# Running the DocSentry Demo App

## Quick start (3 commands)

```powershell
# 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.