Update app.py

app.py

@@ -2,74 +2,75 @@ import gradio as gr
 import json
 from datetime import datetime
 from evaluate import evaluate_model
-from datasets import load_dataset, Dataset
-from huggingface_hub import HfApi, HfFolder, Repository
+from huggingface_hub import Repository, HfApi, hf_hub_download
 import os
 
-# Config
-HF_REPO_ID = "Mehrdad-S/persian-leaderboard-data"  # Replace with your dataset repo ID
-HF_TOKEN = os.getenv("HF_TOKEN")
+# --- Configuration ---
+HF_REPO_ID = "Mehrdad-S/persian-leaderboard-data"  # Replace with your repo
+HF_TOKEN = os.getenv("HF_TOKEN") or "your_hf_write_token_here"
 FILENAME = "leaderboard.json"
+CLONE_DIR = "leaderboard_repo"
 
-# Load evaluation dataset
-eval_dataset = load_dataset("arshiaafshani/persian-natural-fluently", split="train[:10]")
-
+# --- Load leaderboard.json from the repo ---
 def load_leaderboard():
-    dataset = load_dataset(HF_REPO_ID, split="train")
-    leaderboard = [row for row in dataset]
-    return leaderboard
-
-def save_leaderboard(leaderboard):
-    dataset = Dataset.from_list(leaderboard)
-    dataset.save_to_disk("local_dataset")
-    repo = Repository(local_dir="local_dataset", clone_from=HF_REPO_ID, use_auth_token=HF_TOKEN)
+    path = hf_hub_download(repo_id=HF_REPO_ID, filename=FILENAME, repo_type="dataset")
+    with open(path, "r", encoding="utf-8") as f:
+        return json.load(f)
+
+# --- Save leaderboard.json and push to HF Hub ---
+def save_leaderboard(data):
+    if not os.path.exists(CLONE_DIR):
+        repo = Repository(local_dir=CLONE_DIR, clone_from=HF_REPO_ID, repo_type="dataset", use_auth_token=HF_TOKEN)
+    else:
+        repo = Repository(local_dir=CLONE_DIR, use_auth_token=HF_TOKEN)
+
+    leaderboard_path = os.path.join(CLONE_DIR, FILENAME)
+    with open(leaderboard_path, "w", encoding="utf-8") as f:
+        json.dump(data, f, ensure_ascii=False, indent=2)
+
     repo.push_to_hub(commit_message="Update leaderboard")
 
+# --- Submit model and update leaderboard ---
 def submit_model(model_name):
     leaderboard = load_leaderboard()
 
-    score = round(evaluate_model(model_name, eval_dataset) * 100, 2)
+    score = round(evaluate_model(model_name), 2)
     date = datetime.now().strftime("%Y-%m-%d")
 
-    # Update if model exists, else add new
-    updated = False
+    # Update existing or add new
     for item in leaderboard:
         if item["model"] == model_name:
             item["score"] = score
             item["date"] = date
-            updated = True
             break
-    if not updated:
+    else:
         leaderboard.append({"model": model_name, "score": score, "date": date})
 
-    # Sort by score
     leaderboard.sort(key=lambda x: x["score"], reverse=True)
-
-    # Save back
     save_leaderboard(leaderboard)
-
     return update_table(leaderboard)
 
-def update_table(leaderboard=None):
-    if leaderboard is None:
-        leaderboard = load_leaderboard()
+# --- Build table view ---
+def update_table(data=None):
+    if data is None:
+        data = load_leaderboard()
 
-    headers = ["🏅 Rank", "📌 Model Name", "🎯 Score", "📆 Date"]
-    rows = [[i + 1, row["model"], f"{row['score']}%", row["date"]] for i, row in enumerate(leaderboard)]
+    headers = ["🏅 Rank", "📌 Model", "🎯 Score", "📆 Date"]
+    rows = [[i + 1, row["model"], f"{row['score']}%", row["date"]] for i, row in enumerate(data)]
     return headers, rows
 
-# UI
+# --- UI ---
 with gr.Blocks(theme=gr.themes.Monochrome()) as demo:
     gr.Markdown("""
     # 🏆 Persian Embedding Leaderboard
-    Submit your model to evaluate on the [arshiaafshani/persian-natural-fluently](https://huggingface.co/datasets/arshiaafshani/persian-natural-fluently) dataset.
+    Submit your HuggingFace model to evaluate and rank on the leaderboard.
     """)
 
     with gr.Row():
-        model_input = gr.Textbox(label="Enter HuggingFace Model Name", placeholder="e.g. HooshvareLab/bert-fa-base-uncased")
+        model_input = gr.Textbox(label="Model Name", placeholder="e.g. HooshvareLab/bert-fa-base-uncased")
         submit_btn = gr.Button("Evaluate & Submit")
 
-    table = gr.Dataframe(headers=["🏅 Rank", "📌 Model Name", "🎯 Score", "📆 Date"],
+    table = gr.Dataframe(headers=["🏅 Rank", "📌 Model", "🎯 Score", "📆 Date"],
                          value=update_table()[1],
                          interactive=False)