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| import streamlit as st | |
| from huggingface_hub import DatasetFilter, HfApi, ModelFilter | |
| api = HfApi() | |
| def get_metadata(dataset_name): | |
| filt = DatasetFilter(dataset_name=dataset_name) | |
| data = api.list_datasets(filter=filt, full=True) | |
| return data[0].cardData["train-eval-index"] | |
| def get_compatible_models(task, dataset_name): | |
| filt = ModelFilter(task=task, trained_dataset=dataset_name) | |
| compatible_models = api.list_models(filter=filt) | |
| return [model.modelId for model in compatible_models] | |
| with st.form(key="form"): | |
| dataset_name = st.selectbox("Select a dataset to evaluate on", ["lewtun/autoevaluate_emotion"]) | |
| metadata = get_metadata(dataset_name) | |
| compatible_models = get_compatible_models(metadata[0]["task"], dataset_name.split("/")[-1].split("_")[-1]) | |
| options = st.multiselect("Select the models you wish to evaluate", compatible_models) | |
| submit_button = st.form_submit_button("Make Submission") | |
| if submit_button: | |
| st.success(f"✅ Evaluation was successfully submitted for evaluation with job ID ") | |