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""" this app is streamlit app for the current project hosted on HuggingFace spaces """
import streamlit as st
from openai_chat_completion import OpenAIChatCompletions
from dataclean_hf import main
from util import json_to_dict #, join_dicts
st.title("Kaleidoscope Data - Data Cleaning LLM App")
st.write("This app is a demo of the LLM model for data cleaning. It is a work in progress and is not yet ready for production use.")
# text box or csv upload
text_input = st.text_input("Enter text", "")
# csv_file = st.file_uploader("Upload CSV", type=['csv'])
# button to run data cleaning API on text via c class in openai_chat_completion.py
if st.button("Run Data Cleaning API"):
# if text_input is not empty, run data cleaning API on text_input
if text_input:
MODEL = "gpt-4" # "gpt-3.5-turbo"
try:
with open('prompts/gpt4-system-message3.txt', 'r', encoding='utf8') as f:
sys_mes = f.read()
f.close()
except FileNotFoundError:
with open('../prompts/gpt4-system-message3.txt', 'r', encoding='utf8') as f:
sys_mes = f.read()
f.close()
# instantiate OpenAIChatCompletions class
# get response from openai_chat_completion method
chat = OpenAIChatCompletions(model=MODEL, system_message=sys_mes)
response = chat.openai_chat_completion(text_input, n_shot=None)
# display response
# st.write(response['choices'][0]['message']['content'])
response_content = response['choices'][0]['message']['content']
st.write(json_to_dict(response_content))
# if csv_file is not empty, run data cleaning API on csv_file
# elif csv_file:
# # run data cleaning API on csv_file
# output_df = main(csv_file)
# @st.cache_data
# def convert_df(df):
# """coverting dataframe to csv
# Args:
# df (_type_): pd.DataFrame
# Returns:
# _type_: csv
# """
# # IMPORTANT: Cache the conversion to prevent computation on every rerun
# return df.to_csv().encode('utf-8')
# csv = convert_df(output_df)
# st.download_button(
# label="Download data as CSV",
# data=csv,
# file_name='cleaned_df.csv',
# mime='text/csv',
# )
# if both text_input and csv_file are empty, display error message
else:
st.write("Please enter text or upload a CSV file.") |