| 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"]) |
|
|
| |
| 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() |
