File size: 14,730 Bytes
7db401b 9805ef5 7db401b |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 |
import json
import re
import gradio as gr
from dotenv import load_dotenv
load_dotenv()
from .gen_api_answer import (
get_atla_response,
get_selene_mini_response,
parse_selene_mini_response
)
from .prompts import (
DEFAULT_EVAL_CRITERIA,
DEFAULT_EVAL_PROMPT,
DEFAULT_EVAL_PROMPT_EDITABLE,
ATLA_PROMPT,
ATLA_PROMPT_WITH_REFERENCE
)
from .random_sample_generation import (
get_random_human_ai_pair,
get_random_human_ai_ground_truth_pair,
generate_ai_response
)
from common import CSS_STYLES, MAIN_TITLE, HOW_IT_WORKS
def parse_variables(prompt):
# Extract variables enclosed in double curly braces
variables = re.findall(r"{{(.*?)}}", prompt)
# Remove duplicates while preserving order
seen = set()
variables = [
x.strip() for x in variables if not (x.strip() in seen or seen.add(x.strip()))
]
return variables
def get_final_prompt(eval_prompt, variable_values):
# Replace variables in the eval prompt with their values
for var, val in variable_values.items():
eval_prompt = eval_prompt.replace("{{" + var + "}}", val)
return eval_prompt
def populate_random_example(request: gr.Request, compatible_mode: bool):
"""Generate a random human-AI conversation example and reset judge outputs."""
if compatible_mode:
human_msg, ai_msg, ground_truth_msg = get_random_human_ai_ground_truth_pair()
else:
human_msg, ai_msg = get_random_human_ai_pair()
ground_truth_msg = ""
return [
gr.update(value=human_msg),
gr.update(value=ai_msg),
gr.update(value="🎲", variant="secondary"),
gr.update(value=""), # Clear score
gr.update(value=""), # Clear critique
gr.update(value=ground_truth_msg, visible=compatible_mode), # Set ground truth and visibility
]
def create_arena_interface():
with gr.Blocks(theme="default", css=CSS_STYLES) as interface:
# Hidden eval prompt that will always contain DEFAULT_EVAL_PROMPT
eval_prompt = gr.Textbox(
value=DEFAULT_EVAL_PROMPT,
visible=False
)
with gr.Row():
# Add model selector dropdown at the top
model_selector = gr.Dropdown(
choices=["Selene", "Selene Mini"],
value="Selene",
label="Choose your Atla Model",
interactive=True
)
with gr.Row():
# Left side - Input section
with gr.Column(scale=1):
with gr.Group():
human_input = gr.TextArea(
label="👩 User Input",
lines=5,
placeholder="Enter the human message here..."
)
with gr.Row():
generate_btn = gr.Button(
"Generate AI Response",
size="sm",
interactive=False
)
ai_response = gr.TextArea(
label="🤖 AI Response",
lines=10,
placeholder="Enter the AI response here..."
)
# Ground truth response (initially hidden)
ground_truth = gr.TextArea(
label="🎯 Ground truth response",
lines=10,
placeholder="Enter the ground truth response here...",
visible=False
)
with gr.Row():
random_btn = gr.Button("🎲", scale=2)
send_btn = gr.Button(
value="Run evaluation",
variant="primary",
size="lg",
scale=8
)
# Right side - Model outputs
with gr.Column(scale=1):
gr.Markdown("## 👩⚖️ Atla Evaluation")
with gr.Group():
with gr.Row():
score = gr.Textbox(label="Score", lines=1, interactive=False)
critique = gr.TextArea(label="Critique", lines=12, interactive=False)
gr.Markdown("<br>")
# Replace the "Edit Judge Prompt" Accordion section with:
with gr.Accordion("📝 Edit Judge Prompt", open=False) as prompt_accordion:
gr.Markdown("<br>")
use_reference_toggle = gr.Checkbox(
label="Use a reference response",
value=False
)
# Hide the default prompt editor
with gr.Column(visible=False) as default_prompt_editor:
eval_prompt_editable = gr.TextArea(
value=DEFAULT_EVAL_PROMPT_EDITABLE,
label="Evaluation Criteria",
lines=12
)
with gr.Row(visible=False) as edit_buttons_row:
cancel_prompt_btn = gr.Button("Cancel")
save_prompt_btn = gr.Button("Save", variant="primary")
# Show the compatible mode editor
with gr.Column(visible=True) as compatible_prompt_editor:
eval_criteria_text = gr.TextArea(
label="Evaluation Criteria",
lines=12,
value=DEFAULT_EVAL_CRITERIA,
placeholder="Enter the complete evaluation criteria and scoring rubric..."
)
with gr.Row(visible=False) as compatible_edit_buttons_row:
compatible_cancel_btn = gr.Button("Cancel")
compatible_save_btn = gr.Button("Save", variant="primary")
eval_prompt_previous = gr.State(value=DEFAULT_EVAL_PROMPT_EDITABLE) # Initialize with default value
is_editing = gr.State(False) # Track editing state
compatible_mode_state = gr.State(False) # Track compatible mode state
# Update model names after responses are generated
def update_model_names(model_a, model_b):
return gr.update(value=f"*Model: {model_a}*"), gr.update(
value=f"*Model: {model_b}*"
)
# Store the last submitted prompt and variables for comparison
last_submission = gr.State({})
# Update the save/cancel buttons section in the compatible prompt editor
def save_criteria(new_criteria, previous_criteria):
return [
gr.update(value=new_criteria), # Update the criteria
new_criteria, # Update the previous criteria state
gr.update(visible=False) # Hide the buttons
]
def cancel_criteria(previous_criteria):
return [
gr.update(value=previous_criteria), # Revert to previous criteria
previous_criteria, # Keep the previous criteria state
gr.update(visible=False) # Hide the buttons
]
def show_criteria_edit_buttons(current_value, previous_value):
# Show buttons only if the current value differs from the previous value
return gr.update(visible=current_value != previous_value)
# Add handlers for save/cancel buttons and criteria changes
compatible_save_btn.click(
fn=save_criteria,
inputs=[eval_criteria_text, eval_prompt_previous],
outputs=[eval_criteria_text, eval_prompt_previous, compatible_edit_buttons_row]
)
compatible_cancel_btn.click(
fn=cancel_criteria,
inputs=[eval_prompt_previous],
outputs=[eval_criteria_text, eval_prompt_previous, compatible_edit_buttons_row]
)
eval_criteria_text.change(
fn=show_criteria_edit_buttons,
inputs=[eval_criteria_text, eval_prompt_previous],
outputs=compatible_edit_buttons_row
)
# Function to toggle visibility based on compatible mode
def toggle_use_reference(checked):
if checked:
human_msg, ai_msg, ground_truth_msg = get_random_human_ai_ground_truth_pair()
return {
ground_truth: gr.update(visible=True, value=ground_truth_msg),
human_input: gr.update(value=human_msg),
ai_response: gr.update(value=ai_msg),
score: gr.update(value=""),
critique: gr.update(value=""),
random_btn: gr.update(value="🎲", variant="secondary"),
}
else:
return {
ground_truth: gr.update(visible=False)
}
# Update the change handler to include all necessary outputs
use_reference_toggle.change(
fn=toggle_use_reference,
inputs=[use_reference_toggle],
outputs=[
ground_truth,
human_input,
ai_response,
score,
critique,
random_btn,
]
)
# Add a new state variable to track first game
first_game_state = gr.State(True) # Initialize as True
# Update the submit function to handle both models
def submit_and_store(
model_choice,
use_reference,
eval_criteria_text,
human_input,
ai_response,
ground_truth,
):
if model_choice == "Selene Mini":
# Prepare prompt based on reference mode
prompt_template = ATLA_PROMPT_WITH_REFERENCE if use_reference else ATLA_PROMPT
prompt = prompt_template.format(
human_input=human_input,
ai_response=ai_response,
eval_criteria=eval_criteria_text,
ground_truth=ground_truth if use_reference else ""
)
print("\n=== Debug: Prompt being sent to Selene Mini ===")
print(prompt)
print("============================================\n")
# Get and parse response
raw_response = get_selene_mini_response(
model_name="AtlaAI/Selene-1-Mini-Llama-3.1-8B",
prompt=prompt,
max_tokens=500,
temperature=0.01
)
response = parse_selene_mini_response(raw_response)
else:
# Selene API logic
prompt_data = {
'human_input': human_input,
'ai_response': ai_response,
'ground_truth': ground_truth if use_reference else None,
'eval_criteria': eval_criteria_text,
}
print("\n=== Debug: Prompt data being sent to Selene API ===")
print(json.dumps(prompt_data, indent=2))
print("============================================\n")
response = get_atla_response(
model_name="AtlaAI/Selene-1-Mini-Llama-3.1-8B",
prompt=prompt_data,
max_tokens=500,
temperature=0.01
)
# Response now contains score and critique directly
if isinstance(response, dict) and 'score' in response and 'critique' in response:
score = str(response['score'])
critique = response['critique']
else:
score = "Error"
critique = str(response)
return [
score,
critique,
gr.update(value="Regenerate evaluation", variant="secondary", interactive=True),
gr.update(value="🎲"),
]
# Update the send_btn click handler with new input
send_btn.click(
fn=submit_and_store,
inputs=[
model_selector,
use_reference_toggle,
eval_criteria_text,
human_input,
ai_response,
ground_truth,
],
outputs=[
score,
critique,
send_btn,
random_btn,
],
)
# Add random button handler
random_btn.click(
fn=populate_random_example,
inputs=[use_reference_toggle],
outputs=[
human_input,
ai_response,
random_btn,
score,
critique,
ground_truth,
]
)
# Add input change handlers
def handle_input_change():
"""Reset UI state when inputs are changed"""
return [
gr.update(value="Run evaluation", variant="primary"), # send_btn
gr.update(value="🎲", variant="secondary"), # random_btn
]
# Update the change handlers for inputs
human_input.change(
fn=handle_input_change,
inputs=[],
outputs=[send_btn, random_btn]
)
ai_response.change(
fn=handle_input_change,
inputs=[],
outputs=[send_btn, random_btn]
)
generate_btn.click(
fn=lambda msg: (
generate_ai_response(msg)[0], # Only take the response text
gr.update(
value="Generate AI Response", # Keep the label
interactive=False # Disable the button
)
),
inputs=[human_input],
outputs=[ai_response, generate_btn]
)
human_input.change(
fn=lambda x: gr.update(interactive=bool(x.strip())),
inputs=[human_input],
outputs=[generate_btn]
)
# Update the demo.load to include the random example population
interface.load(
fn=lambda: populate_random_example(None, False), # Pass False for initial compatible_mode
inputs=[],
outputs=[
human_input,
ai_response,
random_btn,
score,
critique,
ground_truth,
]
)
return interface
if __name__ == "__main__":
demo = create_arena_interface()
demo.launch() |