Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+from transformers import GPT2Tokenizer
+
+# 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 Usage Cost Estimator")
+
+# User input
+user_input = st.text_area("Enter your prompt")
+estimated_output_tokens = st.number_input("Estimated number of output tokens", min_value=0, value=50)
+selected_model = st.selectbox("Select the GPT model", list(rate_prices.keys()))
+
+if st.button("Calculate Cost"):
+    input_tokens = count_tokens(user_input)
+    total_cost = calculate_cost(selected_model, input_tokens, estimated_output_tokens)
+
+    # 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:.2f}"]
+    })
+
+    # 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>
+""", unsafe_allow_html=True)