app.py

import gradio as gr
import pandas as pd
import plotly.express as px

data = pd.read_csv('data/env_disclosure_data.csv')
data = data.drop('Unnamed: 0', axis=1)
data['Environmental Transparency'] = data['Environmental Transparency'].fillna('None')
data.Organization = data.Organization.replace('University of Montreal / Université de Montréal', 'University of Montreal')
data.Organization = data.Organization.replace('University of Washington,Allen Institute for AI', 'Allen Institute for AI')
data.Organization = data.Organization.replace('Allen Institute for AI,University of Washington', 'Allen Institute for AI')
data.Organization = data.Organization.replace(['Google', 'DeepMind', 'Google DeepMind','Google Brain','Google Research'], 'Alphabet')
data.Organization = data.Organization.replace(['Meta AI','Facebook AI Research','Facebook AI', 'Facebook'], 'Meta')
data.Organization = data.Organization.replace(['Microsoft','Microsoft Research'], 'Microsoft')

organizations=['Alphabet', 'OpenAI', 'Alibaba', 'Stanford University', 'University of Toronto','University of Toronto', 'Microsoft', 'NVIDIA',
               'Carnegie Mellon University (CMU)', 'University of Oxford','University of California (UC) Berkeley','Baidu','Anthropic',
               'Salesforce Research', 'Amazon', 'University of Montreal', 'Apple', 'Mistral AI', 'DeepSeek', 'Allen Institute for AI']

def generate_figure(org_name):
    org_data = data[data['Organization'] == org_name]
    fig = px.histogram(org_data, x="Year", color="Environmental Transparency")
    fig.update_layout(xaxis_type='category')
    return fig

with gr.Blocks() as demo:
    gr.Markdown("# Environmental Transparency Explorer Tool")
    gr.Markdown("## Explore the data from 'Misinformation by Omission: The Need for More Environmental Transparency in AI'")
    with gr.Accordion('Methodology', open=False):
        gr.Markdown('We analyzed Epoch AI\'s "Notable AI Models" dataset, which tracks information on “models that were state of the art, highly cited, \
        or otherwise historically notable” released over time. We selected the time period starting in 2010 as this is the beginning of the modern “deep learning era” \
        (as defined by Epoch AI), which is representative of the types of AI models currently trained and deployed, including all 754 models from 2010 \
        to the first quarter of 2025 in our analysis. We examined the level of environmental impact transparency for each model based on key information \
        from the Epoch AI dataset (e.g., model accessibility, training compute estimation method) as well as from individual model release content \
        (e.g., paper, model card, announcement).')
    with gr.Row():
        with gr.Column(scale=1):
            org_choice= gr.Dropdown(organizations, value="Alphabet", label="Organizations", info="Pick an organization to explore their environmental disclosures", interactive=True)
            gr.Markdown('The 3 transparency categories are:')
            gr.Markdown('**Direct Disclosure**: Developers explicitly reported energy or GHG emissions, e.g., using hardware TDP, country average carbon intensity or measurements.')
            gr.Markdown('**Indirect Disclosure**: Developers provided training compute data or released their model weights, allowing external estimates of training or inference impacts.')
            gr.Markdown('**No Disclosure**: Environmental impact data was not publicly released and estimation approaches (as noted in Indirect Disclosure) were not possible.')
        with gr.Column(scale=4):
            gr.Markdown("### Data by Organization")
            fig = generate_figure(org_choice)
            plt = gr.Plot(fig)
    with gr.Row():
        with gr.Column():
            gr.Markdown("### All Data")
            fig2 = px.histogram(data, x="Year", color="Environmental Transparency")
            fig2.update_layout(xaxis_type='category')
            plt2 = gr.Plot(fig2)
    org_choice.select(generate_figure, inputs=[org_choice], outputs=[plt])

demo.launch()