National EWS Data Routing & Verification Engine 🛡️

An unbribable, real-time data orchestration platform designed to automate eligibility validation for India's Economically Weaker Sections (EWS) quota system. This repository replaces vulnerable, manual, paper-based income certificate submissions with automated, cross-registry telemetry and zero-knowledge data pipelines.

Created by: Srinivasta

Application Framework: Streamlit Web Architecture

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🚀 System Architecture Overview

The platform operates across a decentralized infrastructure model designed to systematically combat wealth-evasion strategies (such as digital cash-flow obfuscation or hiding real estate assets behind undivided ancestral lines):

1. Input Registry Mesh (Zero-Knowledge Ingestion): Accepts an applicant's core credentials (PAN and Aadhaar) to tokenise identity, sending a binary verification request downstream without traveling raw sensitive data.
2. Cross-Database Telemetry Bus: Queries live synthetic endpoints mirroring the Income Tax Department (CBDT), Banking aggregation systems (UPI velocity logs), and State Land registries (Bhulekh).
3. Ancestral Inheritance Mapping Logic: Programmatically breaks down undivided generational land assets. It calculates an applicant’s virtual inherited share from deceased or living grandparents to counter structural asset loopholes.
4. Dynamic Purchasing Power Parity (PPP) Tiering: Standardizes structural financial thresholds by applying custom multi-weights across Tier-1, Tier-2, and Tier-3 geographic classifications.

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🛠️ Repository File Layout

EWS-Data-Routing-Engine/
│
├── .github/
│   └── workflows/
│       └── streamlit-ci.yml    # Continuous Integration testing pipeline
│
├── app.py                      # Production Streamlit UI Dashboard Interface
├── engine.py                   # Algorithmic decision and risk scoring matrix
├── mock_data.py                # Synthetic profile pipeline data generator (1,000 logs)
├── test_engine.py              # Automated Unit-Test suite for mathematical logic
├── requirements.txt            # Python environment third-party dependencies
└── README.md                   # System documentation (This File)

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📥 Local Installation & Boot Routine

Follow these explicit terminal steps to initialize, test, and host this architecture locally:

1. Clone and Prepare the Workspace

# Initialize a local directory and move into it
mkdir EWS-Data-Routing-Engine && cd EWS-Data-Routing-Engine

# Pull down dependencies
pip install -r requirements.txt

2. Generate the Synthetic Registry Logs

Run the mock generation module to create your simulated local database pool of 1,000 applicant profiles:

python mock_data.py

3. Run Automated Validation Checks

Execute the unit testing script to verify that purchasing-parity rules and grandfather asset calculation logic pass constraints:

python -m unittest test_engine.py

4. Boot Up the Web App Interface

Launch your responsive Streamlit application directly into your local browser window:

streamlit run app.py

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📊 Core Operational Interface Modes

The interface provides three operational views via the System Controls panel on the left sidebar:

• Single Applicant Audit: Simulates real-time manual checks. It pulls individual registry payloads, assesses risk scores based on luxury spending proxies (utility metrics vs reported income), and generates clear system warning alerts if the statutory 5-acre agricultural ceiling is breached.
• 1,000 Profile Database Analytics: Processes bulk database logs into interactive, colored Scatter Plots and Histograms built via Plotly. This mode segregates records into In EWS and Above EWS categories with dedicated download buttons.
• Upload Application File (.CSV): Allows an administrator to upload any custom batch template. It processes rows on the fly and integrates an On-Demand EWS Certificate Generator to compile official, formatted PDF verification summaries.

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🔐 Compliance & Security Blueprints

• Data Integrity: Real-time data synchronization with bank aggregators bypasses locally forged, paper-based documents.
• Algorithmic Parity: Removes political or bureaucratic influence by routing decisions purely through deterministic code logic.
• PDF Auditing: Generated reports feature a clear structural matrix rendering metadata, calculated effective land holding metrics, and systemic assessment scores for audit-ready documentation. """

⚠️ IMPORTANT COPYRIGHT NOTICE

All Rights Reserved © 2026 T A Srinivas. This repository is strictly for portfolio viewing purposes. DO NOT COPY, CLONE, OR REDISTRIBUTE this code. Stolen copies or unauthorized forks will be reported immediately for a GitHub copyright takedown.

• Lead Architect & Developer: Srinivasta

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