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import asyncio
import functools
import math
import os
import tempfile
import traceback
import uuid
from typing import Any, Tuple
import aiohttp
import backoff
import discord
# An enum of two modes, TOP_P or TEMPERATURE
import requests
from PIL import Image
from discord import File
class Mode:
TEMPERATURE = "temperature"
TOP_P = "top_p"
ALL_MODES = [TEMPERATURE, TOP_P]
class Models:
# Text models
DAVINCI = "text-davinci-003"
CURIE = "text-curie-001"
BABBAGE = "text-babbage-001"
ADA = "text-ada-001"
# Code models
CODE_DAVINCI = "code-davinci-002"
CODE_CUSHMAN = "code-cushman-001"
# Embedding models
EMBEDDINGS = "text-embedding-ada-002"
# Edit models
EDIT = "text-davinci-edit-001"
CODE_EDIT = "code-davinci-edit-001"
# Model collections
TEXT_MODELS = [DAVINCI, CURIE, BABBAGE, ADA, CODE_DAVINCI, CODE_CUSHMAN]
EDIT_MODELS = [EDIT, CODE_EDIT]
DEFAULT = DAVINCI
LOW_USAGE_MODEL = CURIE
class ImageSize:
SMALL = "256x256"
MEDIUM = "512x512"
LARGE = "1024x1024"
ALL_SIZES = [SMALL, MEDIUM, LARGE]
class ModelLimits:
MIN_TOKENS = 15
MAX_TOKENS = 4096
MIN_CONVERSATION_LENGTH = 1
MAX_CONVERSATION_LENGTH = 500
MIN_SUMMARIZE_THRESHOLD = 800
MAX_SUMMARIZE_THRESHOLD = 3500
MIN_NUM_IMAGES = 1
MAX_NUM_IMAGES = 4
MIN_NUM_STATIC_CONVERSATION_ITEMS = 5
MAX_NUM_STATIC_CONVERSATION_ITEMS = 20
MIN_NUM_CONVERSATION_LOOKBACK = 5
MAX_NUM_CONVERSATION_LOOKBACK = 15
MIN_TEMPERATURE = 0.0
MAX_TEMPERATURE = 2.0
MIN_TOP_P = 0.0
MAX_TOP_P = 1.0
MIN_PRESENCE_PENALTY = -2.0
MAX_PRESENCE_PENALTY = 2.0
MIN_FREQUENCY_PENALTY = -2.0
MAX_FREQUENCY_PENALTY = 2.0
MIN_BEST_OF = 1
MAX_BEST_OF = 3
MIN_PROMPT_MIN_LENGTH = 10
MAX_PROMPT_MIN_LENGTH = 4096
class Model:
def __init__(self, usage_service):
self._mode = Mode.TEMPERATURE
self._temp = 0.8 # Higher value means more random, lower value means more likely to be a coherent sentence
self._top_p = 0.95 # 1 is equivalent to greedy sampling, 0.1 means that the model will only consider the top 10% of the probability distribution
self._max_tokens = 4000 # The maximum number of tokens the model can generate
self._presence_penalty = (
0 # Penalize new tokens based on whether they appear in the text so far
)
# Penalize new tokens based on their existing frequency in the text so far. (Higher frequency = lower probability of being chosen.)
self._frequency_penalty = 0
self._best_of = 1 # Number of responses to compare the loglikelihoods of
self._prompt_min_length = 8
self._max_conversation_length = 100
self._model = Models.DEFAULT
self._low_usage_mode = False
self.usage_service = usage_service
self.DAVINCI_ROLES = ["admin", "Admin", "GPT", "gpt"]
self._image_size = ImageSize.MEDIUM
self._num_images = 2
self._summarize_conversations = True
self._summarize_threshold = 3000
self.model_max_tokens = 4024
self._welcome_message_enabled = True
self._num_static_conversation_items = 10
self._num_conversation_lookback = 5
try:
self.IMAGE_SAVE_PATH = os.environ["IMAGE_SAVE_PATH"]
self.custom_image_path = True
except Exception:
self.IMAGE_SAVE_PATH = "dalleimages"
# Try to make this folder called images/ in the local directory if it doesnt exist
if not os.path.exists(self.IMAGE_SAVE_PATH):
os.makedirs(self.IMAGE_SAVE_PATH)
self.custom_image_path = False
self._hidden_attributes = [
"usage_service",
"DAVINCI_ROLES",
"custom_image_path",
"custom_web_root",
"_hidden_attributes",
"model_max_tokens",
"openai_key",
]
self.openai_key = os.getenv("OPENAI_TOKEN")
# Use the @property and @setter decorators for all the self fields to provide value checking
@property
def num_static_conversation_items(self):
return self._num_static_conversation_items
@num_static_conversation_items.setter
def num_static_conversation_items(self, value):
value = int(value)
if value < ModelLimits.MIN_NUM_STATIC_CONVERSATION_ITEMS:
raise ValueError(f"Number of static conversation items must be >= {ModelLimits.MIN_NUM_STATIC_CONVERSATION_ITEMS}")
if value > ModelLimits.MAX_NUM_STATIC_CONVERSATION_ITEMS:
raise ValueError(
f"Number of static conversation items must be <= {ModelLimits.MAX_NUM_STATIC_CONVERSATION_ITEMS}, this is to ensure reliability and reduce token wastage!"
)
self._num_static_conversation_items = value
@property
def num_conversation_lookback(self):
return self._num_conversation_lookback
@num_conversation_lookback.setter
def num_conversation_lookback(self, value):
value = int(value)
if value < ModelLimits.MIN_NUM_CONVERSATION_LOOKBACK:
raise ValueError(f"Number of conversations to look back on must be >= {ModelLimits.MIN_NUM_CONVERSATION_LOOKBACK}")
if value > ModelLimits.MAX_NUM_CONVERSATION_LOOKBACK:
raise ValueError(
f"Number of conversations to look back on must be <= {ModelLimits.MIN_NUM_CONVERSATION_LOOKBACK}, this is to ensure reliability and reduce token wastage!"
)
self._num_conversation_lookback = value
@property
def welcome_message_enabled(self):
return self._welcome_message_enabled
@welcome_message_enabled.setter
def welcome_message_enabled(self, value):
if value.lower() == "true":
self._welcome_message_enabled = True
elif value.lower() == "false":
self._welcome_message_enabled = False
else:
raise ValueError("Value must be either `true` or `false`!")
@property
def summarize_threshold(self):
return self._summarize_threshold
@summarize_threshold.setter
def summarize_threshold(self, value):
value = int(value)
if value < ModelLimits.MIN_SUMMARIZE_THRESHOLD or value > ModelLimits.MAX_SUMMARIZE_THRESHOLD:
raise ValueError(
f"Summarize threshold should be a number between {ModelLimits.MIN_SUMMARIZE_THRESHOLD} and {ModelLimits.MAX_SUMMARIZE_THRESHOLD}!"
)
self._summarize_threshold = value
@property
def summarize_conversations(self):
return self._summarize_conversations
@summarize_conversations.setter
def summarize_conversations(self, value):
# convert value string into boolean
if value.lower() == "true":
value = True
elif value.lower() == "false":
value = False
else:
raise ValueError("Value must be either `true` or `false`!")
self._summarize_conversations = value
@property
def image_size(self):
return self._image_size
@image_size.setter
def image_size(self, value):
if value in ImageSize.ALL_SIZES:
self._image_size = value
else:
raise ValueError(
f"Image size must be one of the following: {ImageSize.ALL_SIZES}"
)
@property
def num_images(self):
return self._num_images
@num_images.setter
def num_images(self, value):
value = int(value)
if value < ModelLimits.MIN_NUM_IMAGES or value > ModelLimits.MAX_NUM_IMAGES:
raise ValueError(f"Number of images to generate should be a number between {ModelLimits.MIN_NUM_IMAGES} and {ModelLimits.MAX_NUM_IMAGES}!")
self._num_images = value
@property
def low_usage_mode(self):
return self._low_usage_mode
@low_usage_mode.setter
def low_usage_mode(self, value):
# convert value string into boolean
if value.lower() == "true":
value = True
elif value.lower() == "false":
value = False
else:
raise ValueError("Value must be either `true` or `false`!")
if value:
self._model = Models.LOW_USAGE_MODEL
self.max_tokens = 1900
self.model_max_tokens = 1000
else:
self._model = Models.DEFAULT
self.max_tokens = 4000
self.model_max_tokens = 4024
@property
def model(self):
return self._model
@model.setter
def model(self, model):
if model not in Models.TEXT_MODELS:
raise ValueError(f"Invalid model, must be one of: {Models.TEXT_MODELS}")
self._model = model
@property
def max_conversation_length(self):
return self._max_conversation_length
@max_conversation_length.setter
def max_conversation_length(self, value):
value = int(value)
if value < ModelLimits.MIN_CONVERSATION_LENGTH:
raise ValueError(f"Max conversation length must be greater than {ModelLimits.MIN_CONVERSATION_LENGTH}")
if value > ModelLimits.MAX_CONVERSATION_LENGTH:
raise ValueError(
f"Max conversation length must be less than {ModelLimits.MIN_CONVERSATION_LENGTH}, this will start using credits quick."
)
self._max_conversation_length = value
@property
def mode(self):
return self._mode
@mode.setter
def mode(self, value):
if value not in Mode.ALL_MODES:
raise ValueError(f"Mode must be one of: {Mode.ALL_MODES}")
if value == Mode.TOP_P:
self._top_p = 0.1
self._temp = 0.7
elif value == Mode.TEMPERATURE:
self._top_p = 0.9
self._temp = 0.6
else:
raise ValueError(f"Unknown mode: {value}")
self._mode = value
@property
def temp(self):
return self._temp
@temp.setter
def temp(self, value):
value = float(value)
if value < ModelLimits.MIN_TEMPERATURE or value > ModelLimits.MAX_TEMPERATURE:
raise ValueError(
f"Temperature must be between {ModelLimits.MIN_TEMPERATURE} and {ModelLimits.MAX_TEMPERATURE}, it is currently: {value}"
)
self._temp = value
@property
def top_p(self):
return self._top_p
@top_p.setter
def top_p(self, value):
value = float(value)
if value < ModelLimits.MIN_TOP_P or value > ModelLimits.MAX_TOP_P:
raise ValueError(
f"Top P must be between {ModelLimits.MIN_TOP_P} and {ModelLimits.MAX_TOP_P}, it is currently: {value}"
)
self._top_p = value
@property
def max_tokens(self):
return self._max_tokens
@max_tokens.setter
def max_tokens(self, value):
value = int(value)
if value < ModelLimits.MIN_TOKENS or value > ModelLimits.MAX_TOKENS:
raise ValueError(
f"Max tokens must be between {ModelLimits.MIN_TOKENS} and {ModelLimits.MAX_TOKENS}, it is currently: {value}"
)
self._max_tokens = value
@property
def presence_penalty(self):
return self._presence_penalty
@presence_penalty.setter
def presence_penalty(self, value):
value = float(value)
if value < ModelLimits.MIN_PRESENCE_PENALTY or value > ModelLimits.MAX_PRESENCE_PENALTY:
raise ValueError(
f"Presence penalty must be between {ModelLimits.MIN_PRESENCE_PENALTY} and {ModelLimits.MAX_PRESENCE_PENALTY}, it is currently: {value}"
)
self._presence_penalty = value
@property
def frequency_penalty(self):
return self._frequency_penalty
@frequency_penalty.setter
def frequency_penalty(self, value):
value = float(value)
if value < ModelLimits.MIN_FREQUENCY_PENALTY or value > ModelLimits.MAX_FREQUENCY_PENALTY:
raise ValueError(
f"Frequency penalty must be greater between {ModelLimits.MIN_FREQUENCY_PENALTY} and {ModelLimits.MAX_FREQUENCY_PENALTY}, it is currently: {value}"
)
self._frequency_penalty = value
@property
def best_of(self):
return self._best_of
@best_of.setter
def best_of(self, value):
value = int(value)
if value < ModelLimits.MIN_BEST_OF or value > ModelLimits.MAX_BEST_OF:
raise ValueError(
f"Best of must be between {ModelLimits.MIN_BEST_OF} and {ModelLimits.MAX_BEST_OF}, it is currently: {value}\nNote that increasing the value of this parameter will act as a multiplier on the number of tokens requested!"
)
self._best_of = value
@property
def prompt_min_length(self):
return self._prompt_min_length
@prompt_min_length.setter
def prompt_min_length(self, value):
value = int(value)
if value < ModelLimits.MIN_PROMPT_MIN_LENGTH or value > ModelLimits.MAX_PROMPT_MIN_LENGTH:
raise ValueError(
f"Minimal prompt length must be between {ModelLimits.MIN_PROMPT_MIN_LENGTH} and {ModelLimits.MAX_PROMPT_MIN_LENGTH}, it is currently: {value}"
)
self._prompt_min_length = value
def backoff_handler(details):
print(
f"Backing off {details['wait']:0.1f} seconds after {details['tries']} tries calling function {details['target']} | "
f"{details['exception'].status}: {details['exception'].message}"
)
async def valid_text_request(self, response):
try:
tokens_used = int(response["usage"]["total_tokens"])
await self.usage_service.update_usage(tokens_used)
except Exception as e:
raise ValueError(
"The API returned an invalid response: "
+ str(response["error"]["message"])
) from e
@backoff.on_exception(
backoff.expo,
aiohttp.ClientResponseError,
factor=3,
base=5,
max_tries=4,
on_backoff=backoff_handler,
)
async def send_embedding_request(self, text, custom_api_key=None):
async with aiohttp.ClientSession(raise_for_status=True) as session:
payload = {
"model": Models.EMBEDDINGS,
"input": text,
}
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {self.openai_key if not custom_api_key else custom_api_key}",
}
async with session.post(
"https://api.openai.com/v1/embeddings", json=payload, headers=headers
) as resp:
response = await resp.json()
try:
return response["data"][0]["embedding"]
except Exception:
print(response)
traceback.print_exc()
return
@backoff.on_exception(
backoff.expo,
aiohttp.ClientResponseError,
factor=3,
base=5,
max_tries=6,
on_backoff=backoff_handler,
)
async def send_edit_request(
self,
instruction,
text=None,
temp_override=None,
top_p_override=None,
codex=False,
custom_api_key=None,
):
# Validate that all the parameters are in a good state before we send the request
if len(instruction) < self.prompt_min_length:
raise ValueError(
"Instruction must be greater than 8 characters, it is currently "
+ str(len(instruction))
)
print(
f"The text about to be edited is [{text}] with instructions [{instruction}] codex [{codex}]"
)
print(f"Overrides -> temp:{temp_override}, top_p:{top_p_override}")
async with aiohttp.ClientSession(raise_for_status=True) as session:
payload = {
"model": Models.EDIT if codex is False else Models.CODE_EDIT,
"input": "" if text is None else text,
"instruction": instruction,
"temperature": self.temp if temp_override is None else temp_override,
"top_p": self.top_p if top_p_override is None else top_p_override,
}
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {self.openai_key if not custom_api_key else custom_api_key}",
}
async with session.post(
"https://api.openai.com/v1/edits", json=payload, headers=headers
) as resp:
response = await resp.json()
await self.valid_text_request(response)
return response
@backoff.on_exception(
backoff.expo,
aiohttp.ClientResponseError,
factor=3,
base=5,
max_tries=6,
on_backoff=backoff_handler,
)
async def send_moderations_request(self, text):
# Use aiohttp to send the above request:
async with aiohttp.ClientSession(raise_for_status=True) as session:
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {self.openai_key}",
}
payload = {"input": text}
async with session.post(
"https://api.openai.com/v1/moderations",
headers=headers,
json=payload,
) as response:
return await response.json()
@backoff.on_exception(
backoff.expo,
aiohttp.ClientResponseError,
factor=3,
base=5,
max_tries=4,
on_backoff=backoff_handler,
)
async def send_summary_request(self, prompt, custom_api_key=None):
"""
Sends a summary request to the OpenAI API
"""
summary_request_text = []
summary_request_text.append(
"The following is a conversation instruction set and a conversation between two people, a <username>, and GPTie."
" Firstly, determine the <username>'s name from the conversation history, then summarize the conversation."
" Do not summarize the instructions for GPTie, only the conversation. Summarize the conversation in a detailed fashion. If <username> mentioned"
" their name, be sure to mention it in the summary. Pay close attention to things the <username> has told you, such as personal details."
)
summary_request_text.append(prompt + "\nDetailed summary of conversation: \n")
summary_request_text = "".join(summary_request_text)
tokens = self.usage_service.count_tokens(summary_request_text)
async with aiohttp.ClientSession(raise_for_status=True) as session:
payload = {
"model": Models.DAVINCI,
"prompt": summary_request_text,
"temperature": 0.5,
"top_p": 1,
"max_tokens": self.max_tokens - tokens,
"presence_penalty": self.presence_penalty,
"frequency_penalty": self.frequency_penalty,
"best_of": self.best_of,
}
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {self.openai_key if not custom_api_key else custom_api_key}",
}
async with session.post(
"https://api.openai.com/v1/completions", json=payload, headers=headers
) as resp:
response = await resp.json()
await self.valid_text_request(response)
# print(response["choices"][0]["text"])
return response
@backoff.on_exception(
backoff.expo,
aiohttp.ClientResponseError,
factor=3,
base=5,
max_tries=4,
on_backoff=backoff_handler,
)
async def send_request(
self,
prompt,
tokens,
temp_override=None,
top_p_override=None,
best_of_override=None,
frequency_penalty_override=None,
presence_penalty_override=None,
max_tokens_override=None,
model=None,
stop=None,
custom_api_key=None,
) -> (
Tuple[dict, bool]
): # The response, and a boolean indicating whether or not the context limit was reached.
# Validate that all the parameters are in a good state before we send the request
if len(prompt) < self.prompt_min_length:
raise ValueError(
f"Prompt must be greater than {self.prompt_min_length} characters, it is currently: {len(prompt)} characters"
)
print(f"The prompt about to be sent is {prompt}")
print(
f"Overrides -> temp:{temp_override}, top_p:{top_p_override} frequency:{frequency_penalty_override}, presence:{presence_penalty_override}"
)
async with aiohttp.ClientSession(raise_for_status=True) as session:
payload = {
"model": self.model if model is None else model,
"prompt": prompt,
"stop": "" if stop is None else stop,
"temperature": self.temp if temp_override is None else temp_override,
"top_p": self.top_p if top_p_override is None else top_p_override,
"max_tokens": self.max_tokens - tokens
if not max_tokens_override
else max_tokens_override,
"presence_penalty": self.presence_penalty
if presence_penalty_override is None
else presence_penalty_override,
"frequency_penalty": self.frequency_penalty
if frequency_penalty_override is None
else frequency_penalty_override,
"best_of": self.best_of if not best_of_override else best_of_override,
}
headers = {
"Authorization": f"Bearer {self.openai_key if not custom_api_key else custom_api_key}"
}
async with session.post(
"https://api.openai.com/v1/completions", json=payload, headers=headers
) as resp:
response = await resp.json()
# print(f"Payload -> {payload}")
# Parse the total tokens used for this request and response pair from the response
await self.valid_text_request(response)
print(f"Response -> {response}")
return response
@staticmethod
async def send_test_request(api_key):
async with aiohttp.ClientSession() as session:
payload = {
"model": Models.LOW_USAGE_MODEL,
"prompt": "test.",
"temperature": 1,
"top_p": 1,
"max_tokens": 10,
}
headers = {"Authorization": f"Bearer {api_key}"}
async with session.post(
"https://api.openai.com/v1/completions", json=payload, headers=headers
) as resp:
response = await resp.json()
try:
int(response["usage"]["total_tokens"])
except:
raise ValueError(str(response["error"]["message"]))
return response
@backoff.on_exception(
backoff.expo,
aiohttp.ClientResponseError,
factor=3,
base=5,
max_tries=4,
on_backoff=backoff_handler,
)
async def send_image_request(
self, ctx, prompt, vary=None, custom_api_key=None
) -> tuple[File, list[Any]]:
# Validate that all the parameters are in a good state before we send the request
words = len(prompt.split(" "))
if words < 3 or words > 75:
raise ValueError(
"Prompt must be greater than 3 words and less than 75, it is currently "
+ str(words)
)
# print("The prompt about to be sent is " + prompt)
await self.usage_service.update_usage_image(self.image_size)
response = None
if not vary:
payload = {"prompt": prompt, "n": self.num_images, "size": self.image_size}
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {self.openai_key if not custom_api_key else custom_api_key}",
}
async with aiohttp.ClientSession(raise_for_status=True) as session:
async with session.post(
"https://api.openai.com/v1/images/generations",
json=payload,
headers=headers,
) as resp:
response = await resp.json()
else:
async with aiohttp.ClientSession(raise_for_status=True) as session:
data = aiohttp.FormData()
data.add_field("n", str(self.num_images))
data.add_field("size", self.image_size)
with open(vary, "rb") as f:
data.add_field(
"image", f, filename="file.png", content_type="image/png"
)
async with session.post(
"https://api.openai.com/v1/images/variations",
headers={
"Authorization": f"Bearer {self.openai_key if not custom_api_key else custom_api_key}",
},
data=data,
) as resp:
response = await resp.json()
print(response)
image_urls = []
for result in response["data"]:
image_urls.append(result["url"])
# For each image url, open it as an image object using PIL
images = await asyncio.get_running_loop().run_in_executor(
None,
lambda: [
Image.open(requests.get(url, stream=True, timeout=10).raw)
for url in image_urls
],
)
# Save all the images with a random name to self.IMAGE_SAVE_PATH
image_names = [f"{uuid.uuid4()}.png" for _ in range(len(images))]
for image, name in zip(images, image_names):
await asyncio.get_running_loop().run_in_executor(
None, image.save, f"{self.IMAGE_SAVE_PATH}/{name}"
)
# Update image_urls to include the local path to these new images
image_urls = [f"{self.IMAGE_SAVE_PATH}/{name}" for name in image_names]
widths, heights = zip(*(i.size for i in images))
# Calculate the number of rows and columns needed for the grid
num_rows = num_cols = int(math.ceil(math.sqrt(len(images))))
# If there are only 2 images, set the number of rows to 1
if len(images) == 2:
num_rows = 1
# Calculate the size of the combined image
width = max(widths) * num_cols
height = max(heights) * num_rows
# Create a transparent image with the same size as the images
transparent = await asyncio.get_running_loop().run_in_executor(
None, lambda: Image.new("RGBA", (max(widths), max(heights)))
)
# Create a new image with the calculated size
new_im = await asyncio.get_running_loop().run_in_executor(
None, lambda: Image.new("RGBA", (width, height))
)
# Paste the images and transparent segments into the grid
x_offset = y_offset = 0
for im in images:
await asyncio.get_running_loop().run_in_executor(
None, new_im.paste, im, (x_offset, y_offset)
)
x_offset += im.size[0]
if x_offset >= width:
x_offset = 0
y_offset += im.size[1]
# Fill the remaining cells with transparent segments
while y_offset < height:
while x_offset < width:
await asyncio.get_running_loop().run_in_executor(
None, new_im.paste, transparent, (x_offset, y_offset)
)
x_offset += transparent.size[0]
x_offset = 0
y_offset += transparent.size[1]
# Save the new_im to a temporary file and return it as a discord.File
temp_file = tempfile.NamedTemporaryFile(suffix=".png", delete=False)
await asyncio.get_running_loop().run_in_executor(
None, new_im.save, temp_file.name
)
# Print the filesize of new_im, in mega bytes
image_size = os.path.getsize(temp_file.name) / 1048576
if ctx.guild is None:
guild_file_limit = 8
else:
guild_file_limit = ctx.guild.filesize_limit / 1048576
# If the image size is greater than 8MB, we can't return this to the user, so we will need to downscale the
# image and try again
safety_counter = 0
while image_size > guild_file_limit:
safety_counter += 1
if safety_counter >= 3:
break
print(
f"Image size is {image_size}MB, which is too large for this server {guild_file_limit}MB. Downscaling and trying again"
)
# We want to do this resizing asynchronously, so that it doesn't block the main thread during the resize.
# We can use the asyncio.run_in_executor method to do this
new_im = await asyncio.get_running_loop().run_in_executor(
None,
functools.partial(
new_im.resize, (int(new_im.width / 1.05), int(new_im.height / 1.05))
),
)
temp_file = tempfile.NamedTemporaryFile(suffix=".png", delete=False)
await asyncio.get_running_loop().run_in_executor(
None, new_im.save, temp_file.name
)
image_size = os.path.getsize(temp_file.name) / 1000000
print(f"New image size is {image_size}MB")
return (discord.File(temp_file.name), image_urls)