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"""
This module contains functions for generating responses using LLMs.
"""
import enum
from random import sample
from typing import List
from uuid import uuid4
from firebase_admin import firestore
import gradio as gr
from leaderboard import db
from model import completion
from model import Model
from model import supported_models
def create_history(model_name: str, instruction: str, prompt: str,
response: str):
doc_id = uuid4().hex
doc = {
"id": doc_id,
"model": model_name,
"instruction": instruction,
"prompt": prompt,
"response": response,
"timestamp": firestore.SERVER_TIMESTAMP
}
doc_ref = db.collection("arena-history").document(doc_id)
doc_ref.set(doc)
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, maintaining the original language of the text in the summary." # pylint: disable=line-too-long
if category == Category.TRANSLATE.value:
return f"Translate the following text from {source_lang} to {target_lang}."
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: List[Model] = sample(list(supported_models), 2)
instruction = get_instruction(category, source_lang, target_lang)
responses = []
for model in models:
try:
# TODO(#1): Allow user to set configuration.
response = completion(model=model,
messages=[{
"role": "system",
"content": instruction
}, {
"role": "user",
"content": user_prompt
}])
create_history(model.name, instruction, user_prompt, response)
responses.append(response)
# TODO(#1): Narrow down the exception type.
except Exception as e: # pylint: disable=broad-except
print(f"Error with model {model.name}: {e}")
raise gr.Error("Failed to get response. Please try again.")
model_names = [model.name for model in models]
# It simulates concurrent stream response generation.
max_response_length = max(len(response) for response in responses)
for i in range(max_response_length):
yield [response[:i + 1] for response in responses
] + model_names + [instruction]
yield responses + model_names + [instruction]
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