with open('app.py', 'w') as f:
    f.write('# Sales Dashboard App\n')
    f.write('# Generated step by step throughout this lesson\n\n')
print('app.py created')

page_setup = '''import streamlit as st
import pandas as pd
import numpy as np

st.set_page_config(page_title='Sales Dashboard', layout='wide')

st.title('Sales Dashboard')
st.write('An interactive view of synthetic regional sales data.')
'''

with open('app.py', 'a') as f:
    f.write(page_setup)
print('Page setup written')

widgets_code = '''
st.sidebar.header('Filters')
user_name = st.sidebar.text_input('Your name', value='Analyst')
min_amount = st.sidebar.slider('Minimum order amount ($)', 0, 300, 50)
region_choice = st.sidebar.selectbox('Region', ['All', 'North', 'South', 'East', 'West'])
show_raw = st.sidebar.checkbox('Show raw data table')

st.sidebar.write(f'Hello, {user_name}!')
'''

with open('app.py', 'a') as f:
    f.write(widgets_code)
print('Widgets written')

data_code = '''
@st.cache_data
def load_sales_data():
    rng = np.random.default_rng(seed=11)
    regions = ['North', 'South', 'East', 'West']
    rows = []
    for _ in range(200):
        rows.append({
            'region': rng.choice(regions),
            'product': rng.choice(['Mouse', 'Keyboard', 'Monitor', 'Webcam']),
            'amount': round(float(rng.uniform(10, 300)), 2),
        })
    return pd.DataFrame(rows)

sales = load_sales_data()
'''

with open('app.py', 'a') as f:
    f.write(data_code)
print('Data loading written')

filter_and_display_code = '''
filtered = sales[sales['amount'] >= min_amount]
if region_choice != 'All':
    filtered = filtered[filtered['region'] == region_choice]

st.metric('Orders shown', len(filtered))
st.metric('Total revenue', f"${filtered['amount'].sum():,.2f}")

if show_raw:
    st.dataframe(filtered)
'''

with open('app.py', 'a') as f:
    f.write(filter_and_display_code)
print('Filtering and display written')

charts_code = '''
st.subheader('Revenue by Region')
region_totals = filtered.groupby('region')['amount'].sum()
st.bar_chart(region_totals)

st.subheader('Order Amount Distribution')
amount_counts = filtered['amount'].round(-1).value_counts().sort_index()
st.line_chart(amount_counts)
'''

with open('app.py', 'a') as f:
    f.write(charts_code)
print('Charts written')

columns_code = '''
st.subheader('Regional Comparison')
col1, col2, col3, col4 = st.columns(4)
for col, region in zip([col1, col2, col3, col4], ['North', 'South', 'East', 'West']):
    region_total = filtered[filtered['region'] == region]['amount'].sum()
    col.metric(region, f'${region_total:,.0f}')
'''

with open('app.py', 'a') as f:
    f.write(columns_code)
print('Columns layout written')

tabs_code = '''
tab1, tab2 = st.tabs(['Summary', 'About'])

with tab1:
    st.write(f'Showing {len(filtered)} orders totaling ${filtered["amount"].sum():,.2f}.')

with tab2:
    st.write('This dashboard was built entirely in Python using Streamlit, pandas, and numpy.')
'''

with open('app.py', 'a') as f:
    f.write(tabs_code)
print('Tabs written')

cache_note_code = '''
st.sidebar.divider()
if st.sidebar.button('Clear cached data'):
    load_sales_data.clear()
    st.sidebar.write('Cache cleared, data will reload on next run.')
'''

with open('app.py', 'a') as f:
    f.write(cache_note_code)
print('Cache control written')

import py_compile
py_compile.compile('app.py', doraise=True)
print('app.py is valid, runnable Python')

with open('app.py') as f:
    contents = f.read()
print(contents)

download_code = '''
st.sidebar.divider()
csv_data = filtered.to_csv(index=False).encode('utf-8')
st.sidebar.download_button(
    label='Download filtered data as CSV',
    data=csv_data,
    file_name='filtered_sales.csv',
    mime='text/csv',
)

st.divider()
st.caption('Built with Streamlit, pandas, and numpy.')
'''

with open('app.py', 'a') as f:
    f.write(download_code)
print('Download button and footer written')

py_compile.compile('app.py', doraise=True)
with open('app.py') as f:
    final_line_count = len(f.readlines())
print(f'app.py is valid and complete: {final_line_count} lines')