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Runtime error
Runtime error
Commit
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7fe8d4e
1
Parent(s):
324f080
Add missing files
Browse files- .gitignore +3 -0
- requirements.txt +8 -0
- sections/abstract.md +0 -0
- sections/acknowledgements.md +0 -0
- sections/caveats.md +0 -0
- sections/challenges.md +0 -0
- sections/pretraining.md +0 -0
- sections/references.md +0 -0
- sections/social_impact.md +0 -0
- sections/usage.md +0 -0
- session.py +89 -0
- utils.py +1 -18
.gitignore
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*mic_env/*
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**__pycache__**
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*.pyc
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requirements.txt
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plotly==5.1.0
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streamlit==0.84.1
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git+https://github.com/huggingface/transformers.git
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torchvision==0.10.0
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mtranslate==1.8
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black==21.7b0
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flax==0.3.4
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sentencepiece==0.1.96
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sections/abstract.md
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sections/acknowledgements.md
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sections/caveats.md
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sections/challenges.md
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sections/pretraining.md
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sections/references.md
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sections/social_impact.md
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sections/usage.md
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session.py
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#
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# Code for managing session state, which is needed for multi-input forms
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# See https://github.com/streamlit/streamlit/issues/1557
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#
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# This code is taken from
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# https://gist.github.com/okld/0aba4869ba6fdc8d49132e6974e2e662
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#
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from streamlit.hashing import _CodeHasher
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from streamlit.report_thread import get_report_ctx
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from streamlit.server.server import Server
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class _SessionState:
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def __init__(self, session, hash_funcs):
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"""Initialize SessionState instance."""
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self.__dict__["_state"] = {
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"data": {},
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"hash": None,
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"hasher": _CodeHasher(hash_funcs),
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"is_rerun": False,
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"session": session,
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}
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def __call__(self, **kwargs):
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"""Initialize state data once."""
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for item, value in kwargs.items():
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if item not in self._state["data"]:
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self._state["data"][item] = value
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def __getitem__(self, item):
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"""Return a saved state value, None if item is undefined."""
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return self._state["data"].get(item, None)
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def __getattr__(self, item):
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"""Return a saved state value, None if item is undefined."""
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return self._state["data"].get(item, None)
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def __setitem__(self, item, value):
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"""Set state value."""
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self._state["data"][item] = value
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def __setattr__(self, item, value):
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"""Set state value."""
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self._state["data"][item] = value
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def clear(self):
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"""Clear session state and request a rerun."""
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self._state["data"].clear()
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self._state["session"].request_rerun()
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def sync(self):
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"""
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Rerun the app with all state values up to date from the beginning to
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fix rollbacks.
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"""
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data_to_bytes = self._state["hasher"].to_bytes(self._state["data"], None)
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# Ensure to rerun only once to avoid infinite loops
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# caused by a constantly changing state value at each run.
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#
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# Example: state.value += 1
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if self._state["is_rerun"]:
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self._state["is_rerun"] = False
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elif self._state["hash"] is not None:
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if self._state["hash"] != data_to_bytes:
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self._state["is_rerun"] = True
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self._state["session"].request_rerun()
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self._state["hash"] = data_to_bytes
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def _get_session():
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session_id = get_report_ctx().session_id
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session_info = Server.get_current()._get_session_info(session_id)
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if session_info is None:
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raise RuntimeError("Couldn't get your Streamlit Session object.")
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return session_info.session
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def _get_state(hash_funcs=None):
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session = _get_session()
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if not hasattr(session, "_custom_session_state"):
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session._custom_session_state = _SessionState(session, hash_funcs)
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return session._custom_session_state
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utils.py
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@@ -3,7 +3,6 @@ import torch
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import numpy as np
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from torchvision.transforms import CenterCrop, ConvertImageDtype, Normalize, Resize
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from torchvision.transforms.functional import InterpolationMode
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from transformers import MBart50TokenizerFast
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from PIL import Image
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transform = Transform(224)
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def get_transformed_image(image):
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if image.shape[-1] == 3 and isinstance(image, np.ndarray):
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image = image.transpose(2, 0, 1)
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image = torch.tensor(image)
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return transform(image).unsqueeze(0).permute(0, 2, 3, 1).numpy()
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tokenizer = MBart50TokenizerFast.from_pretrained("facebook/mbart-large-50")
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language_mapping = {
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"english": "en_XX",
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"german": "de_DE",
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"french": "fr_XX",
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"spanish": "es_XX"
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}
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def generate_sequence(model, pixel_values, lang_code):
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lang_code = language_mapping[lang_code]
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output_ids = model.generate(input_ids=pixel_values, decoder_start_token_id=tokenizer.lang_code_to_id[lang_code], max_length=64, num_beams=4)
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output_sequence = tokenizer.batch_decode(output_ids[0], skip_special_tokens=True, max_length=64)
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return output_sequence
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import numpy as np
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from torchvision.transforms import CenterCrop, ConvertImageDtype, Normalize, Resize
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from torchvision.transforms.functional import InterpolationMode
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from PIL import Image
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transform = Transform(224)
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def get_transformed_image(image):
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if image.shape[-1] == 3 and isinstance(image, np.ndarray):
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image = image.transpose(2, 0, 1)
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image = torch.tensor(image)
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return transform(image).unsqueeze(0).permute(0, 2, 3, 1).numpy()
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