app.py

import gradio as gr
import plotly.graph_objects as go
import pandas as pd

# Capex in Millions of USD per Quarter of Microsoft, Google, Meta, Apple, Amazon
big_five_capex: list[tuple[str, int, int, int, int, int]] = [
   ("2014 Q3", 1282, 2417, 482, 3826, 1378),
   ("2014 Q4", 1490, 3606, 517, 3217, 1145),
   ("2015 Q1", 1391, 2927, 502, 2369, 871),
   ("2015 Q2", 1781, 2515, 549, 2043, 1213),
   ("2015 Q3", 1356, 2406, 780, 3618, 1195),
   ("2015 Q4", 2024, 2102, 692, 3612, 1309),
   ("2016 Q1", 2308, 2444, 1132, 2336, 1179),
   ("2016 Q2", 2655, 2136, 995, 2809, 1711),
   ("2016 Q3", 2163, 2554, 1095, 3977, 1841),
   ("2016 Q4", 1988, 3078, 1269, 3334, 3073),
   ("2017 Q1", 1695, 2508, 1271, 2975, 2148),
   ("2017 Q2", 2283, 2831, 1444, 2277, 3113),
   ("2017 Q3", 2132, 3538, 1755, 3865, 3074),
   ("2017 Q4", 2586, 4307, 2263, 2810, 3619),
   ("2018 Q1", 2934, 7299, 2812, 4195, 3098),
   ("2018 Q2", 3980, 5477, 3460, 3267, 3243),
   ("2018 Q3", 3602, 5282, 3342, 3041, 3352),
   ("2018 Q4", 3707, 7081, 4301, 3355, 3734),
   ("2019 Q1", 2565, 4638, 3837, 2363, 3290),
   ("2019 Q2", 4051, 6126, 3633, 2000, 3562),
   ("2019 Q3", 3385, 6732, 3532, 2777, 4697),
   ("2019 Q4", 3545, 6052, 4100, 2107, 5312),
   ("2020 Q1", 3767, 6005, 3558, 1853, 6795),
   ("2020 Q2", 4744, 5391, 3255, 1565, 7459),
   ("2020 Q3", 4907, 5406, 3689, 1784, 11063),
   ("2020 Q4", 4174, 5479, 4613, 3500, 14823),
   ("2021 Q1", 5089, 5942, 4303, 2269, 12082),
   ("2021 Q2", 6452, 5496, 4641, 2093, 14288),
   ("2021 Q3", 5810, 6819, 4346, 3223, 15748),
   ("2021 Q4", 5865, 6383, 5400, 2803, 18935),
   ("2022 Q1", 5340, 9786, 5441, 2514, 14951),
   ("2022 Q2", 6871, 6828, 7572, 2102, 15724),
   ("2022 Q3", 6283, 7276, 9375, 3289, 16378),
   ("2022 Q4", 6274, 7595, 9043, 3787, 16592),
   ("2023 Q1", 6607, 6289, 6823, 2916, 14207),
   ("2023 Q2", 8943, 6888, 6134, 2093, 11455),
   ("2023 Q3", 9917, 8055, 6543, 2163, 12479),
   ("2023 Q4", 9735, 11019, 7665, 2392, 14588),
   ("2024 Q1", 10952, 12012, 6400, 1996, 14925),
   ("2024 Q2", 13873, 13186, 8173, 2151, 17620),
]


def create_chart():
    df = pd.DataFrame(big_five_capex, columns=['Quarter', 'Microsoft', 'Google', 'Meta', 'Apple', 'Amazon'])
    
    fig = go.Figure()
    
    companies = ['Microsoft', 'Google', 'Meta', 'Apple', 'Amazon']
    colors = ['#80bb00', '#ee161f', '#0065e3', '#000000', '#ff6200']
    
    for company, color in zip(companies, colors):
        fig.add_trace(go.Bar(
            x=df['Quarter'],
            y=df[company],
            name=company,
            marker_color=color
        ))
    
    fig.update_layout(
        title='Capital Expenditure of the Big Five Tech Companies in Millions of USD per Quarter',
        xaxis_title='Quarter',
        yaxis_title='Capex (Millions of USD)',
        barmode='stack',
        legend_title='Companies',
        height=800
    )

    return fig


with gr.Blocks() as demo:
    with gr.Tab("Big Five Capex"):
        big_five_capex_plot: gr.Plot = gr.Plot()
        generate_btn = gr.Button("Generate Plot")
    generate_btn.click(fn=create_chart, outputs=big_five_capex_plot)


if __name__ == "__main__":
    demo.launch()