pages/2_🏋️‍♀️_Model_training.py

import streamlit as st
import pandas as pd
from persist import persist, load_widget_state
import numpy as np
import matplotlib.pyplot as plt
global variable_output

def main():
  
    cs_body()

def convert_csv():
    d = {'col1': [], 'col2': []}
    df = pd.DataFrame(data=d, columns=['Age', 'Sex'])
    return df.to_csv().encode("utf-8")

def cs_body():
    
    st.header('Training Data and Methodology')
    st.write("Provide an overview of the Training Data and Training Procedure for this model")
    st.markdown('##### Training dataset')
    left, right = st.columns(2)
    left.number_input("Training set size",value=100)
    right.number_input("Validation set size",value=20)
    st.text("Demographical and clinical characteristics")
    left, right = st.columns(2)#, vertical_alignment ="center")
    left.download_button("Download Template", data=convert_csv(), file_name='file.csv')
    demo = right.file_uploader("Load template",type=['csv'])
    if demo is not None:
        left, right = st.columns(2)#, vertical_alignment ="center")
        
        fig, ax = plt.subplots()
        ax.set_title("Age distribution")
        ax.hist(np.random.normal(loc=40,scale=4.0,size=500))
        age = left.pyplot(fig)

        

        fig, ax = plt.subplots()
        ax.pie([45,55],labels=["Men","Women"])
        right.pyplot(fig)
    st.text_input("Source",placeholder="Brats challenge/ Clinic ...")
    st.text("Acquisition date")
    left, right = st.columns(2)
    left.date_input("From")
    right.date_input("To")
   
   
    

if __name__ == '__main__':
    load_widget_state()
    main()