Duplicate from os1187/Estimated_App_Rollout_Usage_Volume

Co-authored-by: Oleg Seifert <os1187@users.noreply.huggingface.co>

.gitattributes

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README.md

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+---
+title: Estimated App Rollout Usage Volume
+emoji: 🏃
+colorFrom: pink
+colorTo: gray
+sdk: gradio
+sdk_version: 3.19.1
+app_file: app.py
+pinned: false
+duplicated_from: os1187/Estimated_App_Rollout_Usage_Volume
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import altair as alt
+import gradio as gr
+import pandas as pd
+import numpy as np
+
+
+def plot_usage_volume(system1, users1, timeline1, system2, users2, timeline2):
+    # 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([system1, system2]):
+        # Calculate the monthly usage volume based on the estimated number of users and timeline
+        usage = np.concatenate([
+            np.linspace(0, [users1, users2][i], int([timeline1, timeline2][i] * 0.25)),
+            np.linspace([users1, users2][i] * 0.5, [users1, users2][i], int([timeline1, timeline2][i] * 0.5)),
+            np.linspace([users1, users2][i], [users1, users2][i] * 0.75, int([timeline1, timeline2][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 multiline plot of the usage volume for each system
+    line = alt.Chart(data).mark_line().encode(
+        x=alt.X('Month', sort=list(data['Month'].unique())),
+        y='Usage:Q',
+        color='System:N'
+    ).properties(title="System Rollout Usage Volume Plot")
+
+    # Create an aggregated line for the total usage across both systems
+    aggregated_data = data.groupby(['Month']).sum().reset_index()
+    aggregated_data['System'] = 'Total'
+    data = pd.concat([data, aggregated_data], axis=0)
+
+    aggregated_line = alt.Chart(aggregated_data).mark_line(color='black').encode(
+        x=alt.X('Month', sort=list(data['Month'].unique())),
+        y='Usage:Q'
+    )
+
+    # Combine the multiline and aggregated line plots
+    chart = alt.layer(line, aggregated_line)
+
+    # Return the plot as a Gradio output
+    return chart
+
+# Define the input components
+inputs = [
+    gr.inputs.Textbox(label="System 1 Name", default="System1"),
+    gr.inputs.Number(label="System 1 Users", default=1000),
+    gr.inputs.Number(label="System 1 Timeline (months)", default=12),
+    gr.inputs.Textbox(label="System 2 Name", default="System2"),
+    gr.inputs.Number(label="System 2 Users", default=500),
+    gr.inputs.Number(label="System 2 Timeline (months)", default=18)
+]
+
+# Define the output component
+output = gr.Plot(label="Usage Volume Plot")
+
+# Create the interface
+iface = gr.Interface(fn=plot_usage_volume, inputs=inputs, outputs=output, title="System Rollout Usage Volume Plot")
+
+# Launch the interface
+iface.launch()

requirements.txt

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+pandas
+numpy
+matplotlib
+plotly
+altair