# CropGuard GH — Standalone HTML App Documentation

**The self-contained, single-file browser application.**
Final Year Project · Oppong David · BTech Computer Technology, Kumasi Technical University

---

## 1. What this is

A complete crop disease detection front end in **one HTML file** — no install, no build step, no server required to open it. A user takes or chooses a photo of a crop leaf and immediately gets a disease name, a confidence score, a severity level (with colour), an urgency level, ordered treatment steps and suggested products.

It is the most accessible deliverable in the project: email the file, host it on any static link, or open it straight from a phone's storage. It works on a basic Android phone with no app-store download.

### The three files

| File | Language | Notes |
|---|---|---|
| `cropguard.html` | **Bilingual** | English ⇄ Twi toggle in the header. This is the main app. |
| `cropguard-en.html` | English only | Same app, no toggle — for English-only deployment. |
| `cropguard-tw.html` | Twi only | Same app, no toggle — for Twi-only deployment. |

All three are byte-for-byte self-contained: HTML, CSS and JavaScript in a single file, fonts from Google Fonts, no other external dependencies.

### Coverage
14 Ghanaian crops, 55 disease/healthy classes: maize, cassava, tomato, cocoa, cashew, plantain, yam, pepper, cowpea, groundnut, rice, okra, garden egg, mango.

---

## 2. How to use it

1. **Open** the file in any modern browser (phone or computer) — double-click it, or host it and visit the link.
2. **Take a photo** ("Take a photo of a leaf") or **Choose from gallery**. On a phone the camera button opens the rear camera directly.
3. **Check the photo** on the preview screen — the app reminds the user to fill the frame with the diseased leaf in good light — then tap **Analyse crop**.
4. **Read the result:** crop + disease, confidence bar, severity badge (green/amber/red), urgency, a numbered treatment list (most urgent first), suggested products and a short "about this disease" note.
5. **Scan another leaf** or **Share result** (uses the device share sheet, or copies to clipboard).

A **Help** sheet on the home screen explains all of this to first-time users, in the selected language.

---

## 3. The four-screen flow

The app is a single-page application with four screens (`home → preview → loading → result`) toggled by a small `show()` function — there is no routing library and no page reload.

```
 HOME ──tap camera/gallery──▶ PREVIEW ──tap Analyse──▶ LOADING ──result ready──▶ RESULT
   ▲                                                                               │
   └───────────────────────────── "Scan another leaf" ────────────────────────────┘
```

---

## 4. How the diagnosis works — two modes

The app is designed to **never be broken**, so it has two inference paths and picks automatically:

### Mode A — Connected to the trained model (accurate)
If an API URL is configured, the app sends the photo to the backend (`POST /predict`) and shows the real CNN prediction. This is the accurate mode and the one to use for real diagnosis. Set it by either:
- running `localStorage.setItem('cropguard_api', 'https://your-server:8000')` in the browser console, or
- hosting the app and pointing it at your deployed FastAPI server (see the backend documentation).

The footer shows **"Connected to AI model"** with a live dot when an API URL is set.

### Mode B — On-device estimate (offline fallback)
When **no** API URL is set, the app runs a genuine on-device image analysis in JavaScript using an HTML `<canvas>`. It:
1. draws the photo to a 180×180 canvas and reads the pixels,
2. drops near-white background pixels,
3. measures the proportion of **green**, **yellow**, **brown** and **dark** leaf pixels,
4. converts that into a *diseased-area ratio*, which sets the severity (early < 0.20 ≤ moderate < 0.55 ≤ severe), and
5. maps the dominant colour signature to a likely disease among common Ghanaian field patterns.

The footer shows **"On-device analysis"** in this mode.

> **Be honest about Mode B.** It is a real colour/lesion heuristic, not the trained model, and it is **not** the ~98% system. It is a reasonable offline estimate and a graceful fallback so the app still does something useful with no connectivity — but accurate, all-crop diagnosis comes from Mode A (the connected CNN). The on-screen confidence in Mode B is an estimate, and the app's disclaimer says clearly that this is a diagnostic aid, not a replacement for an extension officer.

---

## 5. Bilingual design (`cropguard.html`)

Everything the user sees exists in both English and Twi. The header toggle calls `setLang('en' | 'tw')`, which:
- stores the choice in `localStorage` (`cropguard_lang`) so it persists,
- swaps every UI string (elements tagged `data-i="key"` are filled from the language table),
- rebuilds the crop "pills" list in the chosen language,
- re-renders the current result in the new language if one is showing.

### Data model
All content lives in one JavaScript object, `DATA`, injected into the file:

```js
const DATA = {
  crops:    { en: [...14 names...], tw: [...14 names...] },
  t:        { en: {...UI strings...}, tw: {...} },     // labels, buttons, help text
  severity: { en: {early/moderate/severe...}, tw: {...} },
  help:     { en: [[title, body], ...], tw: [...] },
  diseases: {
    "cocoa_blackpod": {
      crop: { en: "Cocoa", tw: "Kookoo" },
      name: { en: "Cocoa Black Pod", tw: "Kookoo Aba Tuntum Yare" },
      cause:{ en: "...", tw: "..." },
      tx:   { en: ["step 1", ...], tw: ["..."] },       // treatment steps
      chem: { en: ["Copper hydroxide", ...], tw: ["..."] } // suggested products
    },
    "maize_healthy": { crop:{...}, name:{...}, healthy: true },
    ...
  }
};
```

The single-language files (`-en` / `-tw`) use the same structure flattened to one language and have no toggle.

> The Twi agronomic text was written carefully, but for the more specialised crops (cocoa, cashew, plantain, yam, etc.) a local agricultural extension officer or MoFA/COCOBOD agent should review it before farmer-facing release — some disease terms have no settled Twi equivalent.

---

## 6. Severity and urgency

Severity is derived from the diseased-area ratio (Mode B) or returned by the server (Mode A) and is shown with a colour and an urgency level:

| Severity | Colour | Urgency | Meaning |
|---|---|---|---|
| Early stage | 🟢 green | Routine | Small/localised (<~20% of leaf). Act soon. |
| Moderate stage | 🟠 amber | Urgent | ~20–60% of leaf; can spread fast. Treat this week. |
| Severe stage | 🔴 red | Emergency | >~60% affected. Act today. |

Healthy results skip severity and show a "no disease detected — keep monitoring" panel instead.

---

## 7. Privacy

No sign-up, no account, no analytics, no tracking. In Mode B the photo never leaves the device. In Mode A the photo is sent to the configured server only for the single prediction and is discarded there (the backend never writes it to disk). This matches the privacy-by-design intent of the report (§3.10/§3.13).

---

## 8. Deploying the HTML app

Because it is one static file, hosting is trivial. Any of these work:

- **Email / copy:** send `cropguard.html`; the recipient opens it on their phone.
- **Static host:** GitHub Pages, Netlify, Vercel, Firebase Hosting, or any web server — just upload the file.
- **Local:** open directly from storage (the camera/gallery inputs still work).

To connect it to your trained model in production, host the FastAPI backend (see the backend documentation), enable HTTPS, and set `cropguard_api` to its URL. CORS is already open on the backend so the browser can call it.

---

## 9. Customising it

- **Change treatment text / add a crop's advice:** edit the matching record in the `DATA.diseases` object (keep the `en`/`tw` shape, or just the one language in the single-language files). Class keys must match `recommendations.json` / `classes.json` so server predictions resolve to the right record.
- **Point at a different server:** change `localStorage.cropguard_api`.
- **Re-theme:** all colours are CSS variables at the top of the `<style>` block (`--leaf`, `--gold`, `--early/moderate/severe`, etc.).

> If you add a brand-new crop, you must also add labelled training images and retrain the model (see the Dataset Guide and backend documentation) — the HTML app can *display* a new class, but only the trained model can *detect* it.

---

## 10. Limitations & browser support

- **Mode B is an estimate, not the CNN** — see §4. For real diagnosis, connect the model.
- **A photo that isn't a clear leaf** is caught by a "not a clear leaf" check and the user is asked to retake it.
- **Very small lesions, extreme lighting, or a crop/disease not in the model** can still be misread — the disclaimer makes this clear.
- **Browsers:** any current Chrome, Firefox, Safari or Edge (mobile or desktop). The camera capture uses the standard file-input `capture` attribute; on desktop without a camera it falls back to file selection.
- **Connectivity:** Mode A needs internet to reach the server; Mode B needs none.