Spaces:
Sleeping
Sleeping
File size: 2,810 Bytes
8b4f999 6bac693 8b4f999 88dbc13 6bac693 8b4f999 6bac693 88dbc13 8b4f999 6bac693 8b4f999 6bac693 8b4f999 6bac693 8b4f999 4eb3d76 6bac693 8b4f999 |
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 |
import streamlit as st
import pandas as pd
from transformers import GPT2Tokenizer
st.set_page_config(page_title="LLM Cost Estimator", page_icon=":moneybag:")
# Load the tokenizer
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
# Updated rate prices with the accurate rates for each model
rate_prices = {
"gpt-4": {"input": 0.03, "output": 0.06},
"gpt-4-32k": {"input": 0.06, "output": 0.12},
"gpt-4-1106-preview": {"input": 0.01, "output": 0.03},
"gpt-4-1106-vision-preview": {"input": 0.01, "output": 0.03},
"gpt-3.5-turbo-1106": {"input": 0.0010, "output": 0.0020},
"gpt-3.5-turbo-instruct": {"input": 0.0015, "output": 0.0020},
"gpt-3.5-turbo": {"input": 0.008, "output": 0.003, "additional_output": 0.006},
"davinci-002": {"input": 0.006, "output": 0.012, "additional_output": 0.012},
"babbage-002": {"input": 0.0004, "output": 0.0016, "additional_output": 0.0016},
}
def count_tokens(text):
return len(tokenizer.encode(text))
def calculate_cost(model, input_tokens, output_tokens):
input_rate = rate_prices[model]["input"]
output_rate = rate_prices[model]["output"]
additional_output_rate = rate_prices[model].get("additional_output", output_rate)
input_cost = (input_tokens / 1000) * input_rate
output_cost = (output_tokens / 1000) * output_rate
additional_output_cost = (output_tokens / 1000) * additional_output_rate
return input_cost + output_cost + additional_output_cost
# Streamlit App
st.title("GPT/LLM Usage Cost Estimator")
st.markdown("> _A simple tool to estimate the cost of using OpenAI models based on the number of input and output tokens._")
# User input
user_input = st.text_area("", placeholder="Paste your prompt here...")
estimated_output_tokens = st.number_input("Estimated number of output tokens", min_value=0, value=100)
selected_model = st.selectbox("Select the model", list(rate_prices.keys()))
# if st.button("Calculate Cost"):
if user_input:
input_tokens = count_tokens(user_input)
total_cost = calculate_cost(selected_model, input_tokens, estimated_output_tokens)
st.markdown(f"### Estimated Cost: `${total_cost:.2f}`")
# Create a DataFrame for displaying results
results_df = pd.DataFrame({
"Detail": ["Number of Input Tokens", "Estimated Number of Output Tokens", "Estimated Total Cost"],
"Value": [input_tokens, estimated_output_tokens, f"${total_cost:.4f}"]
})
# Display the results in a table
st.table(results_df)
# Note about the pricing source
st.markdown("""
---
<sup>**Note:** The pricing information is based on [OpenAI's pricing page](https://openai.com/pricing) as of 12/14/2023.</sup>
<br>
<sub>**Disclaimer:** This application was completely written by GPT-4 from a chat conversation.</sub>
""", unsafe_allow_html=True) |