arena / response.py
Kang Suhyun
[#7] Display model names only after voting (#16)
f00b7ff unverified
raw
history blame
2.92 kB
"""
This module contains functions for generating responses using LLMs.
"""
import enum
from random import sample
import gradio as gr
from litellm import completion
# TODO(#1): Add more models.
SUPPORTED_MODELS = [
"gpt-4", "gpt-4-0125-preview", "gpt-3.5-turbo", "gemini-pro"
]
class Category(enum.Enum):
SUMMARIZE = "Summarize"
TRANSLATE = "Translate"
# TODO(#31): Let the model builders set the instruction.
def get_instruction(category, source_lang, target_lang):
if category == Category.SUMMARIZE.value:
return "Summarize the following text in its original language."
if category == Category.TRANSLATE.value:
return f"Translate the following text from {source_lang} to {target_lang}."
def response_generator(response: str):
for part in response:
content = part.choices[0].delta.content
if content is None:
continue
# To simulate a stream, we yield each character of the response.
for character in content:
yield character
# TODO(#29): Return results simultaneously to prevent bias from generation speed.
def get_responses(user_prompt, category, source_lang, target_lang):
if not category:
raise gr.Error("Please select a category.")
if category == Category.TRANSLATE.value and (not source_lang or
not target_lang):
raise gr.Error("Please select source and target languages.")
models = sample(SUPPORTED_MODELS, 2)
instruction = get_instruction(category, source_lang, target_lang)
generators = []
for model in models:
try:
# TODO(#1): Allow user to set configuration.
response = completion(model=model,
messages=[{
"content": instruction,
"role": "system"
}, {
"content": user_prompt,
"role": "user"
}],
stream=True)
generators.append(response_generator(response))
# TODO(#1): Narrow down the exception type.
except Exception as e: # pylint: disable=broad-except
print(f"Error in bot_response: {e}")
raise e
responses = ["", ""]
# It simulates concurrent response generation from two models.
while True:
stop = True
for i in range(len(generators)):
try:
yielded = next(generators[i])
if yielded is None:
continue
responses[i] += yielded
stop = False
yield responses + models + [
gr.Button(interactive=True) for _ in range(3)
] + [instruction, gr.Row(visible=False)]
except StopIteration:
pass
# TODO(#1): Narrow down the exception type.
except Exception as e: # pylint: disable=broad-except
print(f"Error in generator: {e}")
raise e
if stop:
break