Create streamlitapp.py

streamlitapp.py

@@ -0,0 +1,43 @@
+from functools import partial
+from pathlib import Path
+
+#import kangas as kg
+import streamlit as st
+import streamlit.components.v1 as components
+from datasets import load_dataset
+import socket
+
+proj_dir = Path(__file__).parent
+
+hostname = socket.gethostname()
+ip_address = socket.gethostbyname(hostname)
+
+servername = 'calebcometml-kangas-demo.hf.space'
+src = f"https://{servername}:80/kangas"
+
+st.set_page_config(layout="wide")
+
+st.markdown("1. Select dataset of your choice")
+
+
+def kangas_fn(dataset_repo):
+    repo_wo_slash = dataset_repo.replace('/', '__') + '.datagrid'
+    dg_file_name = repo_wo_slash + '.datagrid'
+    with st.spinner("Loading Dataset..."):
+        dataset = load_dataset(dataset_repo, split="train")
+    with st.spinner("Creating Kangas..."):
+        dg = kg.DataGrid(dataset)
+    with st.spinner("Saving Kangas..."):
+        dg.save(str(proj_dir / 'datagrids' / dg_file_name))
+#kg.show(
+  #  port=7640,
+  #  host=servername,
+  #  protocol="https"
+#)
+height = st.sidebar.slider("iFrame Height", 200, 1500, 900, 100)
+scrolling = st.sidebar.checkbox("iFrame Scrolling")
+
+hf_dataset = st.text_input("HuggingFace Dataset", value='beans')
+st.button("Download and Run", on_click=partial(kangas_fn, hf_dataset))
+st.markdown("""Click the dropdown in Kangas to see pre-loaded datasets""")
+st.components.v1.iframe(src, None, height, scrolling=True)