Spaces:
Sleeping
Sleeping
File size: 2,999 Bytes
39bf943 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 |
import gradio as gr
import pandas as pd
# Define the LLM models and their properties
models = {
"gpt-4o-2024-08-06": {
"input_price_per_1M": 2.50,
"output_price_per_1M": 10.00,
"max_input_tokens": 128_000,
},
"gpt-4o-mini-2024-07-18": {
"input_price_per_1M": 0.15,
"output_price_per_1M": 0.600,
"max_input_tokens": 128_000,
},
"Claude 3.5 Sonnet": {
"input_price_per_1M": 3.0,
"output_price_per_1M": 15.0,
"max_input_tokens": 200_000,
},
"GPT-3.5-turbo": {
"input_price_per_1M": 0.5,
"output_price_per_1M": 1.5,
"max_input_tokens": 4096,
},
"GPT-4": {
"input_price_per_1M": 30.0,
"output_price_per_1M": 60.0,
"max_input_tokens": 8192,
},
}
def calculate_cost(model, input_tokens, output_tokens, num_requests):
if model not in models:
return "Invalid model selected", 0, 0, 0
if input_tokens > models[model]["max_input_tokens"]:
return f"Input tokens exceed the maximum limit for {model}", 0, 0, 0
input_cost = (input_tokens / 1_000_000) * models[model]["input_price_per_1M"] * num_requests
output_cost = (output_tokens / 1_000_000) * models[model]["output_price_per_1M"] * num_requests
total_cost = input_cost + output_cost
return f"${total_cost:.6f}", input_cost, output_cost, total_cost
def compare_models(input_tokens, output_tokens, num_requests):
results = []
for model in models:
total_cost_str, input_cost, output_cost, total_cost = calculate_cost(
model, input_tokens, output_tokens, num_requests
)
results.append(
{
"Model": model,
"Input Cost": f"${input_cost:.6f}",
"Output Cost": f"${output_cost:.6f}",
"Total Cost": total_cost_str,
"Max Input Tokens": models[model]["max_input_tokens"],
"Input Price (1M)": f"${models[model]['input_price_per_1M']:.2f}",
"Output Price (1M)": f"${models[model]['output_price_per_1M']:.2f}",
}
)
return pd.DataFrame(results)
def create_interface():
with gr.Blocks() as interface:
gr.Markdown("# LLM Price Comparison Tool")
with gr.Row():
input_tokens = gr.Number(label="Input Tokens", value=100)
output_tokens = gr.Number(label="Output Tokens", value=100)
num_requests = gr.Number(label="Number of Requests", value=1, step=1)
compare_btn = gr.Button("Compare Models")
output_table = gr.DataFrame(label="Comparison Results")
compare_btn.click(
fn=compare_models,
inputs=[input_tokens, output_tokens, num_requests],
outputs=output_table,
)
return interface
# Create and launch the interface
demo = create_interface()
# Hugging Face specific launch
if __name__ == "__main__":
demo.launch()
else:
demo.launch(share=True) |