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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() | |