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')
data.Year = data.Year.replace(['2025'], 'Q1 2025')

color_discrete_map = {'Direct': '#2ca02c', 'Indirect':  '#1f77b4', 'None':'#d62728'}


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

def generate_figure(org_name):
    org_data = data[data['Organization'] == org_name]
    model_counts = org_data.groupby('Year')[['Model','Environmental Transparency']].value_counts().reset_index()
    model_counts.columns = ['Year', 'Model','Environmental Transparency','Count']
    fig = px.bar(model_counts, x="Year", y="Count",color='Environmental Transparency', color_discrete_map =color_discrete_map, hover_data=["Model"])
    fig.update_layout(xaxis_type='category')
    fig.update_xaxes(categoryorder="category ascending")
    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():
            gr.Markdown("### All Data")
            counts = data.groupby('Year')[['Model','Environmental Transparency']].value_counts().reset_index()
            counts.columns = ['Year', 'Model','Environmental Transparency','Count']
            fig2 = px.bar(counts, x="Year", y="Count",color='Environmental Transparency', color_discrete_map =color_discrete_map, hover_data=["Model"])
            fig2.update_layout(xaxis_type='category')
            fig2.update_xaxes(categoryorder="category ascending")
       
            plt2 = gr.Plot(fig2)
    with gr.Row():
        with gr.Column(scale=1):
            org_choice= gr.Dropdown(organizations, value="", 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)

    org_choice.select(generate_figure, inputs=[org_choice], outputs=[plt])

demo.launch()