app.py

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("""
        <style>
          .header-link { color: black !important; }
          @media (prefers-color-scheme: dark) { .header-link { color: white !important; } }
        </style>
        <div style="display:flex;justify-content:space-evenly;align-items:center;margin-bottom:20px;">
            <a class="header-link" href="https://huggingface.co/spaces/AIEnergyScore/leaderboard" style="text-decoration:none;font-weight:bold;font-size:1.1em;font-family:'Inter',sans-serif;">Leaderboard</a>
            <a class="header-link" href="https://huggingface.co/spaces/AIEnergyScore/Label" style="text-decoration:none;font-weight:bold;font-size:1.1em;font-family:'Inter',sans-serif;">Label Generator</a>
            <a class="header-link" href="https://huggingface.github.io/AIEnergyScore/#faq" style="text-decoration:none;font-weight:bold;font-size:1.1em;font-family:'Inter',sans-serif;">FAQ</a>
            <a class="header-link" href="https://huggingface.github.io/AIEnergyScore/#documentation" style="text-decoration:none;font-weight:bold;font-size:1.1em;font-family:'Inter',sans-serif;">Documentation</a>
            <a class="header-link" href="https://huggingface.co/spaces/AIEnergyScore/README/discussions" style="text-decoration:none;font-weight:bold;font-size:1.1em;font-family:'Inter',sans-serif;">Community</a>
        </div>
    """)

    # Logo (kept)
    gr.HTML("""
        <div style="margin-top:0px;">
            <picture style="display:block;margin:0 auto;max-width:300px;">
                <source media="(prefers-color-scheme: dark)" srcset="https://huggingface.co/spaces/AIEnergyScore/Leaderboard/resolve/main/logodark.png">
                <img src="https://huggingface.co/spaces/AIEnergyScore/Leaderboard/resolve/main/logo.png" alt="Logo" style="display:block;margin:0 auto;max-width:300px;height:auto;">
            </picture>
        </div>
    """)

    gr.Markdown("<div style='text-align:center;'><h2>Submission Portal</h2></div>")
    ##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()