# English AI Coach

English AI Coach is a starter project for a mobile AI-based English learning app. It helps users write short English sentences, find mistakes, and get clear corrections in English.

The project is intentionally lightweight: React Native, Expo, TypeScript, and React Navigation. It avoids unnecessary Expo-only libraries and is prepared for local AI with Ollama, speech input, text-to-speech, and push notifications.

## Features MVP

- HomeScreen with progress card and mock daily reminder
- Level selection for A1, A2, B1, and B2
- ChatScreen with user and AI messages
- AI replies can be read aloud on iPhone
- Ollama integration with offline fallback examples
- TypeScript-typed navigation
- Structure for future services, hooks, and utilities
- Web/PWA path prepared through Expo Web

## Beispiel

```text
User: I go yesterday to work

AI Coach:
Good try. Correct: "I went to work yesterday."
Use the past tense with "yesterday".
```

## Tech Stack

- React Native
- Expo
- TypeScript
- React Navigation
- Ollama API for local AI
- Text-to-speech with `expo-speech`
- Speech input currently through iPhone keyboard dictation
- Planned: in-app speech-to-text
- Planned: push notifications for daily reminders

## Projektstruktur

```text
english-ai-coach/
├── App.tsx
├── app.json
├── package.json
├── src/
│   ├── components/
│   │   ├── ChatBubble.tsx
│   │   ├── PrimaryButton.tsx
│   │   └── ProgressCard.tsx
│   ├── hooks/
│   │   └── useDailyReminder.ts
│   ├── navigation/
│   │   ├── AppNavigator.tsx
│   │   └── types.ts
│   ├── screens/
│   │   ├── ChatScreen.tsx
│   │   ├── HomeScreen.tsx
│   │   └── LevelScreen.tsx
│   ├── services/
│   │   └── mockAiCoach.ts
│   ├── theme/
│   │   └── colors.ts
│   └── utils/
│       └── levels.ts
└── README.md
```

## Installation

Requirements:

- Node.js
- npm
- Expo through `npx expo`

Install dependencies:

```bash
npm install
```

Create optional local AI configuration:

```bash
cp .env.example .env
```

Set your Ollama model in `.env`:

```text
EXPO_PUBLIC_OLLAMA_BASE_URL=http://localhost:11434
EXPO_PUBLIC_OLLAMA_MODEL=llama3.2
```

When testing on a real phone with Expo Go, `localhost` means the phone itself. Use your laptop IP instead, for example:

```text
EXPO_PUBLIC_OLLAMA_BASE_URL=http://192.168.10.102:11434
EXPO_PUBLIC_OLLAMA_MODEL=qwen2.5:7b
```

Start the app:

```bash
npm run start
```

## Speech Input And Playback

Playback works directly in Expo Go. AI replies are read aloud through the iPhone speaker. The chat also has buttons for `Read last answer` and `Stop`.

Speech input currently works through the iPhone keyboard:

- Tap the chat input field
- Press the microphone icon on the iPhone keyboard
- Speak English
- Review the text and send it

Real in-app speech-to-text with a custom microphone button is planned for later. That requires either a native iOS module after `expo prebuild` or a transcription API such as Whisper.

iOS Preview starten:

```bash
npm run ios
```

Web Preview starten:

```bash
npm run web
```

Note: local iOS simulator builds require macOS with Xcode. With Expo Go, the app can be tested on a real device.

## Ollama Integration

The chat uses `src/services/ollamaClient.ts` for real local AI replies. If Ollama is not reachable, `src/services/mockAiCoach.ts` automatically falls back to example replies. Model configuration lives in `src/config/ai.ts` and reads values from `.env`.

Local flow:

```text
React Native Chat UI
-> AI Service
-> local Ollama API
-> model such as llama, qwen, or mistral
-> English-only correction and explanation
```

For a PWA or local web version, the app can later run in the browser and connect to a local Ollama server on the network.

## PWA Idee

Die Web-Version kann spaeter als PWA erweitert werden:

- Auf Home-Bildschirm installieren
- Lokale Ollama-Verbindung konfigurieren
- Daily Reminder ueber Web Push pruefen
- Desktop- und Tablet-Layout optimieren

## Roadmap

- Chat UI erweitern
- Mock KI durch echte KI ersetzen
- Ollama API Client implementieren
- Level-spezifische Prompts erstellen
- Fehleranalyse und Vokabeltraining ergaenzen
- Fortschritt lokal speichern
- Speech-to-Text hinzufuegen
- Text-to-Speech fuer Aussprachetraining hinzufuegen
- Push Notifications fuer taegliche Uebungen einbauen
- PWA-Ausbau pruefen
- App Store Release vorbereiten

## Ziel

Ziel ist ein skalierbares Starterprojekt fuer eine KI-basierte Englischlern-App, die zuerst als mobile Expo-App funktioniert und spaeter lokal mit Ollama oder ueber eine externe KI-API erweitert werden kann.