|
from yaml import load |
|
from persist import persist, load_widget_state |
|
import streamlit as st |
|
from io import StringIO |
|
import tempfile |
|
from pathlib import Path |
|
import requests |
|
from huggingface_hub import hf_hub_download, upload_file |
|
import pandas as pd |
|
from huggingface_hub import create_repo |
|
import os |
|
from middleMan import parse_into_jinja_markdown as pj |
|
|
|
|
|
|
|
@st.cache |
|
def get_cached_data(): |
|
languages_df = pd.read_html("https://hf.co/languages")[0] |
|
languages_map = pd.Series(languages_df["Language"].values, index=languages_df["ISO code"]).to_dict() |
|
|
|
license_df = pd.read_html("https://huggingface.co/docs/hub/repositories-licenses")[0] |
|
license_map = pd.Series( |
|
license_df["License identifier (to use in model card)"].values, index=license_df.Fullname |
|
).to_dict() |
|
|
|
available_metrics = [x['id'] for x in requests.get('https://huggingface.co/api/metrics').json()] |
|
|
|
r = requests.get('https://huggingface.co/api/models-tags-by-type') |
|
tags_data = r.json() |
|
libraries = [x['id'] for x in tags_data['library']] |
|
tasks = [x['id'] for x in tags_data['pipeline_tag']] |
|
return languages_map, license_map, available_metrics, libraries, tasks |
|
|
|
|
|
def card_upload(card_info,repo_id,token): |
|
|
|
repo_type = "space" |
|
commit_description=None, |
|
revision=None, |
|
create_pr=None |
|
with tempfile.TemporaryDirectory() as tmpdir: |
|
tmp_path = Path(tmpdir) / "README.md" |
|
tmp_path.write_text(str(card_info)) |
|
url = upload_file( |
|
path_or_fileobj=str(tmp_path), |
|
path_in_repo="README.md", |
|
repo_id=repo_id, |
|
token=token, |
|
repo_type=repo_type, |
|
identical_ok=True, |
|
revision=revision, |
|
) |
|
return url |
|
|
|
def validate(self, repo_type="model"): |
|
"""Validates card against Hugging Face Hub's model card validation logic. |
|
Using this function requires access to the internet, so it is only called |
|
internally by `modelcards.ModelCard.push_to_hub`. |
|
Args: |
|
repo_type (`str`, *optional*): |
|
The type of Hugging Face repo to push to. Defaults to None, which will use |
|
use "model". Other options are "dataset" and "space". |
|
""" |
|
if repo_type is None: |
|
repo_type = "model" |
|
|
|
|
|
if repo_type not in ["model", "space", "dataset"]: |
|
raise RuntimeError( |
|
"Provided repo_type '{repo_type}' should be one of ['model', 'space'," |
|
" 'dataset']." |
|
) |
|
|
|
body = { |
|
"repoType": repo_type, |
|
"content": str(self), |
|
} |
|
headers = {"Accept": "text/plain"} |
|
|
|
try: |
|
r = requests.post( |
|
"https://huggingface.co/api/validate-yaml", body, headers=headers |
|
) |
|
r.raise_for_status() |
|
except requests.exceptions.HTTPError as exc: |
|
if r.status_code == 400: |
|
raise RuntimeError(r.text) |
|
else: |
|
raise exc |
|
|
|
|
|
|
|
def save_uploadedfile(uploadedfile): |
|
with open(os.path.join("temp_uploaded_filed_Dir",uploadedfile.name),"wb") as f: |
|
f.write(uploadedfile.getbuffer()) |
|
st.success("Saved File:{} to temp_uploaded_filed_Dir".format(uploadedfile.name)) |
|
return uploadedfile.name |
|
|
|
|
|
def main_page(): |
|
|
|
|
|
if "model_name" not in st.session_state: |
|
|
|
st.session_state.update({ |
|
"input_model_name": "", |
|
"languages": [], |
|
"license": "", |
|
"library_name": "", |
|
"datasets": "", |
|
"metrics": [], |
|
"task": "", |
|
"tags": "", |
|
"model_description": "Some cool model...", |
|
"the_authors":"", |
|
"Shared_by":"", |
|
"Model_details_text": "", |
|
"Model_developers": "", |
|
"blog_url":"", |
|
"Parent_Model_url":"", |
|
"Parent_Model_name":"", |
|
|
|
"Model_how_to": "", |
|
|
|
"Model_uses": "", |
|
"Direct_Use": "", |
|
"Downstream_Use":"", |
|
"Out-of-Scope_Use":"", |
|
|
|
"Model_Limits_n_Risks": "", |
|
"Recommendations":"", |
|
|
|
"training_Data": "", |
|
"model_preprocessing":"", |
|
"Speeds_Sizes_Times":"", |
|
|
|
|
|
|
|
"Model_Eval": "", |
|
"Testing_Data":"", |
|
"Factors":"", |
|
"Metrics":"", |
|
"Model_Results":"", |
|
|
|
"Model_c02_emitted": "", |
|
"Model_hardware":"", |
|
"hours_used":"", |
|
"Model_cloud_provider":"", |
|
"Model_cloud_region":"", |
|
|
|
"Model_cite": "", |
|
"paper_url": "", |
|
"github_url": "", |
|
"bibtex_citation": "", |
|
"APA_citation":"", |
|
|
|
"Model_examin":"", |
|
"Model_card_contact":"", |
|
"Model_card_authors":"", |
|
"Glossary":"", |
|
"More_info":"", |
|
|
|
"Model_specs":"", |
|
"compute_infrastructure":"", |
|
"technical_specs_software":"", |
|
|
|
"check_box": bool, |
|
"markdown_upload":" ", |
|
"legal_view":bool, |
|
"researcher_view":bool, |
|
"beginner_technical_view":bool, |
|
"markdown_state":"", |
|
}) |
|
|
|
languages_map, license_map, available_metrics, libraries, tasks = get_cached_data() |
|
|
|
|
|
st.header("Model Card Form") |
|
|
|
warning_placeholder = st.empty() |
|
|
|
st.text_input("Model Name", key=persist("model_name")) |
|
st.text_area("Model Description", help="The model description provides basic details about the model. This includes the architecture, version, if it was introduced in a paper, if an original implementation is available, the author, and general information about the model. Any copyright should be attributed here. General information about training procedures, parameters, and important disclaimers can also be mentioned in this section.", key=persist('model_description')) |
|
st.multiselect("Language(s)", list(languages_map), format_func=lambda x: languages_map[x], help="The language(s) associated with this model. If this is not a text-based model, you should specify whatever language that is used in the dataset. For instance, if the dataset's labels are in english, you should select English here.", key=persist("languages")) |
|
st.selectbox("License", [""] + list(license_map.values()), help="The license associated with this model.", key=persist("license")) |
|
st.selectbox("Library Name", [""] + libraries, help="The name of the library this model came from (Ex. pytorch, timm, spacy, keras, etc.). This is usually automatically detected in model repos, so it is not required.", key=persist('library_name')) |
|
st.text_input("Parent Model (URL)", help="If this model has another model as its base, please provide the URL link to the parent model", key=persist("Parent_Model_name")) |
|
st.text_input("Datasets (comma separated)", help="The dataset(s) used to train this model. Use dataset id from https://hf.co/datasets.", key=persist("datasets")) |
|
st.multiselect("Metrics", available_metrics, help="Metrics used in the training/evaluation of this model. Use metric id from https://hf.co/metrics.", key=persist("metrics")) |
|
st.selectbox("Task", [""] + tasks, help="What task does this model aim to solve?", key=persist('task')) |
|
st.text_input("Tags (comma separated)", help="Additional tags to add which will be filterable on https://hf.co/models. (Ex. image-classification, vision, resnet)", key=persist("tags")) |
|
st.text_input("Author(s) (comma separated)", help="The authors who developed this model. If you trained this model, the author is you.", key=persist("the_authors")) |
|
st.text_input("Related Research Paper", help="Research paper related to this model.", key=persist("paper_url")) |
|
st.text_input("Related GitHub Repository", help="Link to a GitHub repository used in the development of this model", key=persist("github_url")) |
|
st.text_area("Bibtex Citation", help="Bibtex citations for related work", key=persist("bibtex_citations")) |
|
st.text_input("Carbon Emitted:", help="You can estimate carbon emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700)", key=persist("Model_c02_emitted")) |
|
|
|
|
|
|
|
|
|
languages=st.session_state.languages or None |
|
license=st.session_state.license or None |
|
task = st.session_state.task or None |
|
markdown_upload = st.session_state.markdown_upload |
|
|
|
|
|
do_warn = False |
|
warning_msg = "Warning: The following fields are required but have not been filled in: " |
|
if not languages: |
|
warning_msg += "\n- Languages" |
|
do_warn = True |
|
if not license: |
|
warning_msg += "\n- License" |
|
do_warn = True |
|
if not task or not markdown_upload: |
|
warning_msg += "\n- Please choose a task or upload a model card" |
|
do_warn = True |
|
if do_warn: |
|
warning_placeholder.error(warning_msg) |
|
|
|
with st.sidebar: |
|
|
|
|
|
|
|
|
|
st.markdown("## Upload Model Card") |
|
|
|
st.markdown("#### Model Card must be in markdown (.md) format.") |
|
|
|
|
|
uploaded_file = st.file_uploader("Choose a file", type = ['md'], help = 'Please choose a markdown (.md) file type to upload') |
|
if uploaded_file is not None: |
|
|
|
file_details = {"FileName":uploaded_file.name,"FileType":uploaded_file.type} |
|
name_of_uploaded_file = save_uploadedfile(uploaded_file) |
|
|
|
st.session_state.markdown_upload = name_of_uploaded_file |
|
|
|
elif st.session_state.task =='fill-mask' or 'translation' or 'token-classification' or ' sentence-similarity' or 'summarization' or 'question-answering' or 'text2text-generation' or 'text-classification' or 'text-generation' or 'conversational': |
|
|
|
|
|
|
|
st.session_state.markdown_upload = "language_model_template1.md" |
|
|
|
elif st.session_state.task: |
|
|
|
st.session_state.markdown_upload = "current_card.md" |
|
|
|
|
|
|
|
|
|
out_markdown =open( st.session_state.markdown_upload, "r+" |
|
).read() |
|
print_out_final = f"{out_markdown}" |
|
st.markdown("## Export Loaded Model Card to Hub") |
|
with st.form("Upload to 🤗 Hub"): |
|
st.markdown("Use a token with write access from [here](https://hf.co/settings/tokens)") |
|
token = st.text_input("Token", type='password') |
|
repo_id = st.text_input("Repo ID") |
|
submit = st.form_submit_button('Upload to 🤗 Hub', help='The current model card will be uploaded to a branch in the supplied repo ') |
|
|
|
if submit: |
|
if len(repo_id.split('/')) == 2: |
|
repo_url = create_repo(repo_id, exist_ok=True, token=token) |
|
new_url = card_upload(pj(),repo_id, token=token) |
|
st.success(f"Pushed the card to the repo [here]({new_url})!") |
|
else: |
|
st.error("Repo ID invalid. It should be username/repo-name. For example: nateraw/food") |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
st.markdown("## Download current Model Card") |
|
|
|
if st.session_state.model_name is None or st.session_state.model_name== ' ': |
|
downloaded_file_name = 'current_model_card.md' |
|
else: |
|
downloaded_file_name = st.session_state.model_name+'_'+'model_card.md' |
|
download_status = st.download_button(label = 'Download Model Card', data = pj(), file_name = downloaded_file_name, help = "The current model card will be downloaded as a markdown (.md) file") |
|
if download_status == True: |
|
st.success("Your current model card, successfully downloaded 🤗") |
|
|
|
|
|
def page_switcher(page): |
|
st.session_state.runpage = page |
|
|
|
def main(): |
|
|
|
st.header("About Model Cards") |
|
st.markdown(Path('about.md').read_text(), unsafe_allow_html=True) |
|
btn = st.button('Create a Model Card 📝',on_click=page_switcher,args=(main_page,)) |
|
if btn: |
|
st.experimental_rerun() |
|
|
|
if __name__ == '__main__': |
|
load_widget_state() |
|
if 'runpage' not in st.session_state : |
|
st.session_state.runpage = main |
|
st.session_state.runpage() |
|
|
