARATOR = \"--\"\nsys.modules[\"huggingface_hub.snapshot_download\"] = mod\n```\nBut is this \"honest\"? It's a compatibility shim, not hiding failure. Could be acceptable. But need ensure sentence-transformers 2.2.0's other uses of cached_download work. `cached_download` signature differs? In old huggingface_hub, cached_download(repo_id, filename, ...) returns path. hf_hub_download has similar. Should work. `REPO_ID_SEPARATOR` constant maybe used. Need value. Could inspect source by memory or maybe use newer huggingface_hub internal. Let's search mental: In huggingface_hub 0.10, `snapshot_download.py`:\n```\nREPO_ID_SEPARATOR = \"--\"\ndef snapshot_download(repo_id, ...):\n    ...\n    storage_folder = os.path.join(cache_dir, repo_id.replace(\"/\", REPO_ID_SEPARATOR))\n```\nYes likely \"--\". Good.\n\nBut the error in first log: `from huggingface_hub.snapshot_download import REPO_ID_SEPARATOR` happens at import of sentence_transformers.util. If we create module before importing sentence_transformers, import succeeds. Then SentenceTransformer imports cached_download from huggingface_hub; if we alias, succeeds. Then runtime model loading uses SentenceTransformer('all-MiniLM-L6-v2'), which internally uses huggingface_hub functions maybe modern enough? Old sentence-transformers 2.2.0 uses `hf_hub_download`? It may use `cached_download` only at import? Let's see. It likely uses `hf_hub_url`, `cached_download` in model loading. If cached_download alias to hf_hub_download, arguments may differ. Old cached_download signature: `cached_download(url_or_filename, ...)`. Actually huggingface_hub.cached_download in 0.10 accepted `url_or_filename` or repo? Let's recall. In very old huggingface_hub, `cached_download` downloaded from URL. Newer `hf_hub_download` downloads by repo_id/filename. Alias may fail at runtime. But maybe sentence-transformers 2.2.0 uses `hf_hub_download` already? Let's check trace: import line only. It may use `hf_hub_download` for model files. The `cached_download` import could be unused? Need inspect. Could instead avoid sentence-transformers by reimplementing embeddings with transformers? But all-MiniLM-L6-v2 is sentence-transformers model; can use `transformers` AutoModel + mean pooling. That avoids huggingface_hub compatibility issues and keeps functionality. But more code.\n\nAlternative upgrade sentence-transformers to modern. That's cleaner and avoids shims. Need ensure torch 1.11 compatibility. Let's investigate likely version. We can use lock to let uv choose. But spaces upgrade issue? If requirements has modern sentence-transformers and modern huggingface_hub, spaces install won't break because spaces wants modern. Good. So upgrade path is best. Need update requirements maybe sentence-transformers>=2.3.0 and transformers>=4.34.0? But lock can resolve. We can provide lock date. Also keep torch==1.11.0? Lock with exclude-newer date; if torch pin remains, uv keeps 1.11.0. But sentence-transformers modern may require torch>=1.11.0, okay. Transformers modern requires torch>=1.10, okay. mmcv-full 1.5.2 requires torch? It may work. Let's set requirements:\n```\nsentence-transformers>=2.5.0\ntransformers>=4.34.0\n```\nBut maybe Python 3.8 and torch 1.11.0. Lock date maybe 2024-06-01. However uv compile might select torch 2.3? Because requirements has torch==1.11.0 pin, so no. Good. It might select sentence-transformers 3.0.0 which supports Python 3.8? Let's check sentence-transformers 3.0.0 requires Python >=3.8? I think 3.0.0 supports Python 3.8? Actually sentence-transformers 3.0.0 (May 2024) requires Python>=3.8? It might. But 3.0 may require torch>=1.11.0. Fine. Lock date 2024-06-01 includes 3.0.1 maybe. Could be okay.\n\nBut there is potential issue: newer sentence-transformers with torch 1.11.0 and transformers 4.41 may fail due to `torch.load(weights_only=True)`? Not relevant. Also `tokenizers` pin 0.12.1 conflicts with transformers 4.34 requiring tokenizers>=0.14. We should remove tokenizers pin or let lock update. requirements currently tokenizers==0.12.1. Lock will update to compatible if no pin? If we leave pin, conflict. We can edit requirements to remove tokenizers pin or bump. Let's set `tokenizers>=0.14.0` maybe. Or remove. Use lock.\n\nAlso `numpy==1.22.3` okay. `mmcv-full==1.5.2` okay.\n\nWe also need maybe bump sdk_version? Gradio 3.0.17 installed after requirements; spaces install gradio 3.0.17. Fine. Keep.\n\nCould just return lock with date and python, plus requirements edits to relax sentence-transformers/transformers/tokenizers. But user expects edits. We can include edits:\n- requirements.txt: change sentence-transformers==2.2.0 to sentence-transformers>=2.5.0 maybe\n- transformers==4.19.2 to transformers>=4.34.0\n- tokenizers==0.12.1 to tokenizers>=0.14.0\n- remove huggingface_hub pin if present? Previous attempt added huggingface_hub==0.11.1; in current repo requirements shown doesn't include it (we are editing original, not previous). So no.\n- maybe add `huggingface_hub>=0.20.0`? Let lock resolve.\n\nThen lock date 2024-06-01 python 3.8. But is Python 3.8 supported by Spaces? Dockerfile uses python:3.8 fixed. Yes.\n\nWould lock resolve with torch 1.11.0 CPU? PIP_EXTRA_INDEX_URL pytorch CPU. uv compile with python 3.8 should find torch 1.11.0+cpu maybe. Good.\n\nNeed consider `spaces` package installed after requirements and may upgrade huggingface_hub to 0.36; modern sentence-transformers okay. Great.\n\nLet's formulate JSON. Diagnosis quote decisive log: `ImportError: cannot import name 'cached_download' from 'huggingface_hub'` (and previous snapshot_download). Category dependency-conflict or huggingface_hub-api-removed. Verdict fixable. Edits relax pins. Lock. Expected effect. Need ensure JSON valid. Use search/replace exact. requirements lines unique. We'll replace three lines. Could also remove `tokenizers` pin? Let's keep >=. \n\nLet's think if upgrading sentence-transformers to >=2.5.0 while torch==1.11.0 and transformers>=4.34 may cause lock failure due to sentence-transformers requiring torch>=1.11.0? okay. Could choose date 2024-03-01 to avoid too new. But sentence-transformers 2.5.1 released Feb 2024? Let's pick 2024-03-15. Python 3.8. Lock date. Fine.\n\nNeed maybe include `python_version: \"3.8\"` in README? Lock sets python_version. We don't need edit README. But original README no python_version; Dockerfile python 3.8. Lock tool sets README python_version. Good.\n\nLet's answer.",
   "usage": {
    "prompt_tokens": 64152,
    "completion_tokens": 5119,
    "total_tokens": 69271,
    "prompt_tokens_details": {
     "cached_tokens": 49664
    },
    "completion_tokens_details": {
     "reasoning_tokens": 4794
    }
   },
   "diagnosis": "The rebuild fails because sentence-transformers 2.2.0 imports huggingface_hub APIs that no longer exist in the installed huggingface_hub 0.36.2, e.g. `ImportError: cannot import name 'cached_download' from 'huggingface_hub'`. Pinning an old huggingface_hub did not work because Spaces installs `spaces` after requirements.txt and upgrades it again.",
   "category": "huggingface_hub-api-removed",
   "verdict": "fixable",
   "lock": {
    "date": "2024-03-15",
    "python": "3.8"
   },
   "diff": "--- a/README.md\n+++ b/README.md\n@@ -1,4 +1,5 @@\n ---\n+python_version: \"3.8\"\n title: Text2Human\n emoji: \ud83c\udfc3\n colorFrom: purple\n--- a/requirements.txt\n+++ b/requirements.txt\n@@ -1,12 +1,97 @@\n+addict==2.4.0\n+aiohttp==3.9.3\n+aiosignal==1.3.1\n+analytics-python==1.4.post1\n+annotated-types==0.6.0\n+anyio==4.3.0\n+async-timeout==4.0.3\n+attrs==23.2.0\n+backoff==1.10.0\n+bcrypt==4.1.2\n+certifi==2024.2.2\n+cffi==1.16.0\n+charset-normalizer==3.3.2\n+click==7.1.2\n+colorama==0.4.6\n+contourpy==1.1.1\n+cryptography==42.0.5\n+cycler==0.12.1\n einops==0.4.1\n+exceptiongroup==1.2.0\n+fastapi==0.110.0\n+ffmpy==0.3.2\n+filelock==3.13.1\n+fonttools==4.50.0\n+frozenlist==1.4.1\n+fsspec==2024.3.0\n+gradio==3.0.17\n+h11==0.14.0\n+huggingface-hub==0.21.4\n+idna==3.6\n+importlib-metadata==7.0.2\n+importlib-resources==6.3.0\n+jinja2==3.1.3\n+joblib==1.3.2\n+kiwisolver==1.4.5\n+linkify-it-py==2.0.3\n lpips==0.1.4\n+markdown==3.6\n+markdown-it-py==3.0.0\n+markupsafe==2.1.5\n+matplotlib==3.7.5\n+mdit-py-plugins==0.4.0\n+mdurl==0.1.2\n+mmcls==0.25.0\n mmcv-full==1.5.2\n mmsegmentation==0.24.1\n+model-index==0.1.11\n+monotonic==1.6\n+multidict==6.0.5\n numpy==1.22.3\n+opencv-python==4.9.0.80\n openmim==0.1.5\n-Pillow==9.1.1\n-sentence-transformers==2.2.0\n-tokenizers==0.12.1\n+ordered-set==4.1.0\n+orjson==3.9.15\n+packaging==24.0\n+pandas==2.0.3\n+paramiko==3.4.0\n+pillow==9.1.1\n+platformdirs==4.2.0\n+prettytable==3.10.0\n+pycparser==2.21\n+pycryptodome==3.20.0\n+pydantic==2.6.4\n+pydantic-core==2.16.3\n+pydub==0.25.1\n+pynacl==1.5.0\n+pyparsing==3.1.2\n+python-dateutil==2.9.0.post0\n+python-multipart==0.0.9\n+pytz==2024.1\n+pyyaml==6.0.1\n+regex==2023.12.25\n+requests==2.31.0\n+safetensors==0.4.2\n+scikit-learn==1.3.2\n+scipy==1.10.1\n+sentence-transformers==2.5.1\n+six==1.16.0\n+sniffio==1.3.1\n+starlette==0.36.3\n+tabulate==0.9.0\n+threadpoolctl==3.3.0\n+tokenizers==0.15.2\n+tomli==2.0.1\n torch==1.11.0\n torchvision==0.12.0\n-transformers==4.19.2\n+tqdm==4.66.2\n+transformers==4.38.2\n+typing-extensions==4.10.0\n+tzdata==2024.1\n+uc-micro-py==1.0.3\n+urllib3==2.2.1\n+uvicorn==0.28.0\n+wcwidth==0.2.13\n+yapf==0.40.2\n+yarl==1.9.4\n+zipp==3.18.1\n",
   "stage": "api",
   "logs": "PROBE (we called the app's endpoints with synthetic inputs):\n{\"endpoint\": \"fn_index:2\", \"ok\": false, \"inputs\": [\"data:image/png;base64,R0lGODlhPQBEAPeoAJosM//AwO/AwHVYZ/z595kzAP/s7P+goOXMv8+fhw\", \"Howdy!\", \"5\", \"5\"], \"error\": \"ValueError('Unknown protocol: ws')\"}\n{\"endpoint\": \"fn_index:3\", \"ok\": false, \"inputs\": [], \"error\": \"ValueError('Unknown protocol: ws')\"}\n{\"endpoint\": \"fn_index:4\", \"ok\": false, \"inputs\": [], \"error\": \"ValueError('Unknown protocol: ws')\"}\n{\"endpoint\": \"fn_index:2\", \"ok\": false, \"inputs\": [\"/data/work/CVPR__Text2Human/fixed/pose_images/000.png\", \"a photo of a red apple on a wooden table\", \"10\", \"0\"], \"error\": \"InvalidStatus(Response(status_code=404, reason_phrase='Not Found', headers=Headers([('date', 'Mon, 02 Nov 2026 19:17:33 GMT'), ('server', 'uvicorn'), ('content-length', '22'), ('content-type', 'application/json')]), body=bytearray(b'{\\\"detail\\\":\\\"Not Found\\\"}'), _exception=InvalidStatus(...)))\", \"via\": \"legacy\"}\n{\"endpoint\": \"fn_index:1\", \"ok\": false, \"inputs\": [\"None\", \"a photo of a red apple on a wooden table\"], \"error\": \"InvalidStatus(Response(status_code=404, reason_phrase='Not Found', headers=Headers([('date', 'Mon, 02 Nov 2026 19:17:33 GMT'), ('server', 'uvicorn'), ('content-length', '22'), ('content-type', 'application/json')]), body=bytearray(b'{\\\"detail\\\":\\\"Not Found\\\"}'), _exception=InvalidStatus(...)))\", \"via\": \"legacy\"}\n{\"endpoint\": \"fn_index:3\", \"ok\": false, \"inputs\": [\"None\"], \"error\": \"InvalidStatus(Response(status_code=404, reason_phrase='Not Found', headers=Headers([('date', 'Mon, 02 Nov 2026 19:17:33 GMT'), ('server', 'uvicorn'), ('content-length', '22'), ('content-type', 'application/json')]), body=bytearray(b'{\\\"detail\\\":\\\"Not Found\\\"}'), _exception=InvalidStatus(...)))\", \"via\": \"legacy\"}\n\nAPP LOG:\n...[truncated]...\nRequirement already satisfied: packaging in /usr/local/lib/python3.8/site-packages (from mmcv-full==1.5.2) (24.0)\nRequirement already satisfied: Pillow in /usr/local/lib/python3.8/site-packages (from mmcv-full==1.5.2) (9.1.1)\nRequirement already satisfied: pyyaml in /usr/local/lib/python3.8/site-packages (from mmcv-full==1.5.2) (6.0.1)\nRequirement already satisfied: yapf in /usr/local/lib/python3.8/site-packages (from mmcv-full==1.5.2) (0.40.2)\nRequirement already satisfied: opencv-python>=3 in /usr/local/lib/python3.8/site-packages (from mmcv-full==1.5.2) (4.9.0.80)\nRequirement already satisfied: importlib-metadata>=6.6.0 in /usr/local/lib/python3.8/site-packages (from yapf->mmcv-full==1.5.2) (7.0.2)\nRequirement already satisfied: platformdirs>=3.5.1 in /usr/local/lib/python3.8/site-packages (from yapf->mmcv-full==1.5.2) (4.2.0)\nRequirement already satisfied: tomli>=2.0.1 in /usr/local/lib/python3.8/site-packages (from yapf->mmcv-full==1.5.2) (2.0.1)\nRequirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.8/site-packages (from importlib-metadata>=6.6.0->yapf->mmcv-full==1.5.2) (3.18.1)\nInstalling collected packages: mmcv-full\nSuccessfully installed mmcv-full-1.5.2\nSuccessfully installed mmcv-full.\npatching file models/hierarchy_inference_model.py\npatching file models/hierarchy_vqgan_model.py\npatching file models/parsing_gen_model.py\npatching file models/sample_model.py\npatching file models/transformer_model.py\npatching file models/vqgan_model.py\ngpu_list:  3\nWorking with z of shape (1, 256, 32, 16) = 131072 dimensions.\nWorking with z of shape (1, 256, 64, 32) = 524288 dimensions.\nRunning on local URL:  http://localhost:7860/\n\nTo create a public link, set `share=True` in `launch()`.\nWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.\nWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.\nWARNING:  Unsupported upgrade request.\nWARNING:  No supported WebSocket library detected. Please use \"pip install 'uvicorn[standard]'\", or install 'websockets' or 'wsproto' manually.\nWARNING:  Unsupported upgrade request.\nWARNING:  No supported WebSocket library detected. Please use \"pip install 'uvicorn[standard]'\", or install 'websockets' or 'wsproto' manually.\nWARNING:  Unsupported upgrade request.\nWARNING:  No supported WebSocket library detected. Please use \"pip install 'uvicorn[standard]'\", or install 'websockets' or 'wsproto' manually.\n",
   "api": "{\"named\": [], \"unnamed\": 4}",
   "probe": [
    {
     "endpoint": "fn_index:2",
     "ok": false,
     "inputs": [
      "data:image/png;base64,R0lGODlhPQBEAPeoAJosM//AwO/AwHVYZ/z595kzAP/s7P+goOXMv8+fhw",
      "Howdy!",
      "5",
      "5"
     ],
     "error": "ValueError('Unknown protocol: ws')"
    },
    {
     "endpoint": "fn_index:3",
     "ok": false,
     "inputs": [],
     "error": "ValueError('Unknown protocol: ws')"
    },
    {
     "endpoint": "fn_index:4",
     "ok": false,
     "inputs": [],
     "error": "ValueError('Unknown protocol: ws')"
    },
    {
     "endpoint": "fn_index:2",
     "ok": false,
     "inputs": [
      "/data/work/CVPR__Text2Human/fixed/pose_images/000.png",
      "a photo of a red apple on a wooden table",
      "10",
      "0"
     ],
     "error": "InvalidStatus(Response(status_code=404, reason_phrase='Not Found', headers=Headers([('date', 'Mon, 02 Nov 2026 19:17:33 GMT'), ('server', 'uvicorn'), ('content-length', '22'), ('content-type', 'application/json')]), body=bytearray(b'{\"detail\":\"Not Found\"}'), _exception=InvalidStatus(...)))",
     "via": "legacy"
    },
    {
     "endpoint": "fn_index:1",
     "ok": false,
     "inputs": [
      "None",
      "a photo of a red apple on a wooden table"
     ],
     "error": "InvalidStatus(Response(status_code=404, reason_phrase='Not Found', headers=Headers([('date', 'Mon, 02 Nov 2026 19:17:33 GMT'), ('server', 'uvicorn'), ('content-length', '22'), ('content-type', 'application/json')]), body=bytearray(b'{\"detail\":\"Not Found\"}'), _exception=InvalidStatus(...)))",
     "via": "legacy"
    },
    {
     "endpoint": "fn_index:3",
     "ok": false,
     "inputs": [
      "None"
     ],
     "error": "InvalidStatus(Response(status_code=404, reason_phrase='Not Found', headers=Headers([('date', 'Mon, 02 Nov 2026 19:17:33 GMT'), ('server', 'uvicorn'), ('content-length', '22'), ('content-type', 'application/json')]), body=bytearray(b'{\"detail\":\"Not Found\"}'), _exception=InvalidStatus(...)))",
     "via": "legacy"
    }
   ]
  },
  {
   "effort": "high",
   "turns": [],
   "raw": "{\n  \"diagnosis\": \"The rebuild fails because sentence-transformers 2.2.0 imports huggingface_hub APIs that no longer exist in the installed huggingface_hub (e.g. `from huggingface_hub.snapshot_download import REPO_ID_SEPARATOR` and `cached_download`), and `model.py` uses the removed `use_auth_token=` argument. Replacing the sentence-transformers usage with a direct transformers MiniLM implementation and updating the download call to `token=` removes the incompatible APIs.\",\n  \"category\": \"dependency-api-drift\",\n  \"verdict\": \"fixable\",\n  \"edits\": [\n    {\n      \"path\": \"Text2Human/utils/language_utils.py\",\n      \"content\": \"import numpy\\nimport torch\\nfrom transformers import AutoModel, AutoTokenizer\\n\\n# predefined shape text\\nupper_length_text = [\\n    'sleeveless', 'without sleeves', 'sleeves have been cut off', 'tank top',\\n    'tank shirt', 'muscle shirt', 'short-sleeve', 'short sleeves',\\n    'with short sleeves', 'medium-sleeve', 'medium sleeves',\\n    'with medium sleeves', 'sleeves reach elbow', 'long-sleeve',\\n    'long sleeves', 'with long sleeves'\\n]\\nupper_length_attr = {\\n    'sleeveless': 0,\\n    'without sleeves': 0,\\n    'sleeves have been cut off': 0,\\n    'tank top': 0,\\n    'tank shirt': 0,\\n    'muscle shirt': 0,\\n    'short-sleeve': 1,\\n    'with short sleeves': 1,\\n    'short sleeves': 1,\\n    'medium-sleeve': 2,\\n    'with medium sleeves': 2,\\n    'medium sleeves': 2,\\n    'sleeves reach elbow': 2,\\n    'long-sleeve': 3,\\n    'long sleeves': 3,\\n    'with long sleeves': 3\\n}\\nlower_length_text = [\\n    'three-point', 'medium', 'short', 'covering knee', 'cropped',\\n    'three-quarter', 'long', 'slack', 'of long length'\\n]\\nlower_length_attr = {\\n    'three-point': 0,\\n    'medium': 1,\\n    'covering knee': 1,\\n    'short': 1,\\n    'cropped': 2,\\n    'three-quarter': 2,\\n    'long': 3,\\n    'slack': 3,\\n    'of long length': 3\\n}\\nsocks_length_text = [\\n    'socks', 'stocking', 'pantyhose', 'leggings', 'sheer hosiery'\\n]\\nsocks_length_attr = {\\n    'socks': 0,\\n    'stocking': 1,\\n    'pantyhose': 1,\\n    'leggings': 1,\\n    'sheer hosiery': 1\\n}\\nhat_text = ['hat', 'cap', 'chapeau']\\neyeglasses_text = ['sunglasses']\\nbelt_text = ['belt', 'with a dress tied around the waist']\\nouter_shape_text = [\\n    'with outer clothing open', 'with outer clothing unzipped',\\n    'covering inner clothes', 'with outer clothing zipped'\\n]\\nouter_shape_attr = {\\n    'with outer clothing open': 0,\\n    'with outer clothing unzipped': 0,\\n    'covering inner clothes': 1,\\n    'with outer clothing zipped': 1\\n}\\n\\nupper_types = [\\n    'T-shirt', 'shirt', 'sweater', 'hoodie', 'tops', 'blouse', 'Basic Tee'\\n]\\nouter_types = [\\n    'jacket', 'outer clothing', 'coat', 'overcoat', 'blazer', 'outerwear',\\n    'duffle', 'cardigan'\\n]\\nskirt_types = ['skirt']\\ndress_types = ['dress']\\npant_types = ['jeans', 'pants', 'trousers']\\nrompers_types = ['rompers', 'bodysuit', 'jumpsuit']\\n\\nattr_names_list = [\\n    'gender', 'hair length', '0 upper clothing length',\\n    '1 lower clothing length', '2 socks', '3 hat', '4 eyeglasses', '5 belt',\\n    '6 opening of outer clothing', '7 upper clothes', '8 outer clothing',\\n    '9 skirt', '10 dress', '11 pants', '12 rompers'\\n]\\n\\n_MINILM = None\\n\\n\\ndef _get_minilm():\\n    global _MINILM\\n    if _MINILM is None:\\n        tokenizer = AutoTokenizer.from_pretrained(\\n            'sentence-transformers/all-MiniLM-L6-v2')\\n        model = AutoModel.from_pretrained(\\n            'sentence-transformers/all-MiniLM-L6-v2')\\n        model.eval()\\n        _MINILM = tokenizer, model\\n    return _MINILM\\n\\n\\ndef _encode(texts):\\n    tokenizer, model = _get_minilm()\\n    if isinstance(texts, str):\\n        texts = [texts]\\n    encoded = tokenizer(\\n        texts, padding=True, truncation=True, return_tensors='pt')\\n    with torch.no_grad():\\n        output = model(**encoded)\\n    token_embeddings = output.last_hidden_state\\n    attention_mask = encoded['attention_mask']\\n    input_mask_expanded = attention_mask.unsqueeze(-1).expand(\\n        token_embeddings.size()).float()\\n    sum_embeddings = torch.sum(token_embeddings * input_mask_expanded, dim=1)\\n    sum_mask = torch.clamp(input_mask_expanded.sum(dim=1), min=1e-9)\\n    return sum_embeddings / sum_mask\\n\\n\\ndef generate_shape_attributes(user_shape_texts):\\n    parsed_texts = user_shape_texts.split(',')\\n\\n    text_num = len(parsed_texts)\\n\\n    human_attr = [0, 0]\\n    attr = [1, 3, 0, 0, 0, 3, 1, 1, 0, 0, 0, 0, 0]\\n\\n    changed = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\\n    for text_id, text in enumerate(parsed_texts):\\n        user_embeddings = _encode(text)\\n        if ('man' in text) and (text_id == 0):\\n            human_attr[0] = 0\\n            human_attr[1] = 0\\n\\n        if ('woman' in text or 'lady' in text) and (text_id == 0):\\n            human_attr[0] = 1\\n            human_attr[1] = 2\\n\\n        if (not changed[0]) and (text_id == 1):\\n            # upper length\\n            predefined_embeddings = _encode(upper_length_text)\\n            similarities = user_embeddings @ predefined_embeddings.T\\n            arg_idx = torch.argmax(similarities).item()\\n            attr[0] = upper_length_attr[upper_length_text[arg_idx]]\\n            changed[0] = 1\\n\\n        if (not changed[1]) and ((text_num == 2 and text_id == 1) or\\n                                 (text_num > 2 and text_id == 2)):\\n            # lower length\\n            predefined_embeddings = _encode(lower_length_text)\\n            similarities = user_embeddings @ predefined_embeddings.T\\n            arg_idx = torch.argmax(similarities).item()\\n            attr[1] = lower_length_attr[lower_length_text[arg_idx]]\\n            changed[1] = 1\\n\\n        if (not changed[2]) and (text_id > 2):\\n            # socks length\\n            predefined_embeddings = _encode(socks_length_text)\\n            similarities = user_embeddings @ predefined_embeddings.T\\n            arg_idx = torch.argmax(similarities).item()\\n            if similarities[0][arg_idx] > 0.7:\\n                attr[2] = arg_idx + 1\\n                changed[2] = 1\\n\\n        if (not changed[3]) and (text_id > 2):\\n            # hat\\n            predefined_embeddings = _encode(hat_text)\\n            similarities = user_embeddings @ predefined_embeddings.T\\n            if similarities[0][0] > 0.7:\\n                attr[3] = 1\\n                changed[3] = 1\\n\\n        if (not changed[4]) and (text_id > 2):\\n            # glasses\\n            predefined_embeddings = _encode(eyeglasses_text)\\n            similarities = user_embeddings @ predefined_embeddings.T\\n            arg_idx = torch.argmax(similarities).item()\\n            if similarities[0][arg_idx] > 0.7:\\n                attr[4] = arg_idx + 1\\n                changed[4] = 1\\n\\n        if (not changed[5]) and (text_id > 2):\\n            # belt\\n            predefined_embeddings = _encode(belt_text)\\n            similarities = user_embeddings @ predefined_embeddings.T\\n            arg_idx = torch.argmax(similarities).item()\\n            if similarities[0][arg_idx] > 0.7:\\n                attr[5] = arg_idx + 1\\n                changed[5] = 1\\n\\n        if (not changed[6]) and (text_id == 3):\\n            # outer coverage\\n            predefined_embeddings = _encode(outer_shape_text)\\n            similarities = user_embeddings @ predefined_embeddings.T\\n            arg_idx = torch.argmax(similarities).item()\\n            if similarities[0][arg_idx] > 0.7:\\n                attr[6] = arg_idx\\n                changed[6] = 1\\n\\n        if (not changed[10]) and (text_num == 2 and text_id == 1):\\n            # dress_types\\n            predefined_embeddings = _encode(dress_types)\\n            similarities = user_embeddings @ predefined_embeddings.T\\n            similarity_skirt = user_embeddings @ _encode(skirt_types).T\\n            if similarities[0][0] > 0.5 and similarities[0][\\n                    0] > similarity_skirt[0][0]:\\n                attr[10] = 1\\n                attr[7] = 0\\n                attr[8] = 0\\n                attr[9] = 0\\n                attr[11] = 0\\n                attr[12] = 0\\n\\n                changed[0] = 1\\n                changed[10] = 1\\n                changed[7] = 1\\n                changed[8] = 1\\n                changed[9] = 1\\n                changed[11] = 1\\n                changed[12] = 1\\n\\n        if (not changed[12]) and (text_num == 2 and text_id == 1):\\n            # rompers_types\\n            predefined_embeddings = _encode(rompers_types)\\n            similarities = user_embeddings @ predefined_embeddings.T\\n            max_similarity = torch.max(similarities).item()\\n            if max_similarity > 0.6:\\n                attr[12] = 1\\n                attr[7] = 0\\n                attr[8] = 0\\n                attr[9] = 0\\n                attr[10] = 0\\n                attr[11] = 0\\n\\n                changed[12] = 1\\n                changed[7] = 1\\n                changed[8] = 1\\n                changed[9] = 1\\n                changed[10] = 1\\n                changed[11] = 1\\n\\n        if (not changed[7]) and (text_num > 2 and text_id == 1):\\n            # upper_types\\n            predefined_embeddings = _encode(upper_types)\\n            similarities = user_embeddings @ predefined_embeddings.T\\n            max_similarity = torch.max(similarities).item()\\n            if max_similarity > 0.6:\\n                attr[7] = 1\\n                changed[7] = 1\\n\\n        if (not changed[8]) and (text_id == 3):\\n            # outer_types\\n            predefined_embeddings = _encode(outer_types)\\n            similarities = user_embeddings @ predefined_embeddings.T\\n            arg_idx = torch.argmax(similarities).item()\\n            if similarities[0][arg_idx] > 0.7:\\n                attr[6] = outer_shape_attr[outer_shape_text[arg_idx]]\\n                attr[8] = 1\\n                changed[8] = 1\\n\\n        if (not changed[9]) and (text_num > 2 and text_id == 2):\\n            # skirt_types\\n            predefined_embeddings = _encode(skirt_types)\\n            similarity_skirt = user_embeddings @ predefined_embeddings.T\\n            similarity_dress = user_embeddings @ _encode(dress_types).T\\n            if similarity_skirt[0][0] > 0.7 and similarity_skirt[0][\\n                    0] > similarity_dress[0][0]:\\n                attr[9] = 1\\n                attr[10] = 0\\n                changed[9] = 1\\n                changed[10] = 1\\n\\n        if (not changed[11]) and (text_num > 2 and text_id == 2):\\n            # pant_types\\n            predefined_embeddings = _encode(pant_types)\\n            similarities = user_embeddings @ predefined_embeddings.T\\n            max_similarity = torch.max(similarities).item()\\n            if max_similarity > 0.6:\\n                attr[11] = 1\\n                attr[9] = 0\\n                attr[10] = 0\\n                attr[12] = 0\\n                changed[11] = 1\\n                changed[9] = 1\\n                changed[10] = 1\\n                changed[12] = 1\\n\\n    return human_attr + attr\\n\\n\\ndef generate_texture_attributes(user_text):\\n    parsed_texts = user_text.split(',')\\n\\n    attr = []\\n    for text in parsed_texts:\\n        if ('pure color' in text) or ('solid color' in text):\\n            attr.append(4)\\n        elif ('spline' in text) or ('stripe' in text):\\n            attr.append(3)\\n        elif ('plaid' in text) or ('lattice' in text):\\n            attr.append(5)\\n        elif 'floral' in text:\\n            attr.append(1)\\n        elif 'denim' in text:\\n            attr.append(0)\\n        else:\\n            attr.append(17)\\n\\n    if len(attr) == 1:\\n        attr.append(attr[0])\\n        attr.append(17)\\n\\n    if len(attr) == 2:\\n        attr.append(17)\\n\\n    return attr\\n\\n\\nif __name__ == \\\"__main__\\\":\\n    user_request = input('Enter your request: ')\\n    while user_request != '\\\\\\\\q':\\n        attr = generate_shape_attributes(user_request)\\n        print(attr)\\n        for attr_name, attr_value in zip(attr_names_list, attr):\\n            print(attr_name, attr_value)\\n        user_request = input('Enter your request: ')\\n\"\n    },\n    {\n      \"path\": \"model.py\",\n      \"search\": \"        path = huggingface_hub.hf_hub_download('yumingj/Text2Human_SSHQ',\\n                                               'pretrained_models.zip',\\n                                               use_auth_token=token)\",\n      \"replace\": \"        path = huggingface_hub.hf_hub_download('yumingj/Text2Human_SSHQ',\\n                                               'pretrained_models.zip',\\n                                               token=token)\"\n    }\n  ],\n  \"lock\": {\n    \"date\": \"2022-08-01\",\n    \"python\": \"3.8\"\n  },\n  \"expected_effect\": \"The app no longer imports removed sentence-transformers/huggingface_hub APIs, MiniLM embeddings are computed with transformers, and dependencies are locked to their 2022-era compatible versions so gradio 3.0.17 and torch 1.11.0 run without dependency drift.\"\n}",
   "reasoning": "We need solve. Need provide JSON edits. Need reason deeply. We have Space Text2Human, old stack. Previous attempts: attempt1 pin huggingface_hub 0.11.1 but Spaces installs spaces after requirements and upgrades? Actually attempt1 resulted same huggingface_hub 0.36.2 due transitive? They pinned but pip may upgrade due gradio/spaces. Attempt2 massive lock-ish but got API mismatch likely gradio 3 with newer libs? Need determine best honest fix.\n\nWe need make Space run original functionality. Need likely use lock tool? We can return lock date/python. User explicitly allows lock. We need decide. We can combine code edits. Let's parse problem.\n\nOriginal requirements:\neinops 0.4.1\nlpips 0.1.4\nmmcv-full 1.5.2\nmmsegmentation 0.24.1\nnumpy 1.22.3\nopenmim 0.1.5\nPillow 9.1.1\nsentence-transformers 2.2.0\ntokenizers 0.12.1\ntorch 1.11.0\ntorchvision 0.12.0\ntransformers 4.19.2\nREADME gradio 3.0.17. Python 3.8 Dockerfile fixed. Need model download uses huggingface_hub.hf_hub_download(..., use_auth_token=token). New huggingface_hub removed use_auth_token. Also sentence-transformers 2.2.0 old imports removed APIs. Need fix. Options:\n- Pin huggingface_hub<1.0 and sentence-transformers compatible. But Spaces installs gradio==3.0.17 and spaces latest, which may upgrade huggingface_hub to 0.36? Actually gradio 3.0.17 requires huggingface_hub? Let's check. Gradio 3.0.17 likely requires huggingface_hub<1.0? Pip resolver may allow 0.36? huggingface_hub 0.36 has removed cached_download but gradio 3.0.17 may import? Build log says gradio installed after requirements and can upgrade pins. Attempt1 pinned huggingface_hub==0.11.1 in requirements, but final freeze huggingface_hub==0.36.2! Why? Because perhaps `spaces` package or gradio requires newer huggingface_hub, pip upgrades. Indeed Dockerfile: pip install requirements, then pip install gradio[oauth]==3.0.17 uvicorn spaces. If gradio 3.0.17's metadata allows huggingface_hub>=? maybe >=0.13? It upgraded. Attempt2 pin huggingface-hub==0.21.4 but final? Not shown freeze maybe? It ran app, so huggingface_hub likely 0.21.4? Yet app ran, so maybe sentence-transformers 2.5.1 compatible with 0.21.4? It ran to UI. But API tests failed 404 because gradio version mismatch? Wait rebuild result api has app running local URL and 404 endpoints likely gradio client test uses wrong API? Let's inspect: They tested endpoints fn_index:1 etc and got 404 Not Found. Could be because gradio 3.0.17 app has no such endpoints? Or gradio version in attempt2? They pinned gradio==3.0.17 in requirements, then Dockerfile installs gradio[oauth]==3.0.17 after, okay. Why 404? Maybe because gradio 3.0.17 with fastapi/starlette versions too new? Hmm.\n\nNeed perhaps use lock with appropriate date to get compatible full stack. Since code is old era 2022. We can lock date maybe 2022-06-01 and python 3.8, resolving all dependencies including gradio/sdk. But Spaces Dockerfile installs gradio==3.0.17 and spaces after requirements; if lock requirements includes gradio 3.0.17 and huggingface_hub etc pinned, pip install requirements may install, then pip install \"gradio[oauth]==3.0.17\" ... could still upgrade? If exact pins in requirements and second command same version, likely no upgrade except extras maybe huggingface_hub? gradio[oauth] may require huggingface_hub? But if requirements already has compatible, resolver should keep. spaces latest may require newer huggingface_hub? In lock environment, uv compile excludes newer date, so spaces version selected compatible with that date. But Dockerfile pip install spaces without version could install latest within? Since no --exclude-newer in actual build? Wait lock tool presumably resolves requirements.txt to fully pinned versions using uv pip compile --exclude-newer date --python-version. The Dockerfile still pip install \"gradio[oauth]==3.0.17\" \"uvicorn>=0.14.0\" spaces (unpinned spaces) after. If requirements lock includes spaces version? Dockerfile command includes spaces without version, pip may install latest available on PyPI (today) which could upgrade dependencies and break. But maybe lock tool expects us to not include spaces? Hugging Face Spaces installs SDK after requirements by platform. The migration notes say old gradio 3.x pins can break against newer huggingface_hub etc; either pin transitive or bump sdk_version. We can bump sdk_version? But code uses gradio 3 APIs. Could bump to gradio 4? Need rewrite app.py significantly. Maybe not.\n\nAlternative honest fix: edit code to use new huggingface_hub and sentence-transformers? Could upgrade sentence-transformers to newer version that works with huggingface_hub 0.36 and model all-MiniLM-L6-v2. But model.py uses huggingface_hub.hf_hub_download(... use_auth_token=token). Update to token=token. language_utils imports SentenceTransformer, util. sentence-transformers newer likely API changed? `util.dot_score` exists. `model.encode` returns numpy/tensor? In old code, util.dot_score(user_embeddings, predefined_embeddings) where user_embeddings is numpy? In sentence-transformers 2.2.0, model.encode returns numpy by default, util.dot_score converts? Newer versions maybe default convert_to_tensor? Let's check. Could pin sentence-transformers to a version compatible with huggingface_hub 0.36, e.g. 2.2.2? Actually sentence-transformers 2.2.0 requires huggingface-hub>=0.10? Let's inspect error: util.py line 407 `from huggingface_hub.snapshot_download import REPO_ID_SEPARATOR` removed in 0.36? In huggingface_hub 0.20? Let's recall. `REPO_ID_SEPARATOR` moved? Newer sentence-transformers 2.3+ updated. Could upgrade sentence-transformers to 2.7/3.x, but then transformers/torch compatibility? sentence-transformers 2.7 supports transformers 4.38? Our transformers pinned 4.19.2 maybe conflict. But can upgrade transformers? Model code may break. Need maybe lock.\n\nLet's understand app runtime requirements and model loading. BaseSampleModel uses torch.load without weights_only. torch 1.11.0. If we upgrade torch to 2.x, torch.load default weights_only=True in 2.6, but code can pass? Many torch.load calls. Could pin torch<2.6 via lock date. Need compute budget maybe 4GB? Space CPU basic? Original likely CPU? README no hardware. Model large but runs CPU slowly. Need not GPU. We need fix dependency drift.\n\nBest path likely use lock tool to pin entire old-era dependency set, plus small code edits for API drift that lock cannot solve (huggingface_hub use_auth_token, gradio 3 queue maybe). Need determine lock date. Original Space likely worked around 2022. Requirements pins date. huggingface_hub version originally? sentence-transformers 2.2.0 released 2022-07? Let's check memory: sentence-transformers 2.2.0 released July 2022? huggingface_hub 0.8? Actually 2.2.0 requires huggingface-hub>=0.4.0? Let's not rely. We can choose lock date e.g. 2022-08-01, python 3.8. But Spaces Dockerfile uses python 3.8 already. Lock with python 3.8. Need maybe gradio 3.0.17 existed. `spaces` package maybe not in 2022? The spaces package (gradio spaces) likely newer. But Dockerfile installs spaces (for ZeroGPU?) after requirements. If lock date 2022, spaces may not exist or incompatible. But original Spaces build installed spaces latest today even with old code. Hmm.\n\nMigration notes: \"Spaces whose README pins newest sdk_version get gradio 6...\" Here sdk_version 3.0.17, so gradio 3.0.17. Spaces platform installs gradio and spaces after requirements. The spaces package is platform SDK, maybe latest but supports gradio 3? It is installed by platform regardless. Locking spaces to old date may not be desirable; but lock tool likely can resolve spaces too if included? requirements.txt doesn't include spaces. Dockerfile installs spaces latest today, can upgrade dependencies. However platform's installed spaces probably compatible with current gradio 3? It ran in attempt1/2. So don't lock spaces perhaps. The lock tool replaces requirements.txt only. It may include gradio 3.0.17 and all transitive pins. But Dockerfile second pip install gradio[oauth]==3.0.17 spaces may upgrade if spaces requires newer. Yet if requirements pins huggingface_hub to old version, pip's resolver when installing spaces might either fail or upgrade? In attempt1, despite pin, upgraded. Why? Because perhaps `spaces` requires huggingface_hub>=0.20, forcing upgrade. Then sentence-transformers breaks. So lock date old would choose old spaces? But platform's `pip install spaces` latest today ignores requirements lock? Actually pip install spaces (unpinned) will install latest spaces, which requires newer huggingface_hub, upgrading. So lock alone cannot prevent platform spaces upgrade. Unless requirements include spaces pin and second command's `spaces` resolves to same? pip install spaces with no version will choose latest satisfying already installed? Pip's default behavior: if package already installed and satisfies, it won't upgrade unless --upgrade. `pip install spaces` when spaces already installed (from requirements) should say requirement already satisfied, no upgrade. But if not in requirements, platform installs latest. We can add `spaces` (pinned old?) to requirements. But spaces is platform-specific; pinning arbitrary may be okay. However migration notes say SDK and spaces installed AFTER requirements and can upgrade pins. If requirements includes spaces pin, platform command `pip install ... spaces` sees installed and keeps. So include spaces pin in lock? The lock tool may resolve spaces version by date. But spaces package may not be needed for app. It is installed by platform anyway. Could add `spaces==?` to requirements to lock. But which version? We can pin a version compatible with old stack and gradio 3. Could use lock.\n\nAlternatively avoid sentence-transformers/huggingface_hub new APIs by vendoring? We only use SentenceTransformer('all-MiniLM-L6-v2') and util.dot_score. Could replace with transformers or simple? But original functionality. Could use `sentence_transformers` version compatible. We can pin huggingface_hub to 0.13? Need find version where `cached_download` and `REPO_ID_SEPARATOR` exist. The original sentence-transformers 2.2.0 likely supports huggingface_hub up to? Let's inspect pip metadata maybe not available offline? We can reason. Error in attempt1 with huggingface_hub 0.36: `snapshot_download` module removed `REPO_ID_SEPARATOR`. In huggingface_hub 0.19? Let's search memory:\n- huggingface_hub 0.10? \n- `REPO_ID_SEPARATOR` introduced? \n- `cached_download` deprecated in 0.25? Actually migration note says 1.0 removed cached_download. In 0.36 (pre-1.0?) Wait huggingface_hub 0.36 is before 1.0? It removed cached_download? Migration says \"1.0 removed cached_download\". But error in rebuild says huggingface_hub 0.36.2 ImportError cached_download. Hmm maybe 0.36 already removed? Let's check: huggingface_hub 0.25? cached_download deprecated, 0.34? maybe removed. Sentence-transformers 2.2.0 too old.\n- sentence-transformers 2.2.2? Let's maybe upgrade sentence-transformers to 2.2.2 or 2.3.0 while keeping transformers 4.19? Sentence-transformers 2.3 supports transformers>=4.6,<5, huggingface_hub>=0.10? It may work with huggingface_hub 0.13? But platform spaces may force newer.\n\nCould instead modify code to not use sentence-transformers at all, but use `transformers` AutoModel for all-MiniLM-L6-v2. That's more code but possible. Need model all-MiniLM-L6-v2 from sentence-transformers. Could load via transformers: `AutoTokenizer`, `AutoModel`, then mean pooling. util.dot_score manually cosine/dot. This avoids sentence-transformers and huggingface_hub old API. huggingface_hub still used in model.py for download; update use_auth_token to token. Then no need pin huggingface_hub old except maybe model download works with 0.36. This could be robust on today's libs. Need ensure torch/transformers versions compatible. Original transformers 4.19.2 supports AutoModel for MiniLM? all-MiniLM-L6-v2 is a sentence-transformers model, underlying transformers model `sentence-transformers/all-MiniLM-L6-v2` has config. transformers 4.19 can load. We can implement. But is that \"original functionality intact\"? Yes, same embeddings (should match sentence-transformers default). Need verify pooling: SentenceTransformer default uses mean pooling with attention mask. We can replicate. Could also use `sentence_transformers` if installed newer, but simplest remove dependency and implement. However user asks smallest change. Could be acceptable. But we also need gradio 3 app run on modern transitive deps. Attempt2 with massive pins failed 404 due maybe gradio/fastapi incompatibility. Let's inspect attempt2 logs more: app started successfully, gradio local URL. API tests 404. Could be test harness expects gradio 3 endpoints? Hmm.\n\nMaybe actual issue in attempt2: They pinned gradio==3.0.17 in requirements and Dockerfile installs gradio[oauth]==3.0.17. But also pinned fastapi 0.110.0, starlette 0.36.3, pydantic 2.6.4. Gradio 3.0.17 is incompatible with pydantic 2/fastapi 0.110? Yet app started? Gradio 3.0.17 supports pydantic 1? Let's check: gradio 3.0.17 likely requires pydantic<2, fastapi<0.100. But pip may not enforce? It might still import but runtime weird. Attempt2 app started local URL but endpoints 404 could be due gradio API route changes. So better to lock to old compatible versions.\n\nUse lock tool with date around 2022-07 and python 3.8. This will resolve gradio 3.0.17 with pydantic 1, fastapi 0.78, etc. Need include spaces? We can add `spaces` to requirements? The lock date old may not have spaces package. But platform installs spaces separately. Could omit. The requirements lock will pin huggingface_hub etc. But platform's `pip install spaces` (latest) may upgrade huggingface_hub beyond lock, breaking sentence-transformers. To avoid, update code to not depend on sentence-transformers and huggingface_hub old APIs, or pin huggingface_hub in a way platform spaces won't upgrade? If we include `huggingface-hub==0.13.4` (or whatever) in requirements and `spaces` also in requirements pinned to a version compatible with that huggingface_hub, platform command `pip install ... spaces` sees installed and doesn't upgrade. But spaces latest today may require newer; if pinned old, platform command with no version will keep old because already satisfied. Good. Need know a spaces version compatible with gradio 3 and old huggingface_hub. The `spaces` package maybe first released 2023? Could lock date 2023-06? But code stack old. Let's inspect migration: spaces package version in rebuild freeze 0.46.0. It likely supports gradio 3? The platform installs spaces 0.46.0 today. It may require huggingface_hub>=0.19. Could lock huggingface_hub to 0.19 and sentence-transformers to compatible version. Maybe choose lock date 2023-06-01, python 3.8. Then spaces 0.?? maybe. But requirements doesn't include spaces; platform installs latest (0.46) which is okay if huggingface_hub 0.19? spaces 0.46 may require huggingface_hub>=0.20. Could include spaces==0.19? Need not exact.\n\nAlternative: remove huggingface_hub usage from model.py by using `requests` or `hf_hub_download`? It still huggingface_hub. But can download zip via `huggingface_hub.hf_hub_download` with token; newer huggingface_hub supports token param. Keep. sentence-transformers removed via transformers implementation. Then only huggingface_hub needed for model download. Newer huggingface_hub 0.36 supports hf_hub_download(repo_id, filename, token=...). Good. So no need old huggingface_hub. Then lock can focus on torch/gradio etc. Great.\n\nLet's implement language_utils without sentence-transformers. Need exact embeddings to preserve functionality. SentenceTransformer('all-MiniLM-L6-v2') default. We can use transformers. Need check if `all-MiniLM-L6-v2` tokenizer/model available offline? It will download at runtime from HF; okay. Need token? Public model. Could load with `AutoTokenizer` and `AutoModel`. Mean pooling. `util.dot_score` returns tensor of shape [1, n] if inputs are tensors. In code, `user_embeddings = model.encode(text)` (numpy vector). `predefined_embeddings = model.encode(list)` (numpy matrix). `util.dot_score(user_embeddings, predefined_embeddings)` returns tensor shape [1, n]. Then argmax. We can implement `encode_texts(texts)` returning torch tensor (possibly normalized? SentenceTransformer encode default normalize_embeddings=False. dot_score computes dot product. If we return raw mean pooled embeddings, dot product same. Need ensure dtype float. Good.\n\nBut all-MiniLM-L6-v2 uses mean pooling with attention mask and maybe normalize? SentenceTransformer's pooling module default `pooling_mode_mean_tokens=True`, `pooling_mode_cls_token=False`, etc. It does not normalize by default. So raw mean. Good.\n\nPotential issue: `model = SentenceTransformer(...)` created inside generate_shape_attributes each call. We'll create tokenizer/model each call (maybe heavy but okay). Could cache module global. Use `torch.no_grad()`.\n\nLet's write helper:\n```\nfrom transformers import AutoTokenizer, AutoModel\n...\n_MINILM = None\ndef _get_minilm():\n    global _MINILM\n    if _MINILM is None:\n        tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')\n        model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')\n        model.eval()\n        _MINILM = tokenizer, model\n    return _MINILM\n\ndef _encode(texts):\n    tokenizer, model = _get_minilm()\n    if isinstance(texts, str): texts=[texts]\n    encoded = tokenizer(texts, padding=True, truncation=True, return_tensors='pt')\n    with torch.no_grad():\n        out = model(**encoded)\n    token_embeddings = out.last_hidden_state\n    attention_mask = encoded['attention_mask']\n    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()\n    sum_embeddings = torch.sum(token_embeddings * input_mask_expanded, 1)\n    sum_mask = torch.clamp(input_mask_expanded.sum(1), min=1e-9)\n    return (sum_embeddings / sum_mask)\n```\nThen replace `model = SentenceTransformer(...)` with nothing. Replace `user_embeddings = model.encode(text)` with `user_embeddings = _encode(text)`; `predefined_embeddings = model.encode(list)` with `_encode(list)`. `similarities = util.dot_score(...)` -> `torch.matmul(user_embeddings, predefined_embeddings.T)` or `user_embeddings @ predefined_embeddings.T`. `util.dot_score` returns torch tensor. Good. Remove import sentence_transformers. Remove `from pip import main`? Not needed but harmless. We can remove. Keep numpy/torch.\n\nThen model.py: update `use_auth_token=token` to `token=token`. In newer huggingface_hub, `hf_hub_download(repo_id, filename, token=token)` yes. If token None, okay. Also `hf_hub_download` returns path. Good.\n\nGradio app: uses `gr.Variable()` (removed in gradio 4, but gradio 3 okay). `gr.Image.update`, `gr.Textbox.update`, `gr.Dataset`. `demo.launch(enable_queue=...)`. In gradio 3, enable_queue accepted? Yes. Keep. Need lock to gradio 3 compatible dependencies. Use lock date maybe 2022-08-01? But if we implement transformers, torch 1.11.0, transformers 4.19.2, gradio 3.0.17. Need lock all transitive to compatible versions. We can return lock with date \"2022-08-15\", python \"3.8\". But `spaces` package? We can include `spaces` in requirements? The lock tool will resolve spaces version existing by date. But if spaces package didn't exist in 2022, lock may fail or omit. Platform installs spaces latest anyway. Could avoid adding spaces. The requirements lock will pin huggingface_hub to old version (maybe 0.8?), which is okay for our updated code? model download works with old huggingface_hub too? `token=` param may not exist in very old huggingface_hub (use_auth_token). We changed to token. If lock huggingface_hub 0.8, token param may not exist. Need either support both? Could call `hf_hub_download(..., use_auth_token=token)` to support old. But if platform installs newer huggingface_hub 0.36 due spaces, use_auth_token removed. Conflict. We can make code compatible with both by inspecting? Simpler: use `token=` and pin huggingface_hub>=0.10 where token introduced. Lock date should be after token param introduced. When was `token` param added? huggingface_hub 0.8? Let's recall: `use_auth_token` deprecated in 0.12? `token` added maybe 0.10. Choose date 2023-01-01, python 3.8. Then huggingface_hub ~0.11? token likely exists? Actually huggingface_hub 0.10 has `token`? Let's verify memory: In huggingface_hub 0.10.0, `hf_hub_download` signature has `use_auth_token=None`. `token` maybe added in 0.12. Hmm.\n\nMaybe better code supports both:\n```\ntry:\n    path = huggingface_hub.hf_hub_download(..., token=token)\nexcept TypeError:\n    path = huggingface_hub.hf_hub_download(..., use_auth_token=token)\n```\nBut \"never wrap imports in try/except to hide failures\"; this is runtime API compatibility, acceptable? Could be. But smallest? Could just keep `use_auth_token=token` and pin huggingface_hub<0.25? But spaces may upgrade to 0.36. Hmm.\n\nCould avoid huggingface_hub entirely for model download using `hf_hub_download` from `huggingface_hub` is required. But we can use `requests` to download from HF URL if public: `https://huggingface.co/yumingj/Text2Human_SSHQ/resolve/main/pretrained_models.zip`. If token needed for private? Model repo likely public. `os.getenv('HF_TOKEN')`. We can construct URL and stream download. But that's more code and less original. Or use `huggingface_hub` with compatibility.\n\nGiven complexity, maybe lock entire stack including huggingface_hub to a version where `token`/`use_auth_token` both? At huggingface_hub 0.13, `hf_hub_download` accepts `token`? Let's choose date 2023-06-01. huggingface_hub ~0.15. token param definitely? I think `token` introduced in 0.10 or 0.11. use_auth_token still accepted until 1.0. So both. We can use `token=token` and lock date 2023-06. But gradio 3.0.17 with huggingface_hub 0.15 okay. spaces package? Platform installs spaces 0.46 maybe requires huggingface_hub>=0.19, but if lock pins 0.15, platform `pip install spaces` (latest) may upgrade huggingface_hub, conflicting. But if we include spaces in requirements pinned to version available in 2023 (e.g. 0.16?), platform command sees installed and keeps. Does app need spaces? The Dockerfile installs spaces regardless; if old spaces installed, okay. But `spaces` package maybe provides ZeroGPU decorators not used. Fine.\n\nCould also not include spaces in requirements; platform installs latest spaces, which may upgrade huggingface_hub and torch? spaces 0.46 may require torch? It could upgrade? Let's check migration: spaces package likely lightweight and requires gradio/huggingface_hub. It might force huggingface_hub>=0.19. If our lock huggingface_hub 0.15, pip install spaces latest would upgrade huggingface_hub, but our code now works with 0.36 anyway (hf_hub_download token). So okay. It might upgrade transformers? spaces probably not. So no need old huggingface_hub. Great. We can use token= and allow platform upgrade. sentence-transformers removed. So lock date can be 2023-06 with python 3.8; huggingface_hub gets upgraded by spaces to 0.36, code works. Gradio and torch remain locked? Platform `pip install gradio[oauth]==3.0.17` same version; `spaces` may have dependencies that upgrade torch? spaces likely no torch. So torch stays 1.11.0. Good.\n\nNeed decide lock date. We want all of original pins available and compatible. Original torch 1.11.0, torchvision 0.12.0, transformers 4.19.2, gradio 3.0.17. These existed by 2022-05. Lock date 2022-08-01 would resolve. But our transformers code uses AutoModel; transformers 4.19.2 okay. huggingface_hub upgraded by spaces to 0.36; token works. Good. Let's use lock date \"2022-08-01\", python \"3.8\". The lock tool will pin all. It might choose spaces? Not in requirements. Fine.\n\nBut previous attempt2 used 2024-ish pins and gradio 3 with pydantic 2, bad. Lock to 2022 fixes.\n\nNeed also code edits:\n1. requirements? Lock will replace requirements.txt fully. We don't need edit requirements manually except maybe keep original; lock tool replaces. We can return edits for language_utils.py and model.py. And README? python_version added by lock. We may add `python_version: \"3.8\"`? Lock tool sets python_version in README. We can omit. Could also bump? Keep 3.8.\n\n2. model.py use_auth_token -> token. Let's edit.\n\n3. language_utils rewrite imports and encode. Need exact search/replace. We can replace whole file content? It's large; allowed full content for small? language_utils is ~250 lines, not too huge but maybe use search/replace targeted. We need remove import line and replace model/encode calls. Could do multiple edits. Simpler replace entire file content. The instructions allow full content for small files; language_utils maybe medium but okay. We'll provide content full. Need ensure no accidental changes. We'll reconstruct with helper and replace calls.\n\nLet's carefully edit:\n- Remove `from curses import A_ATTRIBUTES`? It's unused weird import. Could leave? It imports curses, fine. But `from pip import main` unused. We can remove both to clean. Not necessary. We'll remove sentence_transformers import and pip import.\n- Add transformers import? We can implement with transformers. Need ensure transformers installed. Yes.\n- Global cache.\n\nReplace:\n```\nmodel = SentenceTransformer('all-MiniLM-L6-v2')\n```\nremove.\n`user_embeddings = model.encode(text)` -> `user_embeddings = _encode(text)`\n`predefined_embeddings = model.encode(upper_length_text)` -> `_encode(upper_length_text)`\netc. There are several `model.encode(...)` inside. Could sed mentally. Easier full file rewrite. We'll include helper at top and replace all.\n\nNeed maintain `util.dot_score` calls? We replace `util.dot_score(user_embeddings, predefined_embeddings)` with `user_embeddings @ predefined_embeddings.T` or `torch.matmul`. In code some `util.dot_score(user_embeddings, model.encode(skirt_types))` nested; replace with `_encode(skirt_types)`. Need adjust. We'll rewrite entire file carefully.\n\nLet's construct language_utils.py full. Keep all text lists and function. Add:\n```\nfrom transformers import AutoModel, AutoTokenizer\n...\n_MINILM = None\ndef _get_sentence_model():\n    global _MINILM\n    if _MINILM is None:\n        tokenizer = AutoTokenizer.from_pretrained(\"sentence-transformers/all-MiniLM-L6-v2\")\n        model = AutoModel.from_pretrained(\"sentence-transformers/all-MiniLM-L6-v2\")\n        model.eval()\n        _MINILM = tokenizer, model\n    return _MINILM\n\ndef _encode(texts):\n    tokenizer, model = _get_sentence_model()\n    if isinstance(texts, str):\n        texts = [texts]\n    encoded = tokenizer(texts, padding=True, truncation=True, return_tensors=\"pt\")\n    with torch.no_grad():\n        output = model(**encoded)\n    token_embeddings = output.last_hidden_state\n    attention_mask = encoded[\"attention_mask\"]\n    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()\n    sum_embeddings = torch.sum(token_embeddings * input_mask_expanded, dim=1)\n    sum_mask = torch.clamp(input_mask_expanded.sum(dim=1), min=1e-9)\n    return sum_embeddings / sum_mask\n```\nThen in generate_shape_attributes:\n```\ndef generate_shape_attributes(user_shape_texts):\n    parsed_texts = ...\n    ...\n    for ...:\n        user_embeddings = _encode(text)\n        ...\n        predefined_embeddings = _encode(upper_length_text)\n        similarities = user_embeddings @ predefined_embeddings.T\n        ...\n```\nNeed note `torch.argmax(similarities)` where similarities shape [1,n], okay. `similarities[0][arg_idx]`. Fine.\nFor `similarity_skirt = util.dot_score(user_embeddings, model.encode(skirt_types))` replace:\n```\nsimilarity_skirt = user_embeddings @ _encode(skirt_types).T\n```\nand `model.encode(dress_types)` -> `_encode(dress_types)`.\n`torch.max(similarities)` okay.\n\nPotential issue: `_encode` returns tensor requiring grad? model.eval but no_grad, okay.\n\nNow model.py edit.\n\nAlso maybe `PIL.Image.Resampling.LANCZOS` in model.py: Pillow 9.1.1? `PIL.Image.Resampling` introduced in Pillow 9.1? Actually Resampling enum added 9.1.0. Fine. Lock 2022 has Pillow 9.1.1.\n\nPotential issue: `app.py` uses `gr.Variable()` removed in gradio 4, but lock gradio 3 okay.\n\nPotential issue: `model.py` `_download_models` uses `huggingface_hub.hf_hub_download(... token=token)`. In huggingface_hub 0.36, `token` accepted. Good. If token None and repo public, works.\n\nPotential issue: `mmcv-full==1.5.2` wheel requires torch 1.11.0? Lock okay.\n\nLet's return JSON with diagnosis, category, verdict fixable, edits two, lock. Need include lock object. The expected_effect mention. No need edit README? Lock tool sets python_version. Could we also ensure gradio sdk_version remains 3.0.17. Yes.\n\nLet's formulate diagnosis: Root cause is dependency/API drift: old sentence-transformers 2.2.0 imports removed huggingface_hub APIs, and model download uses removed use_auth_token. We removed sentence-transformers dependency by implementing MiniLM encoding with transformers, and updated hf_hub_download to token. Lock dependencies to 2022 era. Good.\n\nNeed be careful: The lock date 2022-08-01 might resolve torch CPU? requirements includes torch==1.11.0 (CPU? original no +cpu). Dockerfile sets PIP_EXTRA_INDEX_URL torch CPU, so pip picks CPU wheel. Good.\n\nLet's output.",
   "usage": {
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   "diagnosis": "The rebuild fails because sentence-transformers 2.2.0 imports huggingface_hub APIs that no longer exist in the installed huggingface_hub (e.g. `from huggingface_hub.snapshot_download import REPO_ID_SEPARATOR` and `cached_download`), and `model.py` uses the removed `use_auth_token=` argument. Replacing the sentence-transformers usage with a direct transformers MiniLM implementation and updating the download call to `token=` removes the incompatible APIs.",
   "category": "dependency-api-drift",
   "verdict": "fixable",
   "lock": {
    "date": "2022-08-01",
    "python": "3.8"
   },
   "diff": "--- a/README.md\n+++ b/README.md\n@@ -1,4 +1,5 @@\n ---\n+python_version: \"3.8\"\n title: Text2Human\n emoji: \ud83c\udfc3\n colorFrom: purple\n--- a/Text2Human/utils/language_utils.py\n+++ b/Text2Human/utils/language_utils.py\n@@ -1,9 +1,6 @@\n-from curses import A_ATTRIBUTES\n-\n import numpy\n import torch\n-from pip import main\n-from sentence_transformers import SentenceTransformer, util\n+from transformers import AutoModel, AutoTokenizer\n \n # predefined shape text\n upper_length_text = [\n@@ -89,9 +86,39 @@\n     '9 skirt', '10 dress', '11 pants', '12 rompers'\n ]\n \n+_MINILM = None\n+\n+\n+def _get_minilm():\n+    global _MINILM\n+    if _MINILM is None:\n+        tokenizer = AutoTokenizer.from_pretrained(\n+            'sentence-transformers/all-MiniLM-L6-v2')\n+        model = AutoModel.from_pretrained(\n+            'sentence-transformers/all-MiniLM-L6-v2')\n+        model.eval()\n+        _MINILM = tokenizer, model\n+    return _MINILM\n+\n+\n+def _encode(texts):\n+    tokenizer, model = _get_minilm()\n+    if isinstance(texts, str):\n+        texts = [texts]\n+    encoded = tokenizer(\n+        texts, padding=True, truncation=True, return_tensors='pt')\n+    with torch.no_grad():\n+        output = model(**encoded)\n+    token_embeddings = output.last_hidden_state\n+    attention_mask = encoded['attention_mask']\n+    input_mask_expanded = attention_mask.unsqueeze(-1).expand(\n+        token_embeddings.size()).float()\n+    sum_embeddings = torch.sum(token_embeddings * input_mask_expanded, dim=1)\n+    sum_mask = torch.clamp(input_mask_expanded.sum(dim=1), min=1e-9)\n+    return sum_embeddings / sum_mask\n+\n \n def generate_shape_attributes(user_shape_texts):\n-    model = SentenceTransformer('all-MiniLM-L6-v2')\n     parsed_texts = user_shape_texts.split(',')\n \n     text_num = len(parsed_texts)\n@@ -101,7 +128,7 @@\n \n     changed = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n     for text_id, text in enumerate(parsed_texts):\n-        user_embeddings = model.encode(text)\n+        user_embeddings = _encode(text)\n         if ('man' in text) and (text_id == 0):\n             human_attr[0] = 0\n             human_attr[1] = 0\n@@ -112,9 +139,8 @@\n \n         if (not changed[0]) and (text_id == 1):\n             # upper length\n-            predefined_embeddings = model.encode(upper_length_text)\n-            similarities = util.dot_score(user_embeddings,\n-                                          predefined_embeddings)\n+            predefined_embeddings = _encode(upper_length_text)\n+            similarities = user_embeddings @ predefined_embeddings.T\n             arg_idx = torch.argmax(similarities).item()\n             attr[0] = upper_length_attr[upper_length_text[arg_idx]]\n             changed[0] = 1\n@@ -122,18 +148,16 @@\n         if (not changed[1]) and ((text_num == 2 and text_id == 1) or\n                                  (text_num > 2 and text_id == 2)):\n             # lower length\n-            predefined_embeddings = model.encode(lower_length_text)\n-            similarities = util.dot_score(user_embeddings,\n-                                          predefined_embeddings)\n+            predefined_embeddings = _encode(lower_length_text)\n+            similarities = user_embeddings @ predefined_embeddings.T\n             arg_idx = torch.argmax(similarities).item()\n             attr[1] = lower_length_attr[lower_length_text[arg_idx]]\n             changed[1] = 1\n \n         if (not changed[2]) and (text_id > 2):\n             # socks length\n-            predefined_embeddings = model.encode(socks_length_text)\n-            similarities = util.dot_score(user_embeddings,\n-                                          predefined_embeddings)\n+            predefined_embeddings = _encode(socks_length_text)\n+            similarities = user_embeddings @ predefined_embeddings.T\n             arg_idx = torch.argmax(similarities).item()\n             if similarities[0][arg_idx] > 0.7:\n                 attr[2] = arg_idx + 1\n@@ -141,18 +165,16 @@\n \n         if (not changed[3]) and (text_id > 2):\n             # hat\n-            predefined_embeddings = model.encode(hat_text)\n-            similarities = util.dot_score(user_embeddings,\n-                                          predefined_embeddings)\n+            predefined_embeddings = _encode(hat_text)\n+            similarities = user_embeddings @ predefined_embeddings.T\n             if similarities[0][0] > 0.7:\n                 attr[3] = 1\n                 changed[3] = 1\n \n         if (not changed[4]) and (text_id > 2):\n             # glasses\n-            predefined_embeddings = model.encode(eyeglasses_text)\n-            similarities = util.dot_score(user_embeddings,\n-                                          predefined_embeddings)\n+            predefined_embeddings = _encode(eyeglasses_text)\n+            similarities = user_embeddings @ predefined_embeddings.T\n             arg_idx = torch.argmax(similarities).item()\n             if similarities[0][arg_idx] > 0.7:\n                 attr[4] = arg_idx + 1\n@@ -160,9 +182,8 @@\n \n         if (not changed[5]) and (text_id > 2):\n             # belt\n-            predefined_embeddings = model.encode(belt_text)\n-            similarities = util.dot_score(user_embeddings,\n-                                          predefined_embeddings)\n+            predefined_embeddings = _encode(belt_text)\n+            similarities = user_embeddings @ predefined_embeddings.T\n             arg_idx = torch.argmax(similarities).item()\n             if similarities[0][arg_idx] > 0.7:\n                 attr[5] = arg_idx + 1\n@@ -170,9 +191,8 @@\n \n         if (not changed[6]) and (text_id == 3):\n             # outer coverage\n-            predefined_embeddings = model.encode(outer_shape_text)\n-            similarities = util.dot_score(user_embeddings,\n-                                          predefined_embeddings)\n+            predefined_embeddings = _encode(outer_shape_text)\n+            similarities = user_embeddings @ predefined_embeddings.T\n             arg_idx = torch.argmax(similarities).item()\n             if similarities[0][arg_idx] > 0.7:\n                 attr[6] = arg_idx\n@@ -180,11 +200,9 @@\n \n         if (not changed[10]) and (text_num == 2 and text_id == 1):\n             # dress_types\n-            predefined_embeddings = model.encode(dress_types)\n-            similarities = util.dot_score(user_embeddings,\n-                                          predefined_embeddings)\n-            similarity_skirt = util.dot_score(user_embeddings,\n-                                              model.encode(skirt_types))\n+            predefined_embeddings = _encode(dress_types)\n+            similarities = user_embeddings @ predefined_embeddings.T\n+            similarity_skirt = user_embeddings @ _encode(skirt_types).T\n             if similarities[0][0] > 0.5 and similarities[0][\n                     0] > similarity_skirt[0][0]:\n                 attr[10] = 1\n@@ -204,9 +222,8 @@\n \n         if (not changed[12]) and (text_num == 2 and text_id == 1):\n             # rompers_types\n-            predefined_embeddings = model.encode(rompers_types)\n-            similarities = util.dot_score(user_embeddings,\n-                                          predefined_embeddings)\n+            predefined_embeddings = _encode(rompers_types)\n+            similarities = user_embeddings @ predefined_embeddings.T\n             max_similarity = torch.max(similarities).item()\n             if max_similarity > 0.6:\n                 attr[12] = 1\n@@ -225,9 +242,8 @@\n \n         if (not changed[7]) and (text_num > 2 and text_id == 1):\n             # upper_types\n-            predefined_embeddings = model.encode(upper_types)\n-            similarities = util.dot_score(user_embeddings,\n-                                          predefined_embeddings)\n+            predefined_embeddings = _encode(upper_types)\n+            similarities = user_embeddings @ predefined_embeddings.T\n             max_similarity = torch.max(similarities).item()\n             if max_similarity > 0.6:\n                 attr[7] = 1\n@@ -235,9 +251,8 @@\n \n         if (not changed[8]) and (text_id == 3):\n             # outer_types\n-            predefined_embeddings = model.encode(outer_types)\n-            similarities = util.dot_score(user_embeddings,\n-                                          predefined_embeddings)\n+            predefined_embeddings = _encode(outer_types)\n+            similarities = user_embeddings @ predefined_embeddings.T\n             arg_idx = torch.argmax(similarities).item()\n             if similarities[0][arg_idx] > 0.7:\n                 attr[6] = outer_shape_attr[outer_shape_text[arg_idx]]\n@@ -246,11 +261,9 @@\n \n         if (not changed[9]) and (text_num > 2 and text_id == 2):\n             # skirt_types\n-            predefined_embeddings = model.encode(skirt_types)\n-            similarity_skirt = util.dot_score(user_embeddings,\n-                                              predefined_embeddings)\n-            similarity_dress = util.dot_score(user_embeddings,\n-                                              model.encode(dress_types))\n+            predefined_embeddings = _encode(skirt_types)\n+            similarity_skirt = user_embeddings @ predefined_embeddings.T\n+            similarity_dress = user_embeddings @ _encode(dress_types).T\n             if similarity_skirt[0][0] > 0.7 and similarity_skirt[0][\n                     0] > similarity_dress[0][0]:\n                 attr[9] = 1\n@@ -260,9 +273,8 @@\n \n         if (not changed[11]) and (text_num > 2 and text_id == 2):\n             # pant_types\n-            predefined_embeddings = model.encode(pant_types)\n-            similarities = util.dot_score(user_embeddings,\n-                                          predefined_embeddings)\n+            predefined_embeddings = _encode(pant_types)\n+            similarities = user_embeddings @ predefined_embeddings.T\n             max_similarity = torch.max(similarities).item()\n             if max_similarity > 0.6:\n                 attr[11] = 1\n--- a/model.py\n+++ b/model.py\n@@ -67,7 +67,7 @@\n         token = os.getenv('HF_TOKEN')\n         path = huggingface_hub.hf_hub_download('yumingj/Text2Human_SSHQ',\n                                                'pretrained_models.zip',\n-                                               use_auth_token=token)\n+                                               token=token)\n         model_dir.mkdir()\n         with zipfile.ZipFile(path) as f:\n             f.extractall(model_dir)\n--- a/requirements.txt\n+++ b/requirements.txt\n@@ -1,12 +1,89 @@\n+addict==2.4.0\n+aiohttp==3.8.1\n+aiosignal==1.2.0\n+analytics-python==1.4.0\n+anyio==3.6.1\n+async-timeout==4.0.2\n+attrs==22.1.0\n+backoff==1.10.0\n+bcrypt==3.2.2\n+certifi==2022.6.15\n+cffi==1.15.1\n+charset-normalizer==2.1.0\n+click==7.1.2\n+colorama==0.4.5\n+cryptography==37.0.4\n+cycler==0.11.0\n einops==0.4.1\n+fastapi==0.79.0\n+ffmpy==0.3.0\n+filelock==3.7.1\n+fonttools==4.34.4\n+frozenlist==1.3.0\n+gradio==3.0.17\n+h11==0.13.0\n+huggingface-hub==0.8.1\n+idna==3.3\n+importlib-metadata==4.12.0\n+jinja2==3.1.2\n+joblib==1.1.0\n+kiwisolver==1.4.4\n+linkify-it-py==1.0.3\n lpips==0.1.4\n+markdown==3.4.1\n+markdown-it-py==2.1.0\n+markupsafe==2.1.1\n+matplotlib==3.5.2\n+mdit-py-plugins==0.3.0\n+mdurl==0.1.1\n+mmcls==0.23.2\n mmcv-full==1.5.2\n mmsegmentation==0.24.1\n+model-index==0.1.11\n+monotonic==1.6\n+multidict==6.0.2\n+nltk==3.7\n numpy==1.22.3\n+opencv-python==4.6.0.66\n openmim==0.1.5\n-Pillow==9.1.1\n+ordered-set==4.1.0\n+orjson==3.7.11\n+packaging==21.3\n+pandas==1.4.3\n+paramiko==2.11.0\n+pillow==9.1.1\n+prettytable==3.3.0\n+pycparser==2.21\n+pycryptodome==3.15.0\n+pydantic==1.9.1\n+pydub==0.25.1\n+pynacl==1.5.0\n+pyparsing==3.0.9\n+python-dateutil==2.8.2\n+python-multipart==0.0.5\n+pytz==2022.1\n+pyyaml==6.0\n+regex==2022.7.25\n+requests==2.28.1\n+scikit-learn==1.1.1\n+scipy==1.9.0\n sentence-transformers==2.2.0\n+sentencepiece==0.1.96\n+six==1.16.0\n+sniffio==1.2.0\n+starlette==0.19.1\n+tabulate==0.8.10\n+threadpoolctl==3.1.0\n tokenizers==0.12.1\n torch==1.11.0\n torchvision==0.12.0\n+tqdm==4.64.0\n transformers==4.19.2\n+typing-extensions==4.3.0\n+uc-micro-py==1.0.1\n+urllib3==1.26.11\n+uvicorn==0.18.2\n+wcwidth==0.2.5\n+yapf==0.32.0\n+yarl==1.8.0\n+zipp==3.8.1\n",
   "stage": "runtime_error",
   "logs": "Found existing installation: mmcv-full 1.5.2\nUninstalling mmcv-full-1.5.2:\n  Successfully uninstalled mmcv-full-1.5.2\ninstalling mmcv-full from wheel.\nLooking in indexes: https://pypi.org/simple, https://download.pytorch.org/whl/cpu\nLooking in links: https://download.openmmlab.com/mmcv/dist/cpu/torch1.11.0/index.html\nCollecting mmcv-full==1.5.2\n  Downloading https://download.openmmlab.com/mmcv/dist/cpu/torch1.11.0/mmcv_full-1.5.2-cp38-cp38-manylinux1_x86_64.whl (21.4 MB)\n     \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 21.4/21.4 MB 14.5 MB/s eta 0:00:00\nRequirement already satisfied: addict in /usr/local/lib/python3.8/site-packages (from mmcv-full==1.5.2) (2.4.0)\nRequirement already satisfied: numpy in /usr/local/lib/python3.8/site-packages (from mmcv-full==1.5.2) (1.22.3)\nRequirement already satisfied: packaging in /usr/local/lib/python3.8/site-packages (from mmcv-full==1.5.2) (21.3)\nRequirement already satisfied: Pillow in /usr/local/lib/python3.8/site-packages (from mmcv-full==1.5.2) (9.1.1)\nRequirement already satisfied: pyyaml in /usr/local/lib/python3.8/site-packages (from mmcv-full==1.5.2) (6.0)\nRequirement already satisfied: yapf in /usr/local/lib/python3.8/site-packages (from mmcv-full==1.5.2) (0.32.0)\nRequirement already satisfied: opencv-python>=3 in /usr/local/lib/python3.8/site-packages (from mmcv-full==1.5.2) (4.6.0.66)\nRequirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.8/site-packages (from packaging->mmcv-full==1.5.2) (3.0.9)\nInstalling collected packages: mmcv-full\nSuccessfully installed mmcv-full-1.5.2\nSuccessfully installed mmcv-full.\npatching file models/hierarchy_inference_model.py\npatching file models/hierarchy_vqgan_model.py\npatching file models/parsing_gen_model.py\npatching file models/sample_model.py\npatching file models/transformer_model.py\npatching file models/vqgan_model.py\ngpu_list:  3\nWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.\nWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.\nTraceback (most recent call last):\n  File \"app.py\", line 158, in <module>\n    main()\n  File \"app.py\", line 55, in main\n    model = Model(args.device)\n  File \"/home/user/app/model.py\", line 53, in __init__\n    self._download_models()\n  File \"/home/user/app/model.py\", line 68, in _download_models\n    path = huggingface_hub.hf_hub_download('yumingj/Text2Human_SSHQ',\nTypeError: hf_hub_download() got an unexpected keyword argument 'token'\n",
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