Kafka-Rider-Tracking/generate_pptx.py at main · GodishalaAshwith/Kafka-Rider-Tracking · GitHub

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from pptx import Presentation
from pptx.util import Inches, Pt
from pptx.enum.text import PP_ALIGN

def add_title_slide(prs, title, subtitle):
    slide_layout = prs.slide_layouts[0]
    slide = prs.slides.add_slide(slide_layout)
    title_shape = slide.shapes.title
    subtitle_shape = slide.placeholders[1]
    title_shape.text = title
    subtitle_shape.text = subtitle

def add_bullet_slide(prs, title, bullet_points):
    slide_layout = prs.slide_layouts[1]
    slide = prs.slides.add_slide(slide_layout)
    title_shape = slide.shapes.title
    body_shape = slide.placeholders[1]

    title_shape.text = title
    tf = body_shape.text_frame

    for i, point in enumerate(bullet_points):
        if i == 0:
            p = tf.paragraphs[0]
            p.text = point
        else:
            p = tf.add_paragraph()
            p.text = point

        p.font.size = Pt(20)

prs = Presentation()

# Slide 1: Title
add_title_slide(
    prs,
    "Real-Time AI Delivery Tracking System",
    "End-to-end, high-throughput spatial event processing architecture"
)

# Slide 2: Executive Summary
add_bullet_slide(
    prs,
    "Executive Summary",
    [
        "End-to-end, high-throughput, real-time spatial event processing architecture.",
        "Mimics the backend telemetry systems of modern gig-economy delivery platforms (UberEats, Zomato, Swiggy).",
        "Offloads complex geospatial calculations and ML inference from traditional REST APIs to a streaming layer (Apache Kafka + PySpark).",
        "Achieves sub-second latency for live tracking, routing ETA predictions, and fraud detection."
    ]
)

# Slide 3: Architecture & Technologies
add_bullet_slide(
    prs,
    "Architecture & Technologies",
    [
        "Ingestion (Apache Kafka KRaft mode): Brokers high-throughput GPS location pings asynchronously.",
        "Processing Engine (PySpark Structured Streaming): Stateful sliding windows (10-second intervals), real-time ML inferences, and spatial calculations.",
        "Backend / Router (Node.js/Express): Kafka Consumer to parse enriched payloads and broadcast updates via WebSockets (Socket.io).",
        "Frontend (Leaflet.js & HTML5): Listens to WebSocket events for sub-second map updates and heatmaps."
    ]
)

# Slide 4: Key Technical Implementations
add_bullet_slide(
    prs,
    "Key Technical Implementations",
    [
        "Scalable Event Streaming: Python-based producers push GPS coordinates to Kafka topics.",
        "Serverless Spatial Indexing: Uber H3 Indexing maps coordinates to hexagonal spatial bins inside the data stream, generating clustered density heatmaps.",
        "Dynamic Geofencing: Spatial UDF checks detect when riders fall within bounded polygons and routes them to a priority alerts topic.",
        "Internet Access Tunneling: Integrates NGrok tunneling and Vercel static hosting to ingest real-world GPS streams."
    ]
)

# Slide 5: Machine Learning on the Edge
add_bullet_slide(
    prs,
    "Machine Learning on the Edge",
    [
        "Pre-trained Scikit-Learn models are embedded directly into PySpark User-Defined Functions (UDFs).",
        "AI ETA Prediction (Random Forest Regressor): Predicts spatial time-to-completion based on traffic and time-of-day features.",
        "Ghost Rider / Fraud Detection (Isolation Forest): Unsupervised anomaly detection model that isolates spoofed GPS coordinates or unrealistic speeds.",
        "Impact: Flags anomalies instantly, triggering automated visual alerts directly on the frontend."
    ]
)

# Slide 6: Performance Metrics & Benchmarking
add_bullet_slide(
    prs,
    "Performance Metrics & Benchmarking",
    [
        "Load Test Parameters: Emitting continuous payloads over a 30-second window.",
        "10 Concurrent Riders: Processed 300 events (~9.94 msgs/sec).",
        "1,000 Concurrent Riders: Processed 27,000 events (~883.68 msgs/sec).",
        "10,000 Concurrent Riders: Processed 120,000 events (~3,846.16 msgs/sec).",
        "Result: The architecture scaled linearly, demonstrating no bottlenecks at nearly 4,000 GPS messages per second."
    ]
)

prs.save('Delivery_Tracking_Presentation.pptx')
print("Presentation generated successfully: Delivery_Tracking_Presentation.pptx")