Code Editor for app.py

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

# Configure page to use wide mode and add custom CSS
st.set_page_config(layout="wide")

# Custom CSS for layout adjustments
st.markdown("""
    <style>
    .stSidebar {
        background-color: #f5f5f5;
        padding: 1rem;
        border-right: 1px solid #ddd;
    }
    .stButton button {
        width: 100%;
        margin-bottom: 0.5rem;
    }
    </style>
""", unsafe_allow_html=True)

# Initialize session state for active case
if 'active_case' not in st.session_state:
    st.session_state.active_case = None

# Left Panel - Case & Data Navigation
with st.sidebar:
    st.header("Case Navigation")
    
    # Case selection section
    st.subheader("Active Cases")
    cases = ["Case A-123", "Case B-456", "Case C-789"]
    selected_case = st.selectbox("Select Case", cases)
    
    if st.button("New Case"):
        st.session_state.active_case = "New Case"
    
    # Data source selection
    st.subheader("Data Sources")
    data_sources = ["Database A", "CSV Files", "API Feed"]
    selected_source = st.selectbox("Select Data Source", data_sources)
    
    # Filters section
    st.subheader("Filters")
    date_range = st.date_input("Date Range", [])
    category = st.multiselect("Categories", ["Category A", "Category B", "Category C"])
    
    # Quick actions
    st.subheader("Quick Actions")
    if st.button("Export Data"):
        st.info("Exporting data...")
    if st.button("Generate Report"):
        st.info("Generating report...")

# Main content area - using columns for layout
col1, col2 = st.columns([2, 1])

# Center Panel - Main Analysis Area
with col1:
    st.header("Analysis Dashboard")
    
    # Generate sample data for demonstration
    np.random.seed(42)
    dates = pd.date_range(start='2023-01-01', periods=100)
    data = pd.DataFrame({
        'Date': dates,
        'Value': np.random.randn(100).cumsum(),
        'Category': np.random.choice(['A', 'B', 'C'], 100)
    })
    
    # Create interactive plot
    fig = px.line(data, x='Date', y='Value', color='Category',
                 title='Trend Analysis')
    st.plotly_chart(fig, use_container_width=True)
    
    # Data table
    st.subheader("Data Overview")
    st.dataframe(data.tail(), use_container_width=True)

# Right Panel - Details & Metrics
with col2:
    st.header("Details & Metrics")
    
    # Summary metrics
    st.subheader("Key Metrics")
    metrics_col1, metrics_col2 = st.columns(2)
    with metrics_col1:
        st.metric(label="Total Cases", value="1,234")
        st.metric(label="Active Cases", value="421")
    with metrics_col2:
        st.metric(label="Success Rate", value="87%")
        st.metric(label="Processing Time", value="2.3 days")
    
    # Status and notifications
    st.subheader("Status Updates")
    st.info("Last update: 5 minutes ago")
    st.success("All systems operational")
    
    # Quick stats
    st.subheader("Quick Statistics")
    stats_data = pd.DataFrame({
        'Metric': ['Daily Average', 'Weekly Trend', 'Monthly Growth'],
        'Value': ['145.3', '+12.5%', '+8.2%']
    })
    st.table(stats_data)