Code Editor for app.py

import streamlit as st
import pandas as pd
import plotly.express as px
from PIL import Image
import base64
from pathlib import Path

# Initialize session state variables
if 'authenticated' not in st.session_state:
    st.session_state.authenticated = False
if 'username' not in st.session_state:
    st.session_state.username = ''

# Function to generate sample data
def generate_sample_data():
    data = {
        'Category': ['A', 'B', 'C', 'D', 'E'] * 20,
        'Value': [10, 20, 30, 40, 50] * 20,
        'Region': ['North', 'South', 'East', 'West', 'Central'] * 20,
        'Sales': [100, 200, 300, 400, 500] * 20
    }
    return pd.DataFrame(data)

# Create a simple logo
def create_logo():
    # Create a simple colored rectangle as logo
    img = Image.new('RGB', (200, 100), color='blue')
    return img

# Save the logo
logo = create_logo()
logo.save('logo.png')

# Login page
def login_page():
    col1, col2, col3 = st.columns([1,2,1])
    
    with col2:
        st.image('logo.png', width=200)
        st.title('Company Dashboard')
        
        username = st.text_input('Username')
        password = st.text_input('Password', type='password')
        
        if st.button('Login'):
            # Simple authentication (in real app, use proper authentication)
            if username == 'admin' and password == 'admin':
                st.session_state.authenticated = True
                st.session_state.username = username
                st.rerun()
            else:
                st.error('Invalid credentials')

# Main dashboard
def main_dashboard():
    # Sidebar
    with st.sidebar:
        st.image('logo.png', width=150)
        st.title('Dashboard')
        
        # Menu buttons
        selected = st.radio('Menu', ['Sales Analysis', 'Performance Metrics', 'Reports'])
    
    # Main content
    if selected == 'Sales Analysis':
        show_sales_analysis()
    elif selected == 'Performance Metrics':
        show_performance_metrics()
    else:
        show_reports()

def show_sales_analysis():
    st.title('Sales Analysis')
    
    # Filters
    col1, col2 = st.columns(2)
    with col1:
        category_filter = st.multiselect('Category', ['A', 'B', 'C', 'D', 'E'])
    with col2:
        region_filter = st.multiselect('Region', ['North', 'South', 'East', 'West', 'Central'])
    
    # Get data
    df = generate_sample_data()
    
    # Apply filters
    if category_filter:
        df = df[df['Category'].isin(category_filter)]
    if region_filter:
        df = df[df['Region'].isin(region_filter)]
    
    # Create pie chart
    fig = px.pie(df, values='Value', names='Category', title='Sales by Category')
    st.plotly_chart(fig)
    
    # Show summary dataframe
    st.subheader('Summary Data')
    summary_df = df.groupby(['Category', 'Region'])['Value'].sum().reset_index()
    st.dataframe(summary_df)
    
    # Show complete data
    st.subheader('Complete Data')
    st.dataframe(df)

def show_performance_metrics():
    st.title('Performance Metrics')
    
    # Filters
    region_filter = st.multiselect('Region', ['North', 'South', 'East', 'West', 'Central'])
    
    # Get data
    df = generate_sample_data()
    
    # Apply filters
    if region_filter:
        df = df[df['Region'].isin(region_filter)]
    
    # Create pie chart
    fig = px.pie(df, values='Sales', names='Region', title='Sales by Region')
    st.plotly_chart(fig)
    
    # Show summary and complete data
    st.subheader('Summary Data')
    summary_df = df.groupby('Region')['Sales'].sum().reset_index()
    st.dataframe(summary_df)
    
    st.subheader('Complete Data')
    st.dataframe(df)

def show_reports():
    st.title('Reports')
    
    # Filters
    time_period = st.selectbox('Time Period', ['Daily', 'Weekly', 'Monthly', 'Yearly'])
    
    # Get data
    df = generate_sample_data()
    
    # Create pie chart
    fig = px.pie(df, values='Value', names='Category', title='Category Distribution')
    st.plotly_chart(fig)
    
    # Show summary and complete data
    st.subheader('Summary Data')
    summary_df = df.groupby('Category')['Value'].mean().reset_index()
    st.dataframe(summary_df)
    
    st.subheader('Complete Data')
    st.dataframe(df)

# Main app logic
def main():
    if not st.session_state.authenticated:
        login_page()
    else:
        main_dashboard()

if __name__ == '__main__':
    main()