import os import gradio as gr from huggingface_hub import ModelCard, HfApi from compliance_checks import ( ComplianceSuite, ComplianceCheck, IntendedPurposeCheck, GeneralLimitationsCheck, ComputationalRequirementsCheck, EvaluationCheck, ) hf_writer = gr.HuggingFaceDatasetSaver( os.getenv('HUGGING_FACE_HUB_TOKEN'), organization="society-ethics", dataset_name="model-card-regulatory-check-flags", private=True ) hf_api = HfApi() checks = [ IntendedPurposeCheck(), GeneralLimitationsCheck(), ComputationalRequirementsCheck(), EvaluationCheck(), ] suite = ComplianceSuite(checks=checks) def status_emoji(status: bool): return "✅" if status else "🛑" def search_for_models(query: str): if query.strip() == "": return examples, ",".join([e[0] for e in examples]) models = [m.id for m in list(iter(hf_api.list_models(search=query, limit=10)))] model_samples = [[m] for m in models] models_text = ",".join(models) return model_samples, models_text def load_model_card(index, options_string: str): options = options_string.split(",") model_id = options[index] card = ModelCard.load(repo_id_or_path=model_id).content return card def run_compliance_check(model_card: str): results = suite.run(model_card) return [ *[gr.Accordion.update(label=f"{r.name} - {status_emoji(r.status)}", open=not r.status) for r in results], *[gr.Markdown.update(value=r.to_string()) for r in results], ] def fetch_and_run_compliance_check(model_id: str): model_card = ModelCard.load(repo_id_or_path=model_id).content return run_compliance_check(model_card=model_card) def compliance_result(compliance_check: ComplianceCheck): accordion = gr.Accordion(label=f"{compliance_check.name}", open=False) description = gr.Markdown("Run an evaluation to see results...") return accordion, description def read_file(file_obj): with open(file_obj.name) as f: model_card = f.read() return model_card model_card_box = gr.TextArea(label="Model Card") # Have to destructure everything since I need to delay rendering. col = gr.Column() tab = gr.Tab(label="Results") col2 = gr.Column() compliance_results = [compliance_result(c) for c in suite.checks] compliance_accordions = [c[0] for c in compliance_results] compliance_descriptions = [c[1] for c in compliance_results] examples = [ ["bigscience/bloom"], ["roberta-base"], ["openai/clip-vit-base-patch32"], ["distilbert-base-cased-distilled-squad"], ] with gr.Blocks(css="""\ #file-upload .boundedheight { max-height: 100px; } code { overflow: scroll; } """) as demo: gr.Markdown("""\ # RegCheck AI This Space matches information in [model cards](https://huggingface.co/docs/hub/model-cards) to proposed regulatory \ compliance descriptions in the [EU AI Act](https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex%3A52021PC0206). \ This is a **prototype** to explore the feasibility of automatic checks for compliance, \ and is limited to specific provisions of Article 13 of the Act, “Transparency and \ provision of information to users”. \ **This is research work and NOT a commercial or legal product** To check a model card, first load it by doing any one of the following: - If the model is on the Hugging Face Hub, search for a model and select it from the results. - If you have the model card on your computer as a Markdown file, select the "Upload your own card" tab and click \ "Upload a Markdown file". - Paste your model card's text directly into the "Model Card" text area. """) with gr.Row(): with gr.Column(): with gr.Tab(label="Load a card from the 🤗 Hugging Face Hub"): with gr.Row(): model_id_search = gr.Text(label="Model ID") search_results_text = gr.Text(visible=False, value=",".join([e[0] for e in examples])) search_results_index = gr.Dataset( label="Search Results", components=[model_id_search], samples=examples, type="index", ) model_id_search.change( fn=search_for_models, inputs=[model_id_search], outputs=[search_results_index, search_results_text] ) with gr.Tab(label="Upload your own card"): file = gr.UploadButton(label="Upload a Markdown file", elem_id="file-upload") # TODO: Bug – uploading more than once doesn't trigger the function? Gradio bug? file.upload( fn=read_file, inputs=[file], outputs=[model_card_box] ) model_card_box.render() with col.render(): with tab.render(): with col2.render(): for a, d in compliance_results: with a.render(): d.render() flag = gr.Button(value="Disagree with the result? Click here to flag it! 🚩") flag_message = gr.Text( show_label=False, visible=False, value="Thank you for flagging this! We'll use your report to improve the tool 🤗" ) search_results_index.click( fn=load_model_card, inputs=[search_results_index, search_results_text], outputs=[model_card_box] ) model_card_box.change( fn=run_compliance_check, inputs=[model_card_box], outputs=[*compliance_accordions, *compliance_descriptions] ) flag.click( fn=lambda x: hf_writer.flag(flag_data=[x]) and gr.Text.update(visible=True), inputs=[model_card_box], outputs=[flag_message] ) hf_writer.setup( components=[model_card_box], flagging_dir="flagged" ) demo.launch()