Update app.py

app.py

@@ -7,6 +7,18 @@ import random
 from elo import update_elo_ratings  # Custom function for ELO ratings
 enable_btn = gr.Button.update(interactive=True)
 
+import sqlite3
+
+def init_database():
+    conn = sqlite3.connect('elo_ratings.db')
+    c = conn.cursor()
+    c.execute('''CREATE TABLE IF NOT EXISTS elo_ratings
+                 (bot_name TEXT PRIMARY KEY,
+                  elo_rating INTEGER,
+                  games_played INTEGER)''')
+    conn.commit()
+    conn.close()
+
 # Load chatbot URLs and model names from a JSON file
 # Load chatbot model adapter names from a text file
 with open('chatbots.txt', 'r') as file:
@@ -28,25 +40,24 @@ import requests
 import os
 
 def get_user_elo_ratings():
-    global global_elo_ratings
-    try:
-        dataset = load_dataset("rwitz/mistral-elo-ratings", download_mode="force_redownload")
-        global_elo_ratings = dataset['train']  # or the relevant split
-    except:
-        global_elo_ratings=Dataset.from_dict({"bot_name":["default"],"elo_rating":[1200],"games_played":[0]})
-    return global_elo_ratings
-    
-def update_elo_rating(new_rating,winner,loser):
-    # Fetch the current dataset
-    elo_ratings = get_user_elo_ratings()
-
-    # Function to update the rating of a specific player
-
-    # Update the dataset
-    # Convert updated dataset to a dictionary for pushing
-    updated_ratings=update_elo_ratings(elo_ratings,winner,loser)
-    updated_ratings.push_to_hub("rwitz/mistral-elo-ratings",token=os.environ.get("huggingface_token"))
-    
+    conn = sqlite3.connect('elo_ratings.db')
+    c = conn.cursor()
+    c.execute("SELECT * FROM elo_ratings")
+    rows = c.fetchall()
+    conn.close()
+    if rows:
+        return {row[0]: {'elo_rating': row[1], 'games_played': row[2]} for row in rows}
+    else:
+        return {"default": {'elo_rating': 1200, 'games_played': 0}}
+def update_elo_rating(updated_ratings, winner, loser):
+    conn = sqlite3.connect('elo_ratings.db')
+    c = conn.cursor()
+    c.execute("INSERT OR REPLACE INTO elo_ratings (bot_name, elo_rating, games_played) VALUES (?, ?, ?)",
+              (winner, updated_ratings[winner]['elo_rating'], updated_ratings[winner]['games_played']))
+    c.execute("INSERT OR REPLACE INTO elo_ratings (bot_name, elo_rating, games_played) VALUES (?, ?, ?)",
+              (loser, updated_ratings[loser]['elo_rating'], updated_ratings[loser]['games_played']))
+    conn.commit()
+    conn.close()
 # Function to get bot response
 def format_alpaca_prompt(state):
     alpaca_prompt = "Below is an instruction that describes a task. Write a response that appropriately completes the request."
@@ -152,23 +163,14 @@ import pandas as pd
 # Function to generate leaderboard data
 
 def generate_leaderboard():
-    global global_elo_ratings
-    global_elo_ratings = get_user_elo_ratings()
-
-    # Convert the Hugging Face dataset to a pandas DataFrame
-    leaderboard_data = pd.DataFrame(global_elo_ratings)
-
-    # Rename columns to 'Chatbot' and 'ELO Score'
-    leaderboard_data.columns = ['Chatbot', 'ELO Score','Games Played']
-
-    # Round the ELO Score to the nearest whole number
-    leaderboard_data['ELO Score'] = leaderboard_data['ELO Score'].round()
-
-    # Sort the DataFrame based on the ELO Score in descending order
-    leaderboard_data = leaderboard_data.sort_values('ELO Score', ascending=False)
-
+    conn = sqlite3.connect('elo_ratings.db')
+    c = conn.cursor()
+    c.execute("SELECT bot_name, elo_rating, games_played FROM elo_ratings ORDER BY elo_rating DESC")
+    rows = c.fetchall()
+    conn.close()
+    leaderboard_data = pd.DataFrame(rows, columns=['Chatbot', 'ELO Score', 'Games Played'])
     return leaderboard_data
-
+    
 def refresh_leaderboard():
     return generate_leaderboard()
 # Gradio interface setup
@@ -208,4 +210,5 @@ with gr.Blocks() as demo:
 
     # Launch the Gradio interface
 if __name__ == "__main__":
-    demo.launch(share=False)
+    init_database()
+    demo.launch(share=False)