app.py

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import gradio as gr

def plot_usage_volume(systems, users, timelines):
    # Create empty dataframe to hold usage data
    data = pd.DataFrame(columns=["System", "Month", "Usage"])

    # Generate usage data for each system based on typical rollout behaviors
    for i, system in enumerate(systems):
        # Calculate the monthly usage volume based on the estimated number of users and timeline
        usage = np.concatenate([
            np.linspace(0, users[i], int(timelines[i] * 0.25)),
            np.linspace(users[i] * 0.5, users[i], int(timelines[i] * 0.5)),
            np.linspace(users[i], users[i] * 0.75, int(timelines[i] * 0.25))
        ])

        # Add the usage data to the dataframe
        months = ["Month {}".format(j) for j in range(1, len(usage) + 1)]
        system_data = pd.DataFrame({"System": [system] * len(usage), "Month": months, "Usage": usage})
        data = pd.concat([data, system_data], axis=0)

    # Create a larger plot with multiple subplots
    fig, axs = plt.subplots(2, 1, figsize=(12, 8), sharex=True, gridspec_kw={'hspace': 0.5})

    # Plot the usage volume for each system
    for i, system in enumerate(systems):
        system_data = data[data["System"] == system]
        axs[0].plot(system_data["Month"], system_data["Usage"], label=system)
        axs[0].set_ylabel("Usage Volume")
        axs[0].set_title("Usage Volume for Various Systems")

    # Add a line plot that aggregates the usage of all systems
    aggregated_data = data.groupby("Month").sum().reset_index()
    axs[1].plot(aggregated_data["Month"], aggregated_data["Usage"], label="All Systems", linewidth=3, linestyle="--")
    axs[1].set_xlabel("Month")
    axs[1].set_ylabel("Usage Volume")
    axs[1].set_title("Aggregated Usage Volume for All Systems")

    # Set xticks to only show every 3 months
    axs[0].set_xticks(aggregated_data.index[::3])
    axs[1].set_xticks(aggregated_data.index[::3])

    # Set xticklabels to show only the months with a step of 3
    axs[0].set_xticklabels(aggregated_data["Month"][::3], rotation=45, fontsize=10)
    axs[1].set_xticklabels(aggregated_data["Month"][::3], rotation=45, fontsize=10)

    # Add a legend to the plot
    axs[0].legend()
    axs[1].legend()

    # Return the plot as a Gradio output
    return gr.outputs.Image(plt)

# Create input components for the Gradio app
system1 = gr.inputs.Textbox(label="System 1 Name", default="System1")
users1 = gr.inputs.Slider(minimum=100, maximum=10000, step=100, default=1000, label="System 1 Estimated Users")
timeline1 = gr.inputs.Slider(minimum=6, maximum=48, step=6, default=12, label="System 1 Timeline (Months)")

# Add input components for the remaining two systems
system2 = gr.inputs.Textbox(label="System 2 Name", default="System2")
users2 = gr.inputs.Slider(minimum=100, maximum=10000, step=100, default=500, label="System 2 Estimated Users")
timeline2 = gr.inputs.Slider(minimum=6, maximum=48, step=6, default=18, label="System 2 Timeline (Months)")

system3 = gr.inputs.Textbox(label="System 3 Name", default="System3")
users3 = gr.inputs.Slider(minimum=100, maximum=10000, step=100, default=2000, label="System 3 Estimated Users")
timeline3 = gr.inputs.Slider(minimum=6, maximum=48, step=6, default=24, label="System 3 Timeline (Months)")

# Create the Gradio interface
inputs = [system1, users1, timeline1, system2, users2, timeline2, system3, users3, timeline3]
output = gr.outputs.Image("plot.png")
title = "System Rollout Usage Volume Plot"
description = "Enter the rollout parameters for three different systems and see the resulting usage volume plot."

gr.Interface(plot_usage_volume, inputs, output, title=title, description=description).launch();