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Update app.py
Browse files- add page icon
- remove submit button and automatically calculate cost once the the prompt is entered in the text area
app.py
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@@ -2,6 +2,8 @@ import streamlit as st
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import pandas as pd
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from transformers import GPT2Tokenizer
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# Load the tokenizer
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tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
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@@ -36,20 +38,23 @@ def calculate_cost(model, input_tokens, output_tokens):
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# Streamlit App
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st.title("GPT/LLM Usage Cost Estimator")
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# User input
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user_input = st.text_area("
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estimated_output_tokens = st.number_input("Estimated number of output tokens", min_value=0, value=
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selected_model = st.selectbox("Select the model", list(rate_prices.keys()))
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if st.button("Calculate Cost"):
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input_tokens = count_tokens(user_input)
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total_cost = calculate_cost(selected_model, input_tokens, estimated_output_tokens)
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# Create a DataFrame for displaying results
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results_df = pd.DataFrame({
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"Detail": ["Number of Input Tokens", "Estimated Number of Output Tokens", "Estimated Total Cost"],
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"Value": [input_tokens, estimated_output_tokens, f"${total_cost:.
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})
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# Display the results in a table
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@@ -61,5 +66,5 @@ st.markdown("""
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---
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<sup>**Note:** The pricing information is based on [OpenAI's pricing page](https://openai.com/pricing) as of 12/14/2023.</sup>
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<br>
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<sub>**Disclaimer:** This application
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""", unsafe_allow_html=True)
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import pandas as pd
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from transformers import GPT2Tokenizer
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st.set_page_config(page_title="LLM Cost Estimator", page_icon=":moneybag:")
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# Load the tokenizer
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tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
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# Streamlit App
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st.title("GPT/LLM Usage Cost Estimator")
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st.markdown("> _A simple tool to estimate the cost of using OpenAI models based on the number of input and output tokens._")
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# User input
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user_input = st.text_area("", placeholder="Paste your prompt here...")
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estimated_output_tokens = st.number_input("Estimated number of output tokens", min_value=0, value=100)
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selected_model = st.selectbox("Select the model", list(rate_prices.keys()))
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# if st.button("Calculate Cost"):
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if user_input:
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input_tokens = count_tokens(user_input)
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total_cost = calculate_cost(selected_model, input_tokens, estimated_output_tokens)
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st.markdown(f"### Estimated Cost: `${total_cost:.2f}`")
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# Create a DataFrame for displaying results
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results_df = pd.DataFrame({
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"Detail": ["Number of Input Tokens", "Estimated Number of Output Tokens", "Estimated Total Cost"],
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"Value": [input_tokens, estimated_output_tokens, f"${total_cost:.4f}"]
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})
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# Display the results in a table
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---
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<sup>**Note:** The pricing information is based on [OpenAI's pricing page](https://openai.com/pricing) as of 12/14/2023.</sup>
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<br>
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<sub>**Disclaimer:** This application was completely written by GPT-4 from a chat conversation.</sub>
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""", unsafe_allow_html=True)
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