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Update app.py
Browse files
app.py
CHANGED
@@ -3,7 +3,7 @@ from functools import lru_cache
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import random
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import requests
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import logging
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import
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import plotly.graph_objects as go
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from typing import Dict
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from leaderboard import (
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@@ -15,7 +15,7 @@ from leaderboard import (
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ensure_elo_ratings_initialized
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)
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import sys
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import threading
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import time
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from collections import Counter
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@@ -31,7 +31,7 @@ start_backup_thread()
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# Function to get available models (using predefined list)
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def get_available_models():
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return [model[0] for model in
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# Function to get recent opponents for a model
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recent_opponents = {}
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@@ -46,30 +46,28 @@ def update_recent_opponents(model_a, model_b):
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# Function to call Ollama API with caching
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@lru_cache(maxsize=100)
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def call_ollama_api(model, prompt):
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"content": "You are a helpful assistant. At no point should you reveal your name, identity or team affiliation to the user, especially if asked directly!"
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},
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{
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"role": "user",
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"content": prompt
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}
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]
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}
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try:
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response =
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timeout=100
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)
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response.
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return data["choices"][0]["message"]["content"]
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except requests.exceptions.RequestException as e:
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logger.error(f"Error calling Ollama API for model {model}: {e}")
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return f"Error: Unable to get response from the model."
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@@ -132,8 +130,8 @@ def battle_arena(prompt):
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gr.update(value="Error: Unable to get response from the model", visible=True)
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)
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nickname_a = random.choice(
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nickname_b = random.choice(
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# Format responses for gr.Chatbot, including the user's prompt
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response_a_formatted = [
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@@ -283,8 +281,8 @@ def get_leaderboard_chart():
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return fig
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def new_battle():
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nickname_a = random.choice(
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nickname_b = random.choice(
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return (
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"", # Reset prompt_input
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gr.update(value=[], label=nickname_a), # Reset left Chatbot
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@@ -303,18 +301,18 @@ def new_battle():
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# Add this new function
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def get_human_readable_name(model_name: str) -> str:
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model_dict = dict(
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return model_dict.get(model_name, model_name)
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# Add this new function to randomly select a prompt
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def random_prompt():
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return random.choice(
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# Modify the continue_conversation function
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def continue_conversation(prompt, left_chat, right_chat, left_model, right_model, previous_prompt, tie_count):
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# Check if the prompt is empty or the same as the previous one
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if not prompt or prompt == previous_prompt:
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prompt = random.choice(
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left_response = call_ollama_api(left_model, prompt)
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right_response = call_ollama_api(right_model, prompt)
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@@ -337,84 +335,6 @@ def continue_conversation(prompt, left_chat, right_chat, left_model, right_model
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tie_count
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)
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def get_fun_stats_html():
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stats = get_fun_stats()
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html = f"""
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<style>
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.fun-stats {{
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font-family: Arial, sans-serif;
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font-size: 18px;
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line-height: 1.6;
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max-width: 800px;
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margin: 0 auto;
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padding: 20px;
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}}
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.fun-stats h2 {{
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font-size: 36px;
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color: inherit;
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text-align: center;
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margin-bottom: 20px;
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}}
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.fun-stats h3 {{
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font-size: 28px;
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color: inherit;
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margin-top: 30px;
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margin-bottom: 15px;
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border-bottom: 2px solid currentColor;
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padding-bottom: 10px;
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}}
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.fun-stats ul {{
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list-style-type: none;
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padding-left: 0;
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}}
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.fun-stats li {{
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margin-bottom: 15px;
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padding: 15px;
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border-radius: 5px;
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box-shadow: 0 2px 5px rgba(0,0,0,0.1);
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}}
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.fun-stats .timestamp {{
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font-style: italic;
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text-align: center;
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margin-bottom: 20px;
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}}
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.fun-stats .highlight {{
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font-weight: bold;
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color: #e74c3c;
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}}
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</style>
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<div class="fun-stats">
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<h2>🎭 Fun Arena Stats 🎭</h2>
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<p class="timestamp">Last updated: {stats['timestamp']}</p>
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<h3>🏟️ Arena Overview</h3>
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<p>Total Battles Fought: <span class="highlight">{stats['total_battles']}</span></p>
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<p>Active Gladiators (Models): <span class="highlight">{stats['active_models']}</span></p>
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<h3>🏆 Hall of Fame</h3>
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<p>🥇 Battle Veteran: <span class="highlight">{stats['most_battles']['model']}</span> ({stats['most_battles']['battles']} battles)</p>
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<p>🏹 Sharpshooter: <span class="highlight">{stats['highest_win_rate']['model']}</span> (Win Rate: {stats['highest_win_rate']['win_rate']})</p>
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<p>🌈 Jack of All Trades: <span class="highlight">{stats['most_diverse_opponent']['model']}</span> (Faced {stats['most_diverse_opponent']['unique_opponents']} unique opponents)</p>
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<p>🐕 Underdog Champion: <span class="highlight">{stats['underdog_champion']['model']}</span> ({stats['underdog_champion']['size']} model with {stats['underdog_champion']['win_rate']} win rate)</p>
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<p>⚖️ Mr. Consistent: <span class="highlight">{stats['most_consistent']['model']}</span> (Closest to 50% win rate, difference of {stats['most_consistent']['win_loss_difference']} wins/losses)</p>
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<h3>🤼 Epic Battles</h3>
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<p>🤼 Biggest Rivalry: <span class="highlight">{stats['biggest_rivalry']['model1']}</span> vs <span class="highlight">{stats['biggest_rivalry']['model2']}</span> ({stats['biggest_rivalry']['total_battles']} fierce battles!)</p>
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<p>🏋️ David vs Goliath: <span class="highlight">{stats['david_vs_goliath']['david']}</span> (David) vs <span class="highlight">{stats['david_vs_goliath']['goliath']}</span> (Goliath)<br>
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David won {stats['david_vs_goliath']['wins']} times despite being {stats['david_vs_goliath']['size_difference']} smaller!</p>
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<p>🔄 Comeback King: <span class="highlight">{stats['comeback_king']['model']}</span> (Overcame a {stats['comeback_king']['comeback_margin']}-battle deficit)</p>
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<p>🏆 Pyrrhic Victor: <span class="highlight">{stats['pyrrhic_victor']['model']}</span> (Lowest win rate among models with more wins than losses: {stats['pyrrhic_victor']['win_rate']})</p>
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</div>
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"""
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return html
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def update_fun_stats_periodically(interval):
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while True:
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time.sleep(interval)
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fun_stats_html.update(value=get_fun_stats_html())
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# Initialize Gradio Blocks
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with gr.Blocks(css="""
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#dice-button {
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@@ -422,8 +342,8 @@ with gr.Blocks(css="""
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font-size: 35px;
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}
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""") as demo:
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gr.Markdown(
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gr.Markdown(
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# Leaderboard Tab (now first)
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with gr.Tab("Leaderboard"):
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submit_btn = gr.Button("Generate Responses", variant="primary")
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with gr.Row():
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left_output = gr.Chatbot(label=random.choice(
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right_output = gr.Chatbot(label=random.choice(
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with gr.Row():
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left_vote_btn = gr.Button(f"Vote for {left_output.label}", interactive=False)
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with gr.Tab("ELO Leaderboard"):
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elo_leaderboard = gr.HTML(label="ELO Leaderboard")
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# Add this new tab
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with gr.Tab("Fun Stats"):
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refresh_btn = gr.Button("Refresh Stats")
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fun_stats_html = gr.HTML(label="Fun Arena Stats")
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# Add this new tab
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with gr.Tab("Suggest Models"):
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with gr.Row():
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demo.load(get_leaderboard, outputs=leaderboard)
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demo.load(get_elo_leaderboard, outputs=elo_leaderboard)
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demo.load(get_leaderboard_chart, outputs=leaderboard_chart)
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demo.load(get_fun_stats_html, outputs=fun_stats_html)
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# Add this event handler for the refresh button
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refresh_btn.click(get_fun_stats_html, outputs=fun_stats_html)
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# Start the background task to update stats every hour
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update_thread = threading.Thread(target=update_fun_stats_periodically, args=(3600,), daemon=True)
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update_thread.start()
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if __name__ == "__main__":
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# Initialize ELO ratings before launching the app
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ensure_elo_ratings_initialized()
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demo.launch(show_api=False)
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import random
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import requests
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import logging
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import config
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import plotly.graph_objects as go
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from typing import Dict
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from leaderboard import (
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ensure_elo_ratings_initialized
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)
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import sys
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import openai
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import threading
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import time
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from collections import Counter
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# Function to get available models (using predefined list)
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def get_available_models():
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return [model[0] for model in config.get_approved_models()]
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# Function to get recent opponents for a model
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recent_opponents = {}
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# Function to call Ollama API with caching
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@lru_cache(maxsize=100)
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def call_ollama_api(model, prompt):
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client = openai.OpenAI(
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api_key=config.API_KEY,
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base_url=config.API_URL
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)
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try:
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response = client.chat.completions.create(
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model=model,
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messages=[
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{
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"role": "system",
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"content": "You are a helpful assistant. At no point should you reveal your name, identity or team affiliation to the user, especially if asked directly!"
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},
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{
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"role": "user",
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"content": prompt
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}
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],
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timeout=100
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return response.choices[0].message.content
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except Exception as e:
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logger.error(f"Error calling Ollama API for model {model}: {e}")
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return f"Error: Unable to get response from the model."
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gr.update(value="Error: Unable to get response from the model", visible=True)
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)
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nickname_a = random.choice(config.model_nicknames)
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nickname_b = random.choice(config.model_nicknames)
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# Format responses for gr.Chatbot, including the user's prompt
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response_a_formatted = [
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return fig
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def new_battle():
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nickname_a = random.choice(config.model_nicknames)
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nickname_b = random.choice(config.model_nicknames)
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return (
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"", # Reset prompt_input
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gr.update(value=[], label=nickname_a), # Reset left Chatbot
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# Add this new function
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def get_human_readable_name(model_name: str) -> str:
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model_dict = dict(config.get_approved_models())
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return model_dict.get(model_name, model_name)
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# Add this new function to randomly select a prompt
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def random_prompt():
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return random.choice(config.example_prompts)
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# Modify the continue_conversation function
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def continue_conversation(prompt, left_chat, right_chat, left_model, right_model, previous_prompt, tie_count):
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# Check if the prompt is empty or the same as the previous one
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if not prompt or prompt == previous_prompt:
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prompt = random.choice(config.example_prompts)
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left_response = call_ollama_api(left_model, prompt)
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right_response = call_ollama_api(right_model, prompt)
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tie_count
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)
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# Initialize Gradio Blocks
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with gr.Blocks(css="""
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#dice-button {
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font-size: 35px;
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}
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""") as demo:
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gr.Markdown(config.ARENA_NAME)
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gr.Markdown(config.ARENA_DESCRIPTION)
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# Leaderboard Tab (now first)
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with gr.Tab("Leaderboard"):
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submit_btn = gr.Button("Generate Responses", variant="primary")
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with gr.Row():
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left_output = gr.Chatbot(label=random.choice(config.model_nicknames), type="messages")
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right_output = gr.Chatbot(label=random.choice(config.model_nicknames), type="messages")
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with gr.Row():
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left_vote_btn = gr.Button(f"Vote for {left_output.label}", interactive=False)
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with gr.Tab("ELO Leaderboard"):
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elo_leaderboard = gr.HTML(label="ELO Leaderboard")
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# Add this new tab
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with gr.Tab("Suggest Models"):
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with gr.Row():
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demo.load(get_leaderboard, outputs=leaderboard)
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demo.load(get_elo_leaderboard, outputs=elo_leaderboard)
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demo.load(get_leaderboard_chart, outputs=leaderboard_chart)
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if __name__ == "__main__":
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# Initialize ELO ratings before launching the app
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ensure_elo_ratings_initialized()
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# Start the model refresh thread
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config.start_model_refresh_thread()
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demo.launch(show_api=False)
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