Enable Space to be updated every minute

app.py

@@ -1,7 +1,7 @@
 # AUTOGENERATED! DO NOT EDIT! File to edit: app.ipynb.
 
 # %% auto 0
-__all__ = ['df', 'columns_to_click', 'title', 'description', 'dtypes', 'make_clickable_cell', 'value_func']
+__all__ = ['columns_to_click', 'title', 'description', 'dtypes', 'get_data']
 
 # %% app.ipynb 0
 import gradio as gr
@@ -9,55 +9,54 @@ import pandas as pd
 
 
 # %% app.ipynb 1
-df = pd.read_csv(
-    "https://docs.google.com/spreadsheets/d/e/2PACX-1vSC40sszorOjHfozmNqJT9lFiJhG94u3fbr3Ss_7fzcU3xqqJQuW1Ie_SNcWEB-uIsBi9NBUK7-ddet/pub?output=csv",
-    skiprows=1,
-)
+columns_to_click = ["Paper / Repo", "Selected \nplaygrounds"]
 
+def get_data():
+    df = pd.read_csv(
+        "https://docs.google.com/spreadsheets/d/e/2PACX-1vSC40sszorOjHfozmNqJT9lFiJhG94u3fbr3Ss_7fzcU3xqqJQuW1Ie_SNcWEB-uIsBi9NBUK7-ddet/pub?output=csv",
+        skiprows=1,
+    )
 
-# %% app.ipynb 2
-# Drop footers
-df = df.copy()[~df["Model"].isna()]
 
+    # %% app.ipynb 2
+    # Drop footers
+    df = df.copy()[~df["Model"].isna()]
 
-# %% app.ipynb 3
-# Drop TBA models
-df = df.copy()[df["Parameters \n(B)"] != "TBA"]
 
+    # %% app.ipynb 3
+    # Drop TBA models
+    df = df.copy()[df["Parameters \n(B)"] != "TBA"]
 
-# %% app.ipynb 6
-def make_clickable_cell(cell):
-    if pd.isnull(cell):
-        return ""
-    else:
-        return f'<a  target="_blank" href="{cell}">{cell}</a>'
 
+    # %% app.ipynb 6
+    def make_clickable_cell(cell):
+        if pd.isnull(cell):
+            return ""
+        else:
+            return f'<a  target="_blank" href="{cell}">{cell}</a>'
 
-# %% app.ipynb 7
-columns_to_click = ["Paper / Repo", "Selected \nplaygrounds"]
-for col in columns_to_click:
-    df[col] = df[col].apply(make_clickable_cell)
 
+    # %% app.ipynb 7
+    for col in columns_to_click:
+        df[col] = df[col].apply(make_clickable_cell)
 
-# %% app.ipynb 9
-title = """<h1 align="center">The Large Language Models Landscape</h1>"""
-description = """Large Language Models (LLMs) today come in a variety architectures and capabilities. This interactive landscape provides a visual overview of the most important LLMs, including their training data, size, release date, and whether they are openly accessible or not. It also includes notes on each model to provide additional context. This landscape is derived from data compiled by Dr. Alan D. Thompson at [lifearchitect.ai/models](https://lifearchitect.ai/models/).
-"""
+    return df
 
 
-# %% app.ipynb 10
-dtypes = ["str" if c not in columns_to_click else "markdown" for c in df.columns]
+# %% app.ipynb 2
+title = """<h1 align="center">The Large Language Models Landscape</h1>"""
+description = """Large Language Models (LLMs) today come in a variety architectures and capabilities. This interactive landscape provides a visual overview of the most important LLMs, including their training data, size, release date, and whether they are openly accessible or not. It also includes notes on each model to provide additional context. This landscape is derived from data compiled by Dr. Alan D. Thompson at [lifearchitect.ai](https://lifearchitect.ai).
+"""
 
 
-# %% app.ipynb 11
-def value_func():
-    return df
+# %% app.ipynb 3
+dtypes = ["str" if c not in columns_to_click else "markdown" for c in get_data().columns]
 
 
+# %% app.ipynb 4
 with gr.Blocks() as demo:
     gr.Markdown(title)
     gr.Markdown(description)
-    gr.components.DataFrame(value=value_func, datatype=dtypes)
-
-demo.launch()
+    gr.DataFrame(get_data, datatype=dtypes, every=60)
 
+demo.queue().launch()