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