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import gradio as gr
import tiktoken
from decimal import getcontext
getcontext().prec = 2
def num_tokens_from_messages(messages, model):
"""Return the number of tokens used by a list of messages."""
encoding = tiktoken.encoding_for_model(model)
if model in {
"gpt-3.5-turbo-1106",
"gpt-4-1106-preview",
"gpt-4",
}:
tokens_per_message = 3
tokens_per_name = 1
else:
raise NotImplementedError(
f"""num_tokens_from_messages() is not implemented for model {model}. See https://github.com/openai/openai-python/blob/main/chatml.md for information on how messages are converted to tokens."""
)
num_tokens = 0
for message in messages:
num_tokens += tokens_per_message
for key, value in message.items():
num_tokens += len(encoding.encode(value))
if key == "name":
num_tokens += tokens_per_name
num_tokens += 3 # every reply is primed with <|start|>assistant<|message|>
return num_tokens
# https://openai.com/pricing
def calc_input_cost(tokens, model):
"""Return the input cost of tokens"""
cost = 0
if model == "gpt-3.5-turbo-1106":
# input $0.001 / 1K tokens, output $0.002 / 1K tokens
cost = 0.001 * tokens / 1000
elif model == "gpt-4-1106-preview":
# input $0.01 / 1K tokens, output $0.03 / 1K tokens
cost = 0.01 * tokens / 1000
elif model == "gpt-4":
# input $0.03 / 1K tokens, output $0.06 / 1K tokens
cost = 0.03 * tokens / 1000
else:
raise NotImplementedError(
f"""calc_input_cost() is not implemented for model {model}."""
)
return float(cost)
# https://openai.com/pricing
def calc_output_cost(tokens, model="gpt-3.5-turbo-1106"):
"""Return the output cost of tokens"""
cost = 0
if model == "gpt-3.5-turbo-1106":
# input $0.001 / 1K tokens, output $0.002 / 1K tokens
cost = 0.002 * tokens / 1000
elif model == "gpt-4-1106-preview":
# input $0.03 / 1K tokens, output $0.03 / 1K tokens
cost = 0.03 * tokens / 1000
elif model == "gpt-4":
# input $0.03 / 1K tokens, output $0.06 / 1K tokens
cost = 0.06 * tokens / 1000
else:
raise NotImplementedError(
f"""calc_input_cost() is not implemented for model {model}."""
)
return float(cost)
def make_input_messages(system, user):
return [
{
"role": "system",
"content": system,
},
{
"role": "user",
"content": user,
}
]
def make_output_messages(output):
return [
{
"role": "assistant",
"content": output,
}
]
def calc(request_count, system_prompt, user_prompt, output):
result = ""
input_messages = make_input_messages(system_prompt, user_prompt)
output_messages = make_output_messages(output)
for model in [
"gpt-3.5-turbo-1106",
"gpt-4-1106-preview",
"gpt-4"
]:
# example token count from the function defined above
input_token = num_tokens_from_messages(input_messages, model)
input_cost = calc_input_cost(input_token, model)
output_token = num_tokens_from_messages(output_messages, model)
output_cost = calc_output_cost(output_token, model)
total_cost = input_cost + output_cost
result += f"""{model}
input token:{input_token}, output token: {output_token} tokens counted by tiktoken.
input cost: ${format(input_cost, 'f')}, output cost: ${format(output_cost, 'f')}, total cost: ${format(total_cost, 'f')} per 1 request
{int(request_count)} request cost is ${format(total_cost * request_count, 'f')}\n"""
return result
iface = gr.Interface(
fn=calc,
inputs=[
gr.Number(label="Request Count", value=500),
gr.Textbox(label="System Prompt", value="System Prompt"),
gr.Textbox(label="User Prompt", value="User Prompt"),
gr.Textbox(label="Output", value="Output Text"),
],
outputs=[
gr.Textbox(label="Result")
],
title="ChatGPT Token and Cost Calculator",
description=f"Enter each form value, then click the button to see the results.\nThe results of this calculation should be considered for reference only."
)
iface.launch()
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