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Initial Commit and updated Requirements
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Barry Weiss committed Jun 8, 2023
1 parent 49b2139 commit 2e38da1
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2 changes: 2 additions & 0 deletions .dockerignore
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# ignore to pull into container
.env-template
2 changes: 2 additions & 0 deletions .env-template
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# Add your API Key below
API_KEY_OPENAI = "YOUR KEY IS HERE"
164 changes: 164 additions & 0 deletions .gitignore
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# Ignore .env fiile
.env

# Ignore others
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class

# C extensions
*.so

# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
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.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST

# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec

# Installer logs
pip-log.txt
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# Unit test / coverage reports
htmlcov/
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nosetests.xml
coverage.xml
*.cover
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.hypothesis/
.pytest_cache/
cover/

# Translations
*.mo
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*.log
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# Flask stuff:
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docs/_build/

# PyBuilder
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target/

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profile_default/
ipython_config.py

# pyenv
# For a library or package, you might want to ignore these files since the code is
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# .python-version

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# However, in case of collaboration, if having platform-specific dependencies or dependencies
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# install all needed dependencies.
#Pipfile.lock

# poetry
# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
# This is especially recommended for binary packages to ensure reproducibility, and is more
# commonly ignored for libraries.
# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
#poetry.lock

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#pdm.lock
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.pdm.toml

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__pypackages__/

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celerybeat.pid

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# Environments
.env
.venv
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venv/
ENV/
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venv.bak/

# Spyder project settings
.spyderproject
.spyproject

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.ropeproject

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/site

# mypy
.mypy_cache/
.dmypy.json
dmypy.json

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.pyre/

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.pytype/

# Cython debug symbols
cython_debug/

# PyCharm
# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
# and can be added to the global gitignore or merged into this file. For a more nuclear
# option (not recommended) you can uncomment the following to ignore the entire idea folder.
#.idea/
11 changes: 11 additions & 0 deletions dockerfile
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FROM python:3.10-bullseye

WORKDIR /app

COPY . /app

RUN pip install -r requirements.txt

EXPOSE 8501

CMD ["streamlit", "run", "main.py"]
88 changes: 88 additions & 0 deletions main.py
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#! /usr/bin/env Python3
"""
Tolken Calculation for OpenAI Models
Based on notes found in:
https://github.com/openai/openai-cookbook/blob/297c53430cad2d05ba763ab9dca64309cb5091e9/examples/How_to_count_tokens_with_tiktoken.ipynb
"""

from os import getenv
from dotenv import load_dotenv
import streamlit as st
import tiktoken
import openai

load_dotenv('./.env')

openai.api_key = getenv('API_KEY_OPENAI')

if openai.api_key:
print("API KEY LOADED")
else:
st.error('No API Key found')

st.title('ChatGPT TokenCalculator')

def load():
"""_summary_
Returns:
_type_: _description_
"""
upload_file = st.file_uploader('Please upload a .txt or .pdf File', ['.txt', '.pdf'])
return upload_file

def num_tokens_from_string(string: str, encoding_name: str) -> int:
"""Returns the number of tokens in a text string."""
encoding = tiktoken.get_encoding(encoding_name)
num_tokens = len(encoding.encode(string))
return num_tokens

def get_model_encoding(model):
"""_summary_
Args:
model (_type_): _description_
Returns:
_type_: _description_
"""
encoding = tiktoken.encoding_for_model(model)
return encoding

def get_models():
"""_summary_
Yields:
_type_: _description_
"""
models = openai.Engine.list()
for model in models['data']:
yield model['id']

file = load()
if file is not None:
st.write("File Loaded")
file_output = file.read().decode()

tab1, tab2, tab3 = st.tabs(["OpenAI Model and Encoding", "Number of Tokens", "Encodings"])

with tab1:
st.subheader("OpenAI Model and Encoding")
model = st.selectbox('Choose OpenAI Model', list(get_models()))
encoding = get_model_encoding(model)
st.write(f"The {model} model uses {encoding.name} for encoding.")

with tab2:
st.subheader("Word Block")
word_block = list(file_output.split())
st.write(f"Word count: {len(word_block)}")
num_tokens_from_string = num_tokens_from_string(file_output, encoding.name)
st.write(f"There are {num_tokens_from_string} token(s) in the file.")
st.write(f"File Output:\n\n{file_output}")

with tab3:
encoded_list = encoding.encode(file_output)
st.subheader(f"Text Turned into Encoded Tokens - {num_tokens_from_string} token(s)")
st.table(encoded_list)
5 changes: 5 additions & 0 deletions requirements.txt
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# Required Modules
openai==0.27.8
streamlit==1.23.1
tiktoken==0.4.0
python-dotenv==1.0.0

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