Add basic functionality for datasets and models

app.py

@@ -1,3 +1,32 @@
 import streamlit as st
+from huggingface_hub import DatasetFilter, HfApi, ModelFilter
 
-st.write("Hello world!")
+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 ")

requirements.txt

@@ -0,0 +1 @@
+huggingface-hub==0.4.0