fix some of the status

app.py

@@ -34,11 +34,25 @@ def update_leaderboard_cache():
         if df.empty:
             GLOBAL_LEADERBOARD_DATA = []
         else:
-            # Fill numeric NaNs with 0, string NaNs with ""
-            df = df.fillna(0)
             df = df.drop(columns=["Model Size Filter"], errors="ignore")
+
+            # Keep scores numeric, but show Unknown for missing metadata fields.
+            score_cols = [t[2] for t in TASKS] + ["Average", "Rank"]
+            for col in score_cols:
+                if col in df.columns:
+                    df[col] = pd.to_numeric(df[col], errors="coerce").fillna(0)
+
             if "Model Size" in df.columns:
-                df["Model Size"] = df["Model Size"].astype(float).round(2)
+                size_series = pd.to_numeric(df["Model Size"], errors="coerce")
+                df["Model Size"] = size_series.apply(lambda v: int(v) if pd.notna(v) else "Unknown")
+
+            if "Hub ❤️" in df.columns:
+                likes_series = pd.to_numeric(df["Hub ❤️"], errors="coerce")
+                df["Hub ❤️"] = likes_series.apply(lambda v: int(v) if pd.notna(v) else "Unknown")
+
+            for col in ["License", "Revision", "Type", "Full Type", "Precision"]:
+                if col in df.columns:
+                    df[col] = df[col].replace("", pd.NA).fillna("Unknown")
             
             # Update global variable
             GLOBAL_LEADERBOARD_DATA = df.drop(columns=["datetime"]).to_dict(orient="records")
@@ -122,8 +136,8 @@ async def get_model_likes(
     """Fetches the number of likes for a model from Hugging Face Hub."""
     try:
         info = API.model_info(repo_id=model_name, revision=revision, token=hf_api_token)
-        likes = info.likes or 0
-        downloads = info.downloads or 0
+        likes = info.likes
+        downloads = info.downloads
         return JSONResponse(content={"likes": likes, "downloads": downloads})
     except Exception as e:
         logging.error(f"Error fetching likes for {model_name}: {e}")
@@ -169,4 +183,4 @@ async def read_page(request: Request, page_name: str):
     return templates.TemplateResponse(page_name, {"request": request})
 
 if __name__ == "__main__":
-    uvicorn.run("app:app", host="0.0.0.0", port=7860, reload=True, access_log=False)
+    uvicorn.run("app:app", host="0.0.0.0", port=7860, reload=True, access_log=False)

backend/data_loader.py

@@ -5,6 +5,7 @@ import os
 import contextlib
 import io
 import logging
+from concurrent.futures import ThreadPoolExecutor, as_completed
 from pathlib import Path
 from typing import Dict, List, Any, Optional
 
@@ -13,12 +14,14 @@ import pandas as pd
 from huggingface_hub import snapshot_download
 from datetime import datetime
 from backend.config import (
+    API,
     REQUESTS_REPO_ID,
     RESULTS_REPO_ID,
     TASKS,
     MODEL_TYPE_TO_EMOJI,
+    hf_api_token,
 )
-from backend.helpers import unify_precision
+from backend.helpers import unify_precision, get_model_size
 
 logger = logging.getLogger(__name__)
 
@@ -97,7 +100,7 @@ def _parse_result_file(path: Path) -> Optional[Dict[str, Any]]:
         "Precision": precision,
         "datetime": datetime.strptime(string_date, "%Y-%m-%dT%H-%M-%S.%f")
     }
-
+    
     for task_key, metric_key, display in TASKS:
         if isinstance(task_key,list):
             weight_total = 0
@@ -122,6 +125,33 @@ def _parse_result_file(path: Path) -> Optional[Dict[str, Any]]:
     return row
 
 
+def _fetch_hf_metadata(model_name: str) -> Dict[str, Any]:
+    try:
+        info = API.model_info(repo_id=model_name, token=hf_api_token)
+    except Exception as e:
+        logger.warning("Could not fetch HF metadata for '%s': %s", model_name, e)
+        return {}
+
+    card_data = getattr(info, "card_data", None)
+    if isinstance(card_data, dict):
+        license_name = card_data.get("license")
+    else:
+        license_name = getattr(card_data, "license", None)
+
+    model_size = get_model_size(model_info=info, precision="")
+    if model_size == 0:
+        safetensors = getattr(info, "safetensors", None)
+        if not safetensors or not safetensors.get("total"):
+            model_size = None
+
+    return {
+        "License": license_name,
+        "Revision": getattr(info, "sha", None),
+        "Model Size": model_size,
+        "Hub ❤️": getattr(info, "likes", None),
+    }
+
+
 def load_scoreboard() -> pd.DataFrame:
     """
     Main entrypoint used by the Space UI.
@@ -155,12 +185,36 @@ def load_scoreboard() -> pd.DataFrame:
         df[col] = (pd.to_numeric(df[col], errors="coerce") * 100).round(2)
     df["Average"] = df[task_cols].mean(axis=1).round(2)
 
+    # metadata from Hugging Face API (fetched in parallel for speed)
+    model_names = df["Model Name"].dropna().unique().tolist()
+    hf_meta: Dict[str, Dict[str, Any]] = {}
+    if model_names:
+        max_workers = min(12, len(model_names))
+        with ThreadPoolExecutor(max_workers=max_workers) as executor:
+            future_to_model = {
+                executor.submit(_fetch_hf_metadata, model_name): model_name
+                for model_name in model_names
+            }
+            for future in as_completed(future_to_model):
+                model_name = future_to_model[future]
+                hf_meta[model_name] = future.result() or {}
+
+    df["License"] = df["Model Name"].map(lambda name: hf_meta.get(name, {}).get("License"))
+    df["Revision"] = df["Model Name"].map(lambda name: hf_meta.get(name, {}).get("Revision"))
+    df["Model Size"] = df["Model Name"].map(lambda name: hf_meta.get(name, {}).get("Model Size"))
+    df["Hub ❤️"] = df["Model Name"].map(lambda name: hf_meta.get(name, {}).get("Hub ❤️"))
+    df["Type"] = None
+    df["Full Type"] = None
+
     # merge metadata from finished requests
     finished = load_requests("finished")
     if not finished.empty:
         finished["precision"] = finished["precision"].apply(unify_precision)
         meta = finished.groupby(["model", "precision"]).last().reset_index()
 
+        def is_missing(v: Any) -> bool:
+            return v is None or (isinstance(v, str) and not v.strip()) or pd.isna(v)
+
         def enrich(row):
             m = meta[
                 (meta["model"] == row["Model Name"]) &
@@ -168,13 +222,15 @@ def load_scoreboard() -> pd.DataFrame:
             ]
             if not m.empty:
                 m = m.iloc[0]
-                row["License"] = m.get("license", "UNK")
-                row["Revision"] = m.get("revision", "UNK")
-                row["Model Size"] = m.get("params", 0)
-                row["Hub ❤️"] = m.get("likes", 0)
+                if is_missing(row.get("License")):
+                    row["License"] = m.get("license")
+                if is_missing(row.get("Revision")):
+                    row["Revision"] = m.get("revision")
+                model_type_raw = m.get("model_type", "Missing")
                 row["Type"] = MODEL_TYPE_TO_EMOJI.get(
-                    m.get("model_type", ""), m.get("model_type", "")
+                    model_type_raw, model_type_raw
                 )
+                row["Full Type"] = model_type_raw
             return row
 
         df = df.apply(enrich, axis=1)

frontend/leaderboard.html

@@ -551,6 +551,19 @@
                 const $ = s => document.querySelector(s);
                 const $$ = s => [...document.querySelectorAll(s)];
                 const EVAL_COLUMNS = window.EVAL_COLUMNS;
+                const toNumber = (v) => {
+                    const n = Number.parseFloat(v);
+                    return Number.isFinite(n) ? n : null;
+                };
+                const asUnknown = (v) => {
+                    if (v === undefined || v === null) return "Unknown";
+                    const s = String(v).trim();
+                    return s ? s : "Unknown";
+                };
+                const prettyIntOrUnknown = (v) => {
+                    const n = toNumber(v);
+                    return n === null ? "Unknown" : String(Math.floor(n));
+                };
 
                 let lbData = [], grid, maxMeta = 100, minMeta = 0, tableColumns = [];
                 let currentSort = { colId: null, dir: 'none' };
@@ -565,9 +578,11 @@
                 window.initLeaderboard = function (data) {
                     lbData = data;
 
-                    const sizes = lbData.map(r => r["Model Size"] || 0);
-                    minMeta = sizes.length ? Math.ceil(Math.min(...sizes)) : 0;
-                    maxMeta = sizes.length ? Math.ceil(Math.max(...sizes)) : 100;
+                    const sizes = lbData
+                        .map(r => toNumber(r["Model Size"]))
+                        .filter(v => v !== null);
+                    minMeta = sizes.length ? Math.floor(Math.min(...sizes)) : 0;
+                    maxMeta = sizes.length ? Math.floor(Math.max(...sizes)) : 100;
 
                     if (!lbData.length) {
                         $('#table-wrapper').innerHTML = "<div class='p-8 text-center text-slate-500'>No data loaded.</div>";
@@ -691,30 +706,34 @@
     `;
 
                     // Reset values
+                    const revision = asUnknown(model["Revision"]);
+                    const revisionForApi = revision === "Unknown" ? "main" : revision;
+                    const modelSize = toNumber(model["Model Size"]);
+                    const fallbackLikes = model["Hub ❤️"];
                     $('#modalRank').innerText = "#" + model["Rank"];
                     $('#modalAvg').innerText = parseFloat(model["Average"]).toFixed(2);
-                    $('#modalSize').innerText = model["Model Size"] + "B";
-                    $('#modalLikes').innerText = "--";
+                    $('#modalSize').innerText = modelSize === null ? "Unknown" : `${Math.floor(modelSize)}B`;
+                    $('#modalLikes').innerText = prettyIntOrUnknown(fallbackLikes);
                     // We don't have an ID for downloads yet in the static HTML, so we rely on the injected HTML below
-                    $('#modalLicense').innerText = model["License"];
-                    $('#modalPrecision').innerText = model["Precision"];
-                    $('#modalRevision').innerText = model["Revision"];
+                    $('#modalLicense').innerText = asUnknown(model["License"]);
+                    $('#modalPrecision').innerText = asUnknown(model["Precision"]);
+                    $('#modalRevision').innerText = revision;
 
                     // --- 1. MODIFIED: Added Download Span to Metadata Line ---
                     // I added the separator dot and the Downloads span at the end of this block
                     const metadataHtml = `
-        <span class="flex items-center gap-1" title="License"><i data-lucide="scale" class="w-3.5 h-3.5"></i> <span id="modalLicense">${model["License"]}</span></span>
+        <span class="flex items-center gap-1" title="License"><i data-lucide="scale" class="w-3.5 h-3.5"></i> <span id="modalLicense">${asUnknown(model["License"])}</span></span>
         <span class="w-1 h-1 rounded-full bg-slate-300 dark:bg-slate-600"></span>
 
-        <span class="flex items-center gap-1" title="Precision"><i data-lucide="cpu" class="w-3.5 h-3.5"></i> <span id="modalPrecision">${model["Precision"]}</span></span>
+        <span class="flex items-center gap-1" title="Precision"><i data-lucide="cpu" class="w-3.5 h-3.5"></i> <span id="modalPrecision">${asUnknown(model["Precision"])}</span></span>
         <span class="w-1 h-1 rounded-full bg-slate-300 dark:bg-slate-600"></span>
 
-        <span class="flex items-center gap-1" title="Revision"><i data-lucide="git-commit" class="w-3.5 h-3.5"></i> <span id="modalRevision" class="font-mono">${model["Revision"]}</span></span>
+        <span class="flex items-center gap-1" title="Revision"><i data-lucide="git-commit" class="w-3.5 h-3.5"></i> <span id="modalRevision" class="font-mono">${revision}</span></span>
         <span class="w-1 h-1 rounded-full bg-slate-300 dark:bg-slate-600"></span>
 
         <span class="flex items-center gap-1" title="Downloads (last 30 days)">
             <i data-lucide="download" class="w-3.5 h-3.5"></i> 
-            <span id="modalDownloads">--</span>
+            <span id="modalDownloads">Unknown</span>
         </span>
     `;
 
@@ -724,7 +743,7 @@
                     // --- Fetch Logic to include Downloads ---
                     const formData = new FormData();
                     formData.append('model_name', model["Model Name"]);
-                    formData.append('revision', model["Revision"]);
+                    formData.append('revision', revisionForApi);
 
                     fetch('/api/model-likes', {
                         method: 'POST',
@@ -732,16 +751,21 @@
                     })
                         .then(response => response.json())
                         .then(data => {
-                            if (data.likes !== undefined) {
-                                $('#modalLikes').innerText = data.likes;
+                            if (data.likes !== undefined && data.likes !== null) {
+                                $('#modalLikes').innerText = prettyIntOrUnknown(data.likes);
                             }
                             // Check if API returns downloads and update
                             if (data.downloads !== undefined) {
                                 const dl = document.getElementById('modalDownloads');
-                                if (dl) dl.innerText = data.downloads; // You might want to format this (e.g., 1.5k)
+                                if (dl) dl.innerText = prettyIntOrUnknown(data.downloads);
                             }
                         })
-                        .catch(error => console.error('Error fetching stats:', error));
+                        .catch(error => {
+                            console.error('Error fetching stats:', error);
+                            const dl = document.getElementById('modalDownloads');
+                            if (dl) dl.innerText = "Unknown";
+                            $('#modalLikes').innerText = prettyIntOrUnknown(fallbackLikes);
+                        });
 
                     $('#modalLinkHF').href = `https://huggingface.co/${model["Model Name"]}`;
 
@@ -822,7 +846,11 @@
                     // 1. Filter Data
                     let filtered = lbData.filter(r =>
                         (r["Model Name"] || "").toLowerCase().includes(sVal) &&
-                        (r["Model Size"] || 0) >= minSz && (r["Model Size"] || 0) <= maxSz &&
+                        (() => {
+                            const s = toNumber(r["Model Size"]);
+                            const sizeValue = s === null ? 0 : s;
+                            return sizeValue >= minSz && sizeValue <= maxSz;
+                        })() &&
                         (!precs.length || precs.includes(r["Precision"])) &&
                         (!types.length || types.includes(r["Full Type"])) &&
                         (!lics.length || lics.includes(r["License"]))
@@ -850,7 +878,7 @@
                             if (!isNaN(nA) && !isNaN(nB)) {
                                 return currentSort.dir === 'asc' ? nA - nB : nB - nA;
                             }
-                            return vA.toString().localeCompare(vB.toString()) * (currentSort.dir === 'asc' ? 1 : -1);
+                            return String(vA ?? "").localeCompare(String(vB ?? "")) * (currentSort.dir === 'asc' ? 1 : -1);
                         });
                     }
 
@@ -909,7 +937,10 @@
                         };
 
                         if (key === "Model Size") {
-                            def.formatter = (c) => gridjs.html(`<span class="font-mono">${c}B</span>`);
+                            def.formatter = (c) => {
+                                const n = toNumber(c);
+                                return gridjs.html(`<span class="font-mono">${n === null ? "Unknown" : `${Math.floor(n)}B`}</span>`);
+                            };
                         }
 
                         if (key === "Model Name") {