RUN_APP.md

Running the DocSentry Demo App

Quick start (3 commands)

# From: C:\Users\HP\Desktop\Anomaly Based project\

pip install -r requirements.txt
streamlit run app.py

That's it. Streamlit will open the app at http://localhost:8501 in your browser.

Optional: install Tesseract OCR (for full text-rule checks)

1. Download from https://github.com/UB-Mannheim/tesseract/wiki
2. Run the .exe installer
3. Check "Add Tesseract to system PATH" during install
4. Restart your terminal, then streamlit run app.py again

The app works without Tesseract too — only the text/OCR-based checks are skipped.

What's in the app

Tab 1 — Single-document analysis (your primary demo)

• Drag-drop a PNG / JPG / PDF
• See risk band (LOW / MEDIUM / HIGH / CRITICAL) in big colored text
• Sub-score breakdown bar chart
• ELA heatmap, copy-move match visualization, noise inconsistency heatmap (image files)
• Producer / creator / fonts table (PDFs)
• Trained ML model verdict (if models/forgery_rf.joblib exists)
• Download audit JSON or formatted PDF report

Tab 2 — Cross-document consistency check (the novel angle)

• Upload 2–4 documents for the same applicant
• App extracts name, DOB, address, account, IFSC from each
• Field-by-field comparison table with green/yellow/red status
• Mismatch detector with similarity scores
• Download consistency report JSON

Tab 3 — Batch audit

• Point at a folder, scan every file in it
• Get risk band per file as a sortable table
• Download CSV for the underwriting team

Demo flow (for the pitch)

1. Open Tab 1, drop in a clean land_000.png from data/images/originals/ → "LOW" green band, no evidence
2. Drop in a land_005_copy_move.png from data/images/tampered/ → "HIGH" orange band, copy-move evidence
3. Click through the heatmap tabs → judges see real visualizations, not just numbers
4. Click "Download audit PDF" → a bank-letterhead report renders
5. Switch to Tab 2, upload 2 different agreement_*.png files → "HIGH" mismatch because names differ
6. Switch to Tab 3, point at data/ → batch-process 250+ files in a few seconds

Total demo time: 3 minutes. Hands the judges something to download.

Project file layout

C:\Users\HP\Desktop\Anomaly Based project\
├── app.py                              <-- Streamlit UI (this app)
├── forensics.py                        <-- Core detection module
├── audit_report.py                     <-- PDF report generator
├── requirements.txt                    <-- pip dependencies
├── RUN_APP.md                          <-- this file
├── DATASETS.md                         <-- where to get more training data
├── COLAB_QUICKSTART.md                 <-- Colab usage guide
├── anomaly_detection_banking.ipynb     <-- local Jupyter notebook
├── anomaly_detection_banking_COLAB.ipynb <-- Colab notebook
├── data/
│   ├── images/originals/        130 genuine docs
│   ├── images/tampered/         130 tampered docs
│   ├── pdfs/originals/          30 PDFs
│   ├── pdfs/tampered/           30 tampered PDFs
│   └── statements/              60 statements
└── models/
    └── forgery_rf.joblib        (created after running Section 7.5 in the notebook)

Common issues

"ModuleNotFoundError: No module named 'forensics'" You're not in the project folder. cd "C:\Users\HP\Desktop\Anomaly Based project" first, then streamlit run app.py.

"streamlit: command not found" Streamlit didn't install. Re-run pip install -r requirements.txt. On Windows, you may need python -m streamlit run app.py instead.

The "Download audit PDF" button shows a warning Make sure reportlab installed cleanly. Re-run pip install reportlab.

Cross-doc tab says "ocr_skipped" for every field You don't have Tesseract installed. The forensic checks still work; only the cross-doc field extraction needs OCR. Install Tesseract (see above) to unlock that tab.

The trained ML model section doesn't appear You haven't run Section 7.5 in the notebook yet. Open anomaly_detection_banking.ipynb, run all cells through Section 7.5; that creates models/forgery_rf.joblib. The Streamlit app picks it up automatically.

Architecture sketch

┌──────────────────────────────────────────────┐
│              app.py  (Streamlit)             │
│   Tab1: Single doc   Tab2: Cross-doc         │
│   Tab3: Batch audit  Downloads: JSON + PDF   │
└──────────────────┬───────────────────────────┘
                   │ imports
        ┌──────────┴──────────┐
        ▼                     ▼
   forensics.py        audit_report.py
   - ELA / copy-move    - ReportLab PDF
   - noise / EXIF       - Heatmap embeds
   - PDF audit          - Bank letterhead
   - OCR + text rules   - Evidence list
   - RF model load
   - Cross-doc check

All three files run on plain Python 3.10+, CPU-only, no paid APIs.