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| """App agnostic reusable utility functionality""" | |
| from config import app_config | |
| import streamlit as st | |
| import s3fs | |
| def setup_app(config): | |
| """Sets up all application icon, banner, title""" | |
| st.set_page_config( | |
| page_title=config.app_title, | |
| page_icon=app_config.app_icon, | |
| initial_sidebar_state=config.sidebar_state, | |
| layout=config.layout, | |
| ) | |
| ### Logo and App title, description | |
| with st.container(): | |
| app_icon_title, title, logo = st.columns([0.4, 0.6, 0.3]) | |
| app_icon_title.image(image=app_config.app_icon, width=200) | |
| title.markdown( | |
| f"<h1 style='text-align: left; color: #03989e;'>{app_config.app_title}</h1> ", | |
| unsafe_allow_html=True, | |
| ) | |
| title.markdown( | |
| f"<p style='text-align: left;'>{app_config.app_short_desc}</p>", | |
| unsafe_allow_html=True, | |
| ) | |
| logo.image(image=app_config.logo_image) | |
| # st.divider() | |
| def create_tabs(tabs): | |
| """Creates streamlit tabs""" | |
| return st.tabs(tabs) | |
| def download_file(btn_label, data, file_name, mime_type): | |
| """Creates a download button for data download""" | |
| st.download_button(label=btn_label, data=data, file_name=file_name, mime=mime_type) | |
| def get_class_from_name(module: str, class_name: str): | |
| """Instantiates and return the class given the class name and its module as str""" | |
| return getattr(module, class_name) | |
| # def make_prediction(model, input_data, proba=False): | |
| # """ | |
| # prediction pipeline for the model, model must have predict method and predict_proba | |
| # method if prediction probabilities to be returned | |
| # """ | |
| # ### preprocess the input and return it in a shape suitable for this model | |
| # processed_input_data = data.preprocess_pred_data(input_data) | |
| # ### call model's predict method | |
| # pred = model.predict(processed_input_data) | |
| # ### call model's predict_proba method if required | |
| # pred_proba = [] | |
| # if proba: | |
| # pred_proba = model.predict_proba(processed_input_data) | |
| # return pred, pred_proba.squeeze() | |
| def download_from_s3(source_s3_uri, target_file): | |
| """connect to S3 and download file""" | |
| with st.spinner( | |
| f"Downloading trained model it may take few minutes, please be patient..." | |
| ): | |
| fs = s3fs.S3FileSystem( | |
| key=st.secrets["AWS_ACCESS_KEY"], secret=st.secrets["AWS_ACCESS_SECRET"] | |
| ) | |
| fs.download(source_s3_uri, target_file) | |
| def read(file) -> str: | |
| """read the text file and return the contents""" | |
| with open(file, "r") as f: | |
| text = f.read() | |
| return text | |