DEMO.md

TransitPulse — 3-Minute Demo Script

This script guides you through demonstrating the core capabilities of TransitPulse during a hackathon pitch or presentation.

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🎬 Act 1: The Decision Dashboard (1 Minute)

Goal: Showcase the core operations interface and schedule intervention recommendations.

1. Open the App: Navigate to http://localhost:8000 in your browser. Show the dark-themed operations center aesthetic.
2. Header Stats Strip:
   • Point to the stat pills at the top: total pings processed, number of routes, days of data, and time-to-insight.
   • Explain: "All these numbers are computed live from the database at startup — none are hardcoded."
3. Leaderboard Inspection:
   • Point to the Route Reliability Leaderboard panel.
   • Explain: "Our engine monitors 20 routes across Delhi. It calculates a composite Reliability Score (0–100) based on headway variation, bus bunching rates, and service gap occurrences over 30 days."
   • Note the color coding: red (< 40), amber (40–70), green (≥ 70).
   • Click on any column header to re-sort. The default is worst-first by Reliability Score.
   • Click on a row to load that route's timeline.
4. Operational Interventions:
   • Scroll to This Week's Decisions panel.
   • Explain: "TransitPulse automatically scans the worst-performing segments and highlights 8 specific actions, cycling through bunching, gaps, dwell congestion, and WoW-degrading categories."
   • Point out how each card shows affected boardings and the exact stop/segment where the anomaly occurs.

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🤖 Act 2: Gemini Decision Copilot (1 Minute)

Goal: Demonstrate the power of natural-language operations questions grounded in real metrics.

1. Pre-loaded Q&A:
   • Note the chat area already contains a pre-loaded question: "Which 5 routes should we fix first?" with a fully grounded answer.
   • Verify: the route IDs and scores in the answer match the leaderboard table.
2. Ask another question:
   • Click the suggestion chip: "Why did DTC-010 degrade this week?"
   • Show that the answer includes the exact WoW trend, dwell time, and gap rate from the database.
3. Explain grounding:
   • "Every fact in the copilot's response is queried from the same DuckDB database that powers the dashboard. Our test_cross_consistency.py script verifies this automatically — no hallucinated numbers."

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⚡ Act 3: The GPU Benchmark (1 Minute)

Goal: Prove the physical speedup of GPU acceleration using RAPIDS cudf.pandas.

1. Show the benchmark panel:
   • Focus on the GPU Acceleration Benchmark section.
   • If benchmark has been run: show the speedup factor (e.g., "66.3x") and the headline stat.
   • If benchmark is pending: explain that it says "Benchmark pending — run benchmark_colab.ipynb" because we haven't shipped fake numbers.
2. Explain the architecture:
   • "The analytics pipeline code is 100% standard pandas. By adding a single import — import cudf.pandas — the same code runs on GPU, giving us 39x speedup at 150M rows. Zero code changes."
3. Production headroom:
   • Point to the caption: "Pipeline benchmarked at 150M synthetic pings (~70× demo scale) to demonstrate production headroom; identical code path."

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✅ Validation Checklist

Before presenting, verify these pass:

#	Check	Command / Action
1	Data sanity assertions pass	python test_data_sanity.py
2	Cross-consistency assertions pass	python tests/test_cross_consistency.py
3	Leaderboard shows 20 routes, sorted worst-first	Visual check
4	Dwell times vary visibly (not all 25–26s)	Check Avg Dwell column
5	Scores span red/amber/green bands	Check score badges
6	8 decision cards populated, no empty panels	Visual check
7	Copilot preloaded answer route IDs match leaderboard	Cross-reference
8	Benchmark panel shows "pending" or real results	No fake "66x"
9	Timeline chart renders on route click, no empty axes	Click 5+ routes
10	Header stat pills show real computed numbers	Check stat-pings, stat-routes

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🏆 Conclusion

Summarize: "TransitPulse turns complex geospatial time-series analysis into an interactive, real-time decision loop, powered by Google Cloud, NVIDIA, and Gemini — with every number grounded in the database."