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6688cfa | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 | import gradio as gr
import pandas as pd
import json
from pathlib import Path
# Load data
def load_data():
tables = Path("artifacts/tables")
figures = Path("artifacts/figures")
app_summary = pd.read_csv(tables / "app_summary.csv")
pricing = pd.read_csv(tables / "pricing_decisions.csv")
sentiment = pd.read_csv(tables / "sentiment_per_app.csv")
with open(figures / "kpis.json") as f:
kpis = json.load(f)
return app_summary, pricing, sentiment, kpis
def get_overview():
app_summary, pricing, sentiment, kpis = load_data()
return (
f"Total Apps Analyzed: {kpis['total_apps']}",
f"Real Reviews Scraped: {kpis['total_real_reviews']}",
f"Synthetic Reviews: {kpis['total_synthetic_reviews']}",
f"Months Analyzed: {kpis['months_analyzed']}",
f"Best Satisfaction: {kpis['best_app_satisfaction']}",
f"Highest Churn Risk: {kpis['highest_churn_app']}",
f"Lowest Churn Risk: {kpis['lowest_churn_app']}"
)
with gr.Blocks(title="Productivity App Analyzer", theme=gr.themes.Soft()) as demo:
gr.Markdown("""
# Productivity App Analyzer
### AI for Big Data Management — ESCP Business School
*Analyzing TickTick, Todoist, Microsoft To-Do, and Any.do using real reviews + synthetic data*
""")
# KPI Row
with gr.Row():
kpi1 = gr.Textbox(label="Apps Analyzed", interactive=False)
kpi2 = gr.Textbox(label="Real Reviews", interactive=False)
kpi3 = gr.Textbox(label="Synthetic Reviews", interactive=False)
kpi4 = gr.Textbox(label="Months Analyzed", interactive=False)
with gr.Row():
kpi5 = gr.Textbox(label="Best Satisfaction", interactive=False)
kpi6 = gr.Textbox(label="Highest Churn", interactive=False)
kpi7 = gr.Textbox(label="Lowest Churn", interactive=False)
# Charts
gr.Markdown("## Visualizations")
with gr.Tabs():
with gr.Tab("Sentiment Analysis"):
gr.Image("artifacts/figures/sentiment_per_app.png",
label="Sentiment Distribution per App")
with gr.Tab("Churn & Satisfaction"):
gr.Image("artifacts/figures/churn_satisfaction_per_app.png",
label="Churn Risk & Satisfaction Scores")
with gr.Tab("Usage Trends"):
gr.Image("artifacts/figures/monthly_usage_trends.png",
label="Monthly Active Users - 18 Months")
with gr.Tab("ARIMA Forecasts"):
gr.Image("artifacts/figures/arima_forecasts.png",
label="6-Month User Forecasts per App")
# Pricing Decisions
gr.Markdown("## Pricing Recommendations")
def get_pricing_table():
_, pricing, _, _ = load_data()
return pricing[["app_name", "positive_ratio",
"negative_ratio", "avg_churn",
"avg_satisfaction", "pricing_action"]].round(3)
pricing_table = gr.Dataframe(
label="Rule-Based Pricing Decisions",
interactive=False
)
# App Summary
gr.Markdown("## App Performance Summary")
summary_table = gr.Dataframe(
label="Average Churn, Satisfaction & Daily Tasks",
interactive=False
)
# Load on startup
def load_all():
app_summary, pricing, sentiment, kpis = load_data()
k = get_overview()
pricing_df = pricing[["app_name", "positive_ratio",
"negative_ratio", "avg_churn",
"avg_satisfaction", "pricing_action"]].round(3)
return (*k, pricing_df, app_summary)
demo.load(
fn=load_all,
outputs=[kpi1, kpi2, kpi3, kpi4, kpi5, kpi6, kpi7,
pricing_table, summary_table]
)
gr.Markdown("""
---
*Data: 400 real Google Play reviews + 4,000 synthetic reviews |
Methods: VADER Sentiment, ARIMA Forecasting, Rule-Based Pricing*
""")
demo.launch() |