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import gradio as gr
import requests
URL = "https://openrouter.ai/api/v1/models"
response = requests.get(URL)
print(response.json())
all_models = [a["name"] for a in response.json()["data"]]
name_to_dict = {a["name"]: a for a in response.json()["data"]}
def get_cost(model_name, input_token, output_token):
model_dict = name_to_dict[model_name]
prompt_cost = float(model_dict["pricing"]["prompt"])
completion_cost = float(model_dict["pricing"]["completion"])
context_length = float(model_dict["context_length"])
# print(prompt_cost * input_token, completion_cost * output_token, context_length)
return prompt_cost * min(context_length, input_token) + completion_cost * output_token
get_cost('Google: Gemini Pro 1.0', 1000, 1000)
demo = gr.Interface(
fn=get_cost,
inputs=[
gr.Dropdown(choices=all_models),
gr.Number(label="Input tokens"),
gr.Number(label="Output tokens"),
],
outputs="text",
title="LLM Cost Calculator",
description="Calculate the cost of a prompt",
)
demo.launch() |