app.py

import os, glob
import json
from datetime import datetime, timezone
from dataclasses import dataclass
from datasets import load_dataset, Dataset
import pandas as pd
import gradio as gr
from huggingface_hub import HfApi, snapshot_download, ModelInfo, list_models
from enum import Enum

OWNER = "AIEnergyScore"
COMPUTE_SPACE = f"{OWNER}/launch-computation-example"
TOKEN = os.environ.get("DEBUG")
API = HfApi(token=TOKEN)

task_mappings = {
    'automatic speech recognition': 'automatic-speech-recognition',
    'Object Detection': 'object-detection',
    'Text Classification': 'text-classification',
    'Image to Text': 'image-to-text',
    'Question Answering': 'question-answering',
    'Text Generation': 'text-generation',
    'Image Classification': 'image-classification',
    'Sentence Similarity': 'sentence-similarity',
    'Image Generation': 'image-generation',
    'Summarization': 'summarization'
}

@dataclass
class ModelDetails:
    name: str
    display_name: str = ""
    symbol: str = ""  # emoji

def start_compute_space():
    API.restart_space(COMPUTE_SPACE)
    gr.Info(f"Okay! {COMPUTE_SPACE} should be running now!")

def get_model_size(model_info: ModelInfo):
    """Gets the model size from the configuration, or the model name if the configuration does not contain the information."""
    try:
        model_size = round(model_info.safetensors["total"] / 1e9, 3)
    except (AttributeError, TypeError):
        return 0  # Unknown model sizes are indicated as 0
    return model_size

def add_docker_eval(zip_file):
    new_fid_list = zip_file.split("/")
    new_fid = new_fid_list[-1]
    if new_fid.endswith('.zip'):
        API.upload_file(
            path_or_fileobj=zip_file,
            repo_id="AIEnergyScore/tested_proprietary_models",
            path_in_repo='submitted_models/' + new_fid,
            repo_type="dataset",
            commit_message="Adding logs via submission Space.",
            token=TOKEN
        )
        gr.Info('Uploaded logs to dataset! We will validate their validity and add them to the next version of the leaderboard.')
    else:
        gr.Info('You can only upload .zip files here!')

def add_new_eval(repo_id: str, task: str):
    model_owner = repo_id.split("/")[0]
    model_name = repo_id.split("/")[1]
    current_time = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
    requests = load_dataset("AIEnergyScore/requests_debug", split="test", token=TOKEN)
    requests_dset = requests.to_pandas()
    model_list = requests_dset[requests_dset['status'] == 'COMPLETED']['model'].tolist()
    task_models = list(API.list_models(filter=task_mappings[task]))
    task_model_names = [m.id for m in task_models]
    if repo_id in model_list:
        gr.Info('This model has already been run!')
    elif repo_id not in task_model_names:
        gr.Info("This model isn't compatible with the chosen task! Pick a different model-task combination")
    else:
        try:
            model_info = API.model_info(repo_id=repo_id)
            model_size = get_model_size(model_info=model_info)
            likes = model_info.likes
        except Exception:
            gr.Info("Could not find information for model %s" % (model_name))
            model_size = None
            likes = None

        gr.Info("Adding request")
        request_dict = {
            "model": repo_id,
            "status": "PENDING",
            "submitted_time": pd.to_datetime(current_time),
            "task": task_mappings[task],
            "likes": likes,
            "params": model_size,
            "leaderboard_version": "v0",
        }
        print("Writing out request file to dataset")
        df_request_dict = pd.DataFrame([request_dict])
        print(df_request_dict)
        df_final = pd.concat([requests_dset, df_request_dict], ignore_index=True)
        updated_dset = Dataset.from_pandas(df_final)
        updated_dset.push_to_hub("AIEnergyScore/requests_debug", split="test", token=TOKEN)
        gr.Info("Starting compute space at %s " % COMPUTE_SPACE)
        return start_compute_space()

def print_existing_models():
    requests = load_dataset("AIEnergyScore/requests_debug", split="test", token=TOKEN)
    requests_dset = requests.to_pandas()
    model_df = requests_dset[['model', 'status']]
    model_df = model_df[model_df['status'] == 'COMPLETED']
    return model_df

def highlight_cols(x):
    df = x.copy()
    df[df['status'] == 'COMPLETED'] = 'color: green'
    df[df['status'] == 'PENDING'] = 'color: orange'
    df[df['status'] == 'FAILED'] = 'color: red'
    return df

# Applying the style function for the table
existing_models = print_existing_models()
formatted_df = existing_models.style.apply(highlight_cols, axis=None)

def get_leaderboard_models():
    path = r'leaderboard_v0_data/energy'
    filenames = glob.glob(path + "/*.csv")
    data = []
    for filename in filenames:
        data.append(pd.read_csv(filename))
    leaderboard_data = pd.concat(data, ignore_index=True)
    return leaderboard_data[['model', 'task']]

def get_zip_data_link():
    return (
        '<a href="https://example.com/download.zip" '
        'style="text-decoration: none; font-weight: bold; font-size: 1.1em; '
        'color: black; font-family: \'Inter\', sans-serif;">Download Logs</a>'
    )

with gr.Blocks() as demo:
    # --- Custom CSS for layout and styling ---
    gr.HTML('''
    <style>
      /* Evenly space the header links */
      .header-links {
          display: flex;
          justify-content: space-evenly;
          align-items: center;
          margin: 10px 0;
      }
      /* Center the subtitle text */
      .centered-subtitle {
          text-align: center;
          font-size: 1.2em;
          margin-bottom: 20px;
      }
      /* Full width container for matching widget edges */
      .full-width {
          width: 100% !important;
      }
    </style>
    ''')

    # --- Header Links (at the very top) ---
    with gr.Row(elem_classes="header-links"):
        submission_link = gr.HTML(
            '<a href="https://huggingface.co/spaces/AIEnergyScore/submission_portal" '
            'style="text-decoration: none; font-weight: bold; font-size: 1.1em; '
            'color: black; font-family: \'Inter\', sans-serif;">Submission Portal</a>'
        )
        label_link = gr.HTML(
            '<a href="https://huggingface.co/spaces/AIEnergyScore/Label" '
            'style="text-decoration: none; font-weight: bold; font-size: 1.1em; '
            'color: black; font-family: \'Inter\', sans-serif;">Label Generator</a>'
        )
        faq_link = gr.HTML(
            '<a href="https://huggingface.github.io/AIEnergyScore/#faq" '
            'style="text-decoration: none; font-weight: bold; font-size: 1.1em; '
            'color: black; font-family: \'Inter\', sans-serif;">FAQ</a>'
        )
        documentation_link = gr.HTML(
            '<a href="https://huggingface.github.io/AIEnergyScore/#documentation" '
            'style="text-decoration: none; font-weight: bold; font-size: 1.1em; '
            'color: black; font-family: \'Inter\', sans-serif;">Documentation</a>'
        )
        download_link = gr.HTML(get_zip_data_link())
        community_link = gr.HTML(
            '<a href="https://huggingface.co/spaces/AIEnergyScore/README/discussions" '
            'style="text-decoration: none; font-weight: bold; font-size: 1.1em; '
            'color: black; font-family: \'Inter\', sans-serif;">Community</a>'
        )
    
    # --- Logo (centered) ---
    gr.HTML('''
    <div style="margin-top: 0px;">
        <img src="https://huggingface.co/spaces/AIEnergyScore/Leaderboard/resolve/main/logo.png" 
             alt="Logo" 
             style="display: block; margin: 0 auto; max-width: 400px; height: auto;">
    </div>
    ''')

    # --- Subtitle (centered) ---
    gr.Markdown('<p class="centered-subtitle">Welcome to the AI Energy Score Leaderboard. Select the task to see scored model results.</p>')

    # --- Main UI Container (ensuring matching edges) ---
    with gr.Column(elem_classes="full-width"):
        with gr.Row():
            with gr.Column():
                task = gr.Dropdown(
                    choices=list(task_mappings.keys()),
                    label="Choose a benchmark task",
                    value='Text Generation',
                    multiselect=False,
                    interactive=True,
                )
            with gr.Column():
                model_name_textbox = gr.Textbox(label="Model name (user_name/model_name)")
        
        with gr.Row():
            with gr.Column():
                submit_button = gr.Button("Submit for Analysis")
                submission_result = gr.Markdown()
                submit_button.click(
                    fn=add_new_eval,
                    inputs=[model_name_textbox, task],
                    outputs=submission_result,
                )
        
        # --- Docker Log Submission (Simplified) ---
        with gr.Accordion("Submit log files from a Docker run:", open=False):
            gr.Markdown("""
            **⚠️ Warning: By uploading the zip file, you confirm that you have read and agree to the following terms:**

            - **Public Data Sharing:** You consent to the public sharing of the energy performance data derived from your submission. No additional information related to this model, including proprietary configurations, will be disclosed.
            - **Data Integrity:** You certify that the log files submitted are accurate, unaltered, and generated directly from testing your model as per the specified benchmarking procedures.
            - **Model Representation:** You affirm that the model tested and submitted is representative of the production-level version, including its level of quantization and any other relevant characteristics impacting energy efficiency and performance.
            """)
            file_output = gr.File(visible=False)
            u = gr.UploadButton("Upload a zip file with logs", file_count="single", interactive=True)
            u.upload(add_docker_eval, u, file_output)
        
        # --- Leaderboard and Recent Models Accordions ---
        with gr.Row():
            with gr.Column():
                with gr.Accordion("Models that are in the latest leaderboard version:", open=False, visible=False):
                    gr.Dataframe(get_leaderboard_models(), elem_classes="full-width")
                with gr.Accordion("Models that have been benchmarked recently:", open=False, visible=False):
                    gr.Dataframe(formatted_df, elem_classes="full-width")

demo.launch()