Spaces:
Running
Running
File size: 13,435 Bytes
631fbda e19f726 631fbda e19f726 631fbda e19f726 631fbda e19f726 631fbda e19f726 631fbda e19f726 631fbda |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 |
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()
# Calculate scores for each model
for model, results in battle_results.items():
total_battles = results["wins"] + results["losses"]
if total_battles > 0:
win_rate = results["wins"] / total_battles
# Score formula: win_rate * (1 - 1 / (total_battles + 1))
# This gives more weight to models with more battles
results["score"] = win_rate * (1 - 1 / (total_battles + 1))
else:
results["score"] = 0
# Sort results by score, then by total battles
sorted_results = sorted(
battle_results.items(),
key=lambda x: (x[1]["score"], 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>Score</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['score']:.4f}</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()
|