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from typing import Tuple
import streamlit as st
import tiktoken
def num_tokens_from_messages(messages: list, model: str="gpt-3.5-turbo-0301") -> Tuple[int, int]:
"""Returns the number of tokens used by a list of messages."""
try:
encoding = tiktoken.encoding_for_model(model)
except KeyError:
st.sidebar.warning("Warning: model not found. Using cl100k_base encoding.")
encoding = tiktoken.get_encoding("cl100k_base")
if model == "gpt-3.5-turbo":
st.sidebar.warning("Warning: gpt-3.5-turbo may change over time. Returning num tokens assuming gpt-3.5-turbo-0301.")
return num_tokens_from_messages(messages, model="gpt-3.5-turbo-0301")
elif model == "gpt-4":
st.sidebar.warning("Warning: gpt-4 may change over time. Returning num tokens assuming gpt-4-0314.")
return num_tokens_from_messages(messages, model="gpt-4-0314")
elif model == "gpt-4-32k":
st.sidebar.warning("Warning: gpt-4 may change over time. Returning num tokens assuming gpt-4-0314.")
return num_tokens_from_messages(messages, model="gpt-4-0314")
elif model == "gpt-3.5-turbo-0301":
tokens_per_message = 5 # every message follows <|start|>{role/name}\n{content}<|end|>\n
elif model == "gpt-4-0314":
tokens_per_message = 4
else:
raise NotImplementedError(f"""num_tokens_from_messages() is not implemented for model {model}. See https://github.com/openai/openai-python/blob/main/chatml.md for information on how messages are converted to tokens.""")
prompt_tokens = completion_tokens = 0
for message in messages:
if message['role'] in {'system', 'user'}:
prompt_tokens += len(encoding.encode(message['content'])) + tokens_per_message
else:
completion_tokens += len(encoding.encode(message['content']))
prompt_tokens += 3 # every reply is primed with <|start|>assistant<|message|>
return prompt_tokens, completion_tokens
def get_price(model: str) -> Tuple[float, float]:
"""$ / 1K tokens"""
pricing = {
'gpt-4': (0.03, 0.06),
'gpt-4-32k': (0.06, 0.12),
'gpt-3.5-turbo': (0.002, 0.002),
}
return pricing.get(model)
st.session_state["model"] = st.sidebar.selectbox(
"Please select a model",
["gpt-3.5-turbo", "gpt-4", "gpt-4-32k"],
help="ID of the model to use",
)
if 'messages' not in st.session_state:
st.session_state['messages'] = [{"role": "system", "content": 'You are a helpful assistant'}]
st.title('ChatGPT Token Calculator')
metric_zone = st.empty()
input_zone = st.empty()
add_button = st.button('Add message', use_container_width=True, on_click = lambda: st.session_state['messages'].append({"role": "user", "content": ''}))
def change_role(index: int):
st.session_state['messages'][index]['role'] = st.session_state['role' + str(index)]
st.session_state['messages'][index]['content'] = st.session_state['content' + str(index)]
with input_zone.container():
for index, message in enumerate(st.session_state['messages']):
cols = st.columns([2, 5, 1])
selections = ['user', 'system', 'assistant']
role_index = selections.index(message['role'])
# cols[0].selectbox('role', selections, key='role' + str(index), index=role_index, label_visibility='collapsed', on_change=lambda: st.session_state['messages'][index].__setitem__('role', st.session_state['role' + str(index)]))
cols[0].selectbox('role', selections, key='role' + str(index), index=role_index, label_visibility='collapsed', on_change=change_role, args=(index, ))
# cols[1].text_input('content', value=message.get('content'), key='content' + str(index), placeholder='Content',label_visibility='collapsed',on_change=lambda: st.session_state['messages'][index].__setitem__('content',st.session_state['content' + str(index)]))
cols[1].text_input('content', value=message.get('content'), key='content' + str(index), placeholder='Content',label_visibility='collapsed', on_change=change_role, args=(index, ))
cols[2].button('❌', key='remove'+str(index), on_click=lambda: st.session_state['messages'].pop(index))
with metric_zone.container():
prompt_tokens, completion_tokens = num_tokens_from_messages(st.session_state['messages'], st.session_state['model'])
prompt_price, completion_price = get_price(st.session_state['model'])
col1, col2, col3, col4 = st.columns([1, 1, 1, 1])
col1.metric(label='Total Tokens', value=prompt_tokens+completion_tokens)
col2.metric(label='Prompt Tokens', value=prompt_tokens)
col3.metric(label='Completion Tokens', value=completion_tokens)
col4.metric(label='Price', value=f'${(prompt_price*prompt_tokens+completion_price*completion_tokens)/1000:f}') |