Code Editor for app.py

import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px

# Set page config
st.set_page_config(layout="wide")

# Generate sample data
@st.cache_data
def generate_data():
    np.random.seed(42)
    dates = pd.date_range(start='2023-01-01', end='2023-12-31', freq='D')
    categories = ['A', 'B', 'C', 'D']
    
    data = {
        'date': np.repeat(dates, len(categories)),
        'category': categories * len(dates),
        'value': np.random.randn(len(dates) * len(categories)).cumsum(),
        'region': np.random.choice(['North', 'South', 'East', 'West'], 
                                 size=len(dates) * len(categories))
    }
    return pd.DataFrame(data)

# Load data
df = generate_data()

# Create layout
st.title('Interactive Dashboard')

# Sidebar filters
st.sidebar.header('Filters')

# Date range filter
date_range = st.sidebar.date_input(
    'Select Date Range',
    value=(df['date'].min(), df['date'].max()),
    min_value=df['date'].min(),
    max_value=df['date'].max()
)

# Category filter
categories = st.sidebar.multiselect(
    'Select Categories',
    options=df['category'].unique(),
    default=df['category'].unique()
)

# Region filter
regions = st.sidebar.multiselect(
    'Select Regions',
    options=df['region'].unique(),
    default=df['region'].unique()
)

# Filter data based on selections
mask = (
    (df['date'].dt.date >= date_range[0]) & 
    (df['date'].dt.date <= date_range[1]) &
    (df['category'].isin(categories)) &
    (df['region'].isin(regions))
)
filtered_df = df[mask]

# Create chart
fig = px.line(
    filtered_df, 
    x='date', 
    y='value', 
    color='category',
    title='Time Series by Category',
    labels={'value': 'Value', 'date': 'Date'},
    height=600
)

# Update layout
fig.update_layout(
    plot_bgcolor='white',
    legend=dict(
        yanchor="top",
        y=0.99,
        xanchor="left",
        x=0.01
    )
)

# Display chart
st.plotly_chart(fig, use_container_width=True)

# Show filtered data
st.subheader('Filtered Data')
st.dataframe(filtered_df)