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import gradio as gr
from functools import lru_cache
import random
import requests
import logging
import arena_config
import plotly.graph_objects as go
from typing import Dict
from leaderboard import get_current_leaderboard, update_leaderboard
# Initialize logging for errors only
logging.basicConfig(level=logging.ERROR)
logger = logging.getLogger(__name__)
# Function to get available models (using predefined list)
def get_available_models():
return [model[0] for model in arena_config.APPROVED_MODELS]
# Function to call Ollama API with caching
@lru_cache(maxsize=100)
def call_ollama_api(model, prompt):
payload = {
"model": model,
"messages": [{"role": "user", "content": prompt}],
}
try:
response = requests.post(
f"{arena_config.API_URL}/v1/chat/completions",
headers=arena_config.HEADERS,
json=payload,
timeout=100
)
response.raise_for_status()
data = response.json()
return data["choices"][0]["message"]["content"]
except requests.exceptions.RequestException as e:
logger.error(f"Error calling Ollama API for model {model}: {e}")
return f"Error: Unable to get response from the model."
# Generate responses using two randomly selected models
def generate_responses(prompt):
available_models = get_available_models()
if len(available_models) < 2:
return "Error: Not enough models available", "Error: Not enough models available", None, None
selected_models = random.sample(available_models, 2)
model_a, model_b = selected_models
model_a_response = call_ollama_api(model_a, prompt)
model_b_response = call_ollama_api(model_b, prompt)
return model_a_response, model_b_response, model_a, model_b
def battle_arena(prompt):
response_a, response_b, model_a, model_b = generate_responses(prompt)
nickname_a = random.choice(arena_config.model_nicknames)
nickname_b = random.choice(arena_config.model_nicknames)
# Format responses for gr.Chatbot
response_a_formatted = [{"role": "assistant", "content": response_a}]
response_b_formatted = [{"role": "assistant", "content": response_b}]
if random.choice([True, False]):
return (
response_a_formatted, response_b_formatted, model_a, model_b,
gr.update(label=nickname_a, value=response_a_formatted),
gr.update(label=nickname_b, value=response_b_formatted),
gr.update(interactive=True, value=f"Vote for {nickname_a}"),
gr.update(interactive=True, value=f"Vote for {nickname_b}")
)
else:
return (
response_b_formatted, response_a_formatted, model_b, model_a,
gr.update(label=nickname_a, value=response_b_formatted),
gr.update(label=nickname_b, value=response_a_formatted),
gr.update(interactive=True, value=f"Vote for {nickname_a}"),
gr.update(interactive=True, value=f"Vote for {nickname_b}")
)
def record_vote(prompt, left_response, right_response, left_model, right_model, choice):
# Check if outputs are generated
if not left_response or not right_response or not left_model or not right_model:
return (
"Please generate responses before voting.",
gr.update(),
gr.update(interactive=False),
gr.update(interactive=False),
gr.update(visible=False),
gr.update()
)
winner = left_model if choice == "Left is better" else right_model
loser = right_model if choice == "Left is better" else left_model
# Update the leaderboard
battle_results = update_leaderboard(winner, loser)
result_message = f"""
π Vote recorded! You're awesome! π
π΅ In the left corner: {get_human_readable_name(left_model)}
π΄ In the right corner: {get_human_readable_name(right_model)}
π And the champion you picked is... {get_human_readable_name(winner)}! π₯
"""
return (
gr.update(value=result_message, visible=True), # Show result as Markdown
get_leaderboard(), # Update leaderboard
gr.update(interactive=False), # Disable left vote button
gr.update(interactive=False), # Disable right vote button
gr.update(visible=True), # Show model names
get_leaderboard_chart() # Update leaderboard chart
)
def get_leaderboard():
battle_results = get_current_leaderboard()
sorted_results = sorted(
battle_results.items(),
key=lambda x: (x[1]["wins"], -x[1]["losses"]),
reverse=True
)
leaderboard = """
<style>
.leaderboard-table {
width: 100%;
border-collapse: collapse;
font-family: Arial, sans-serif;
}
.leaderboard-table th, .leaderboard-table td {
border: 1px solid #ddd;
padding: 8px;
text-align: left;
}
.leaderboard-table th {
background-color: rgba(255, 255, 255, 0.1);
font-weight: bold;
}
.rank-column {
width: 60px;
text-align: center;
}
.opponent-details {
font-size: 0.9em;
color: #888;
}
</style>
<table class='leaderboard-table'>
<tr>
<th class='rank-column'>Rank</th>
<th>Model</th>
<th>Wins</th>
<th>Losses</th>
<th>Win Rate</th>
<th>Total Battles</th>
<th>Top Rival</th>
<th>Toughest Opponent</th>
</tr>
"""
for index, (model, results) in enumerate(sorted_results, start=1):
total_battles = results["wins"] + results["losses"]
win_rate = (results["wins"] / total_battles * 100) if total_battles > 0 else 0
if index == 1:
rank_display = "π₯"
elif index == 2:
rank_display = "π₯"
elif index == 3:
rank_display = "π₯"
else:
rank_display = f"{index}"
# Find top rival (most wins against)
top_rival = max(results["opponents"].items(), key=lambda x: x[1]["wins"], default=(None, {"wins": 0}))
top_rival_name = get_human_readable_name(top_rival[0]) if top_rival[0] else "N/A"
top_rival_wins = top_rival[1]["wins"]
# Find toughest opponent (most losses against)
toughest_opponent = max(results["opponents"].items(), key=lambda x: x[1]["losses"], default=(None, {"losses": 0}))
toughest_opponent_name = get_human_readable_name(toughest_opponent[0]) if toughest_opponent[0] else "N/A"
toughest_opponent_losses = toughest_opponent[1]["losses"]
leaderboard += f"""
<tr>
<td class='rank-column'>{rank_display}</td>
<td>{get_human_readable_name(model)}</td>
<td>{results['wins']}</td>
<td>{results['losses']}</td>
<td>{win_rate:.2f}%</td>
<td>{total_battles}</td>
<td class='opponent-details'>{top_rival_name} (W: {top_rival_wins})</td>
<td class='opponent-details'>{toughest_opponent_name} (L: {toughest_opponent_losses})</td>
</tr>
"""
leaderboard += "</table>"
return leaderboard
def get_leaderboard_chart():
battle_results = get_current_leaderboard()
sorted_results = sorted(
battle_results.items(),
key=lambda x: (x[1]["wins"], -x[1]["losses"]),
reverse=True
)
models = [get_human_readable_name(model) for model, _ in sorted_results]
wins = [results["wins"] for _, results in sorted_results]
losses = [results["losses"] for _, results in sorted_results]
fig = go.Figure()
# Stacked Bar chart for Wins and Losses
fig.add_trace(go.Bar(
x=models,
y=wins,
name='Wins',
marker_color='#22577a'
))
fig.add_trace(go.Bar(
x=models,
y=losses,
name='Losses',
marker_color='#38a3a5'
))
# Update layout for full-width and increased height
fig.update_layout(
title='Model Performance',
xaxis_title='Models',
yaxis_title='Number of Battles',
barmode='stack',
height=800,
width=1450,
autosize=True,
legend=dict(
orientation='h',
yanchor='bottom',
y=1.02,
xanchor='right',
x=1
)
)
return fig
def new_battle():
nickname_a = random.choice(arena_config.model_nicknames)
nickname_b = random.choice(arena_config.model_nicknames)
return (
"", # Reset prompt_input
gr.update(value=[], label=nickname_a), # Reset left Chatbot
gr.update(value=[], label=nickname_b), # Reset right Chatbot
None,
None,
gr.update(interactive=False, value=f"Vote for {nickname_a}"),
gr.update(interactive=False, value=f"Vote for {nickname_b}"),
gr.update(value="", visible=False),
gr.update(),
gr.update(visible=False),
gr.update()
)
# Add this new function
def get_human_readable_name(model_name: str) -> str:
model_dict = dict(arena_config.APPROVED_MODELS)
return model_dict.get(model_name, model_name)
# Add this new function to randomly select a prompt
def random_prompt():
return random.choice(arena_config.example_prompts)
# Initialize Gradio Blocks
with gr.Blocks(css="""
#dice-button {
min-height: 90px;
font-size: 35px;
}
""") as demo:
gr.Markdown(arena_config.ARENA_NAME)
gr.Markdown(arena_config.ARENA_DESCRIPTION)
# Battle Arena Tab
with gr.Tab("Battle Arena"):
with gr.Row():
prompt_input = gr.Textbox(
label="Enter your prompt",
placeholder="Type your prompt here...",
scale=20
)
random_prompt_btn = gr.Button("π²", scale=1, elem_id="dice-button")
gr.Markdown("<br>")
# Add the random prompt button functionality
random_prompt_btn.click(
random_prompt,
outputs=prompt_input
)
submit_btn = gr.Button("Generate Responses", variant="primary")
with gr.Row():
left_output = gr.Chatbot(label=random.choice(arena_config.model_nicknames), type="messages")
right_output = gr.Chatbot(label=random.choice(arena_config.model_nicknames), type="messages")
with gr.Row():
left_vote_btn = gr.Button(f"Vote for {left_output.label}", interactive=False)
right_vote_btn = gr.Button(f"Vote for {right_output.label}", interactive=False)
result = gr.Textbox(label="Result", interactive=False, visible=False)
with gr.Row(visible=False) as model_names_row:
left_model = gr.Textbox(label="π΅ Left Model", interactive=False)
right_model = gr.Textbox(label="π΄ Right Model", interactive=False)
new_battle_btn = gr.Button("New Battle")
# Leaderboard Tab
with gr.Tab("Leaderboard"):
leaderboard = gr.HTML(label="Leaderboard")
# Performance Chart Tab
with gr.Tab("Performance Chart"):
leaderboard_chart = gr.Plot(label="Model Performance Chart")
# Define interactions
submit_btn.click(
battle_arena,
inputs=prompt_input,
outputs=[left_output, right_output, left_model, right_model,
left_output, right_output, left_vote_btn, right_vote_btn]
)
left_vote_btn.click(
lambda *args: record_vote(*args, "Left is better"),
inputs=[prompt_input, left_output, right_output, left_model, right_model],
outputs=[result, leaderboard, left_vote_btn,
right_vote_btn, model_names_row, leaderboard_chart]
)
right_vote_btn.click(
lambda *args: record_vote(*args, "Right is better"),
inputs=[prompt_input, left_output, right_output, left_model, right_model],
outputs=[result, leaderboard, left_vote_btn,
right_vote_btn, model_names_row, leaderboard_chart]
)
new_battle_btn.click(
new_battle,
outputs=[prompt_input, left_output, right_output, left_model,
right_model, left_vote_btn, right_vote_btn,
result, leaderboard, model_names_row, leaderboard_chart]
)
# Update leaderboard and chart on launch
demo.load(get_leaderboard, outputs=leaderboard)
demo.load(get_leaderboard_chart, outputs=leaderboard_chart)
if __name__ == "__main__":
demo.launch()
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