Create app1.py

app1.py

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+import os
+import json
+import sqlite3
+import glob
+import pandas as pd
+import gradio as gr
+from datetime import datetime
+from typing import Dict, List
+
+# Directory to store SQLite results
+db_dir = "results/"
+
+def find_or_download_db():
+    """Check if SQLite .db files exist; if not, attempt to download from cloud storage."""
+    if not os.path.exists(db_dir):
+        os.makedirs(db_dir)
+    db_files = glob.glob(os.path.join(db_dir, "*.db"))
+    
+    # Ensure the random bot database exists
+    if "results/random_None.db" not in db_files:
+        raise FileNotFoundError("Please upload results for the random agent in a file named 'random_None.db'.")
+    
+    return db_files
+
+def extract_agent_info(filename: str):
+    """Extract agent type and model name from the filename."""
+    base_name = os.path.basename(filename).replace(".db", "")
+    parts = base_name.split("_", 1)
+    if len(parts) == 2:
+        agent_type, model_name = parts
+    else:
+        agent_type, model_name = parts[0], "Unknown"
+    return agent_type, model_name
+
+def extract_leaderboard_stats(game_name: str) -> pd.DataFrame:
+    """Extract and aggregate leaderboard stats from all SQLite databases."""
+    db_files = find_or_download_db()
+    all_stats = []
+    
+    for db_file in db_files:
+        conn = sqlite3.connect(db_file)
+        agent_type, model_name = extract_agent_info(db_file)
+        
+        if game_name == "Total Performance":
+            query = "SELECT game_name, COUNT(DISTINCT episode) AS games_played, " \
+                    "AVG(generation_time) AS avg_gen_time, SUM(reward) AS total_rewards " \
+                    "FROM game_results GROUP BY game_name"
+        else:
+            query = "SELECT COUNT(DISTINCT episode) AS games_played, " \
+                    "AVG(generation_time) AS avg_gen_time, SUM(reward) AS total_rewards " \
+                    "FROM game_results WHERE game_name = ?"
+        
+        df = pd.read_sql_query(query, conn, params=(game_name,) if game_name != "Total Performance" else None)
+        df["agent_name"] = model_name
+        df["agent_type"] = agent_type
+        all_stats.append(df)
+        conn.close()
+    
+    leaderboard_df = pd.concat(all_stats, ignore_index=True) if all_stats else pd.DataFrame()
+    return leaderboard_df
+
+def generate_leaderboard_json():
+    """Generate a JSON file containing leaderboard stats."""
+    leaderboard = extract_leaderboard_stats("Total Performance").to_dict(orient="records")
+    json_file = "results/leaderboard_stats.json"
+    with open(json_file, "w", encoding="utf-8") as f:
+        json.dump({"timestamp": datetime.utcnow().isoformat(), "leaderboard": leaderboard}, f, indent=4)
+    return json_file
+
+with gr.Blocks() as interface:
+    with gr.Tab("Leaderboard"):
+        gr.Markdown("# Leaderboard")
+        leaderboard_game_dropdown = gr.Dropdown(["Total Performance", "tic_tac_toe"], label="Select Game")
+        leaderboard_table = gr.Dataframe()
+        download_button = gr.File(label="Download Leaderboard")
+        refresh_button = gr.Button("Refresh Leaderboard")
+        leaderboard_game_dropdown.change(extract_leaderboard_stats, inputs=[leaderboard_game_dropdown], outputs=[leaderboard_table])
+        refresh_button.click(extract_leaderboard_stats, inputs=[leaderboard_game_dropdown], outputs=[leaderboard_table])
+        download_button.click(generate_leaderboard_json, outputs=[download_button])
+
+interface.launch()