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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 | |
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" | |
sys_mes = "prompts/gpt4-system-message.txt" | |
# 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=5) | |
# display response | |
st.write(response['choices'][0]['message']['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) | |
def convert_df(df): | |
# 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', | |
) | |