app.py

# AUTOGENERATED! DO NOT EDIT! File to edit: app.ipynb.

# %% auto 0
__all__ = ['columns_to_click', 'title', 'description', 'dtypes', 'get_data']

# %% app.ipynb 0
import gradio as gr
import pandas as pd


# %% app.ipynb 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 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 7
    for col in columns_to_click:
        df[col] = df[col].apply(make_clickable_cell)

    return df


# %% 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 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.DataFrame(get_data, datatype=dtypes, every=60)

demo.queue().launch()