steamlit_lab_003.py

import streamlit as st
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import plotly.graph_objs as go
from sklearn.ensemble import RandomForestClassifier
from sklearn.tree import DecisionTreeClassifier

# import data
@st.cache_data
def read_data():
  url = 'https://docs.google.com/spreadsheets/d/1_kzF44rlmD9sut5R5yZp3IIK4j4PlVvTgVt-nw9gAEE/edit?usp=sharing'
  df = pd.read_html(url, header=0)[0]
  new_header = df.iloc[0] #grab the first row for the header
  df = df[1:] #take the data less the header row
  df.columns = new_header #set the header row as the df header
  # df = df.drop(['Age_Range','Salary_Range'], axis=1)
  return df

def filter_dataframe(df):
    # Filter DataFrame to include only columns with no None values
    df_filtered = df.dropna(axis=1, how='all')
    return df_filtered

st.set_page_config(
    page_title= "VAMStack Course",
    layout="wide",
    #page_icon=None
)


st.write("# Welcome to Employee Management Record")
st.write("#### Name: Chhun , student ID: XXXXXXXX")
st.write("### Data Visualisation")


data = read_data()
new_data = filter_dataframe(data)

col07, col08, col09 = st.columns([1,6,1])
with col07:
	st.write("")
with col08:
	st.write(new_data, use_container_width=True)
with col09:
  st.write("")

col10, col11, col12 = st.columns([1,6,1])
with col10:
	st.write("")
with col11:
  columns = data.columns.tolist()
  s = data['Gender'].str.strip().value_counts()
  counts = go.Bar(x=s.index,y=s.values,showlegend = True)
  layout = go.Layout(title = "Summary graph")
  datas = [counts]
  fig = go.Figure(data=datas,layout=layout)
  st.plotly_chart(fig)
with col12:
  st.write("")


# Adding a function for selecting the model
col07, col08, col09 = st.columns([1, 6, 1])
with col07:
    st.write("")
with col08:
    st.subheader("Select Model")
    model_option = st.selectbox(
        "Choose a model for analysis",
        ("Random Forest", "Decision Tree")
    )
    if model_option == "Random Forest":
        model = RandomForestClassifier()
    elif model_option == "Decision Tree":
        model = DecisionTreeClassifier()
    st.write(f"Selected Model: {model_option}")
with col09:
    st.write("")