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"""App agnostic reusable utility functionality"""
from config import app_config
import data
from typing import List
from PIL import Image
import streamlit as st
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_file,
initial_sidebar_state=config.sidebar_state,
layout=config.layout,
)
### Logo and App title, description
with st.container():
app_icon, app_title, logo = st.columns([0.2, 0.9, 0.3])
app_icon.image(image=app_config.app_icon_file, width=80)
app_title.markdown(
f"<h1 style='text-align: left; color: #03989e;'>{app_config.app_title}</h1> ",
unsafe_allow_html=True,
)
app_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_file, width=100)
def create_tabs(tabs: List[str]):
"""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()
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