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VeriSight Local Run Guide

This document explains how to run the backend API and frontend UI for this project.

Prerequisites

  • Windows PowerShell
  • Python virtual environment at .venv
  • Node.js and npm installed

1) Run Backend (FastAPI)

Open a new terminal at project root.

cd C:\Users\JHASHANK\Downloads\VERISIGHT_V1
.\.venv\Scripts\Activate.ps1
python -m uvicorn engine.pipeline.app:app --host 127.0.0.1 --port 8000 --reload

Backend URLs:

2) Run Frontend (React + Vite)

Open another terminal.

cd C:\Users\JHASHANK\Downloads\VERISIGHT_V1\frontend
npm install
npm run dev

Open the Vite URL shown in terminal (usually http://localhost:5173).

3) How the UI Works

  • Input: product image only
  • The UI auto-sends today date for order_date and delivery_date
  • mfg_date_claimed is not sent, so OCR can infer manufacturing date from the image

Quick Troubleshooting

Error: ECONNREFUSED 127.0.0.1:8000

Cause: backend is not running.

Fix:

  1. Start backend using the command in section 1.
  2. Keep backend terminal open while using frontend.

Error: No module named uvicorn

Install API dependencies inside .venv:

python -m pip install uvicorn fastapi python-multipart

Frontend started from wrong folder

If you run npm commands from root accidentally, move to frontend folder first:

cd C:\Users\JHASHANK\Downloads\VERISIGHT_V1\frontend

4) Canonical End-to-End Benchmark

Run a benchmark report with metrics + latency + calibration:

python evaluation/evaluate_system.py --output-json evaluation/latest_benchmark.json --append-history

Key outputs:

  • evaluation/latest_benchmark.json
  • evaluation/benchmark_history.json

Check regression status from saved benchmark history:

python evaluation/check_regression.py --history-json evaluation/benchmark_history.json

Calibrate decision thresholds from a benchmark report that includes per-sample data:

python evaluation/calibrate_thresholds.py --input-json evaluation/latest_benchmark.json --output-json evaluation/calibrated_thresholds.json

5) Runtime Tuning (Optional)

Environment variables:

  • VERISIGHT_MAX_CONCURRENT_REQUESTS (default 4)
  • VERISIGHT_REQUEST_TIMEOUT_MS (default 15000)
  • VERISIGHT_OCR_ENGINE (auto, easy, paddle, yolo)
  • VERISIGHT_OCR_GPU (true / false)

About

Multi-layer deep learning system for detecting forged and manipulated images. Combines CNN, Vision Transformer, GAN-based analysis, and OCR for comprehensive image forensics authentication.

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