Update app.py

app.py

@@ -1,46 +1,51 @@
+# app.py
 import gradio as gr
-import threading
-import time
-from utils import load_leaderboard, update_leaderboard
-from evaluate import evaluate_model
-
-def refresh_leaderboard_models():
-    leaderboard = load_leaderboard()
-    for entry in leaderboard:
-        model = entry["model"]
-        score = evaluate_model(model)
-        if score is not None:
-            update_leaderboard(model, score)
-    print("Leaderboard refreshed.")
-
-# Run refresh in a background thread on Space start
-threading.Thread(target=refresh_leaderboard_models, daemon=True).start()
-
-def show_leaderboard():
-    data = load_leaderboard()
-    if not data:
-        return "No models evaluated yet."
-    return "\n".join([f"{i+1}. {entry['model']} - {entry['score']:.4f}" for i, entry in enumerate(data)])
+import json
+from evaluate import evaluate_model, load_dataset
+from datetime import datetime
+
+# Load dataset once
+dataset = load_dataset()
+
+# Load or initialize leaderboard
+try:
+    with open("leaderboard.json", "r", encoding="utf-8") as f:
+        leaderboard = json.load(f)
+except FileNotFoundError:
+    leaderboard = []
 
 def submit_model(model_name):
-    score = evaluate_model(model_name)
-    if score is None:
-        return "❌ Failed to load or evaluate model."
-    update_leaderboard(model_name, score)
-    return f"✅ Model evaluated with score: {score:.4f}"
-
-with gr.Blocks() as demo:
-    gr.Markdown("# 🏆 Persian Embedding Leaderboard")
-
-    with gr.Tab("Leaderboard"):
-        leaderboard_display = gr.Textbox(label="Leaderboard", lines=20)
-        btn_refresh = gr.Button("Refresh")
-        btn_refresh.click(fn=show_leaderboard, outputs=leaderboard_display)
-
-    with gr.Tab("Submit Model"):
-        model_input = gr.Textbox(label="Hugging Face Model ID", placeholder="e.g., HooshvareLab/bert-fa-base-uncased")
-        submit_btn = gr.Button("Submit for Evaluation")
-        result_output = gr.Textbox(label="Result")
-        submit_btn.click(fn=submit_model, inputs=model_input, outputs=result_output)
-
-demo.launch()
+    score = evaluate_model(model_name, dataset)
+    leaderboard.append({
+        "model": model_name,
+        "score": round(score * 100, 2),
+        "date": datetime.now().strftime("%Y-%m-%d")
+    })
+    leaderboard.sort(key=lambda x: x["score"], reverse=True)
+    with open("leaderboard.json", "w", encoding="utf-8") as f:
+        json.dump(leaderboard, f, ensure_ascii=False, indent=2)
+    return update_table()
+
+def update_table():
+    headers = ["🏅 Rank", "📌 Model Name", "🎯 Score", "📆 Date"]
+    rows = [[i + 1, row["model"], f"{row['score']}%", row["date"]] for i, row in enumerate(leaderboard)]
+    return headers, rows
+
+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.
+    """)
+
+    with gr.Row():
+        model_input = gr.Textbox(label="Enter HuggingFace 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"],
+                         value=update_table()[1],
+                         interactive=False)
+
+    submit_btn.click(fn=submit_model, inputs=model_input, outputs=table)
+
+if __name__ == "__main__":
+    demo.launch()