import os, glob
from datetime import datetime, timezone
import pandas as pd
import gradio as gr
from datasets import load_dataset, Dataset
from huggingface_hub import HfApi, ModelInfo
# ---------- Config ----------
OWNER = "AIEnergyScore"
COMPUTE_SPACE = f"{OWNER}/launch-computation-example"
TOKEN = os.environ.get("DEBUG") # keep your existing env var
API = HfApi(token=TOKEN)
def preflight_status():
# 1) Check token presence
if not TOKEN:
return ("❌ No HF token found in env var 'DEBUG'. "
"Add a secret named DEBUG in the Space settings (a token with 'write' scope).")
# 2) Check identity
try:
me = API.whoami(token=TOKEN)
user_str = me.get("name") or me.get("username") or "unknown-user"
except Exception as e:
return f"❌ Token error: cannot authenticate ({e})."
# 3) Check dataset access
repo_id = "AIEnergyScore/tested_proprietary_models"
try:
info = API.repo_info(repo_id=repo_id, repo_type="dataset", token=TOKEN)
# If this succeeds, you at least have read access; write failure will surface during upload.
return f"✅ Connected as **{user_str}**. Dataset **{repo_id}** reachable."
except Exception as e:
return (f"⚠️ Auth OK as **{user_str}**, but cannot access dataset "
f"**{repo_id}** ({e}). Make sure the token user has write access.")
# ---------- Upload to HF dataset (kept from your original) ----------
def add_docker_eval(zip_file):
new_fid = os.path.basename(zip_file)
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!")
# ---------- Minimal UI ----------
GITHUB_DOCKER_URL = "https://github.com/huggingface/AIEnergyScore"
METHODOLOGY_URL = "https://huggingface.co/spaces/AIEnergyScore/README"
with gr.Blocks(title="AI Energy Score") as demo:
# Header links (kept)
gr.HTML("""
""")
# Logo (kept)
gr.HTML("""
""")
gr.Markdown("Submission Portal
")
##preflight_box = gr.Markdown(preflight_status())
with gr.Row():
# -------- Open Models ----------
with gr.Column():
gr.Markdown("""
### 🌿 Open Models
If your model is hosted on the 🤗 Hub, please **start a new Discussion** and include:
- The **Hugging Face model link** (e.g., `org/model-name`)
- The **requested task type** (e.g., Text Generation)
> Requires a Hugging Face account.
➡️ **[Start a New Discussion](https://huggingface.co/spaces/AIEnergyScore/README/discussions)**
""")
# -------- Closed Models ----------
with gr.Column():
gr.Markdown(f"""
### 🔒 Closed Models
Run the benchmark **in your own environment** and upload the logs here.
1. Use our Docker setup
• **[Docker & configs]({GITHUB_DOCKER_URL})**
• **[Methodology / Docs]({METHODOLOGY_URL})**
2. When finished, upload the **ZIP file of logs** below.
**⚠️ By uploading the zip file, you agree to:**
- **Public Data Sharing:** We may publicly share the energy performance metrics derived from your submission (no proprietary configs disclosed).
- **Data Integrity:** Logs are accurate, unaltered, and produced per the specified procedures.
- **Model Representation:** The submitted run reflects your production-level model (quantization, etc.).
""")
# Visible status box for user feedback
status_box = gr.Markdown("")
# Hidden file sink (kept pattern from your previous code)
file_sink = gr.File(visible=False)
upload_button = gr.UploadButton(
"📁 Upload a ZIP file with logs", file_count="single", file_types=[".zip"], interactive=True
)
# Wrapper: call your uploader and also write user-visible status
def handle_zip_and_upload(temp_path):
if not temp_path:
gr.Warning("No file selected.")
return "❌ No file uploaded."
if not TOKEN:
gr.Warning("Missing HF token in env var 'DEBUG'.")
return "❌ Upload blocked: missing token (DEBUG)."
# Enforce .zip
if not str(temp_path).lower().endswith(".zip"):
gr.Warning("Only .zip files are accepted.")
return "❌ Please upload a .zip file."
try:
# Your existing uploader: pushes to AIEnergyScore/tested_proprietary_models/submitted_models/
add_docker_eval(temp_path) # shows a toast on success/failure internally
basename = os.path.basename(temp_path)
return f"✅ Received and submitted: **{basename}**"
except Exception as e:
gr.Warning(f"Upload error: {e}")
return f"❌ Upload failed — {e}"
# IMPORTANT: bind inside Blocks context
upload_button.upload(
fn=handle_zip_and_upload,
inputs=upload_button, # UploadButton passes the temp file path
outputs=status_box, # show result here
)
# Launch
if __name__ == "__main__":
demo.launch()