LIRA - provides real-time conversations, context-aware responses, and on-demand image generation. It listens, understands, and interacts naturally to assist users with daily tasks, emotional check-ins, and creative prompts.

Lira is a sleek Flutter mobile app (iOS/Android) that acts as your always-on voice buddy—like ChatGPT’s voice mode but cozier.
It uses a cloned "grandma" voice to provide empathetic advice on daily life, emotional check-ins, quick planning (e.g., "Remind me about that meeting in underepresented languages?"), or just venting sessions.

Hands-free, real-time chat: Speak naturally (even with Ethiopian accents), it listens live, thinks via AI, and responds in a warm, storytelling tone.
Privacy-first (mostly on-device), with optional integration with Neuroviate vibes for multicultural empathy.
Monetization can be added later via premium voices or third-party integrations.
Target audience: Busy individuals craving low-key wisdom, starting in Ethiopia/global diaspora.

🔥 Tech Stack & Free LLM Options

Frontend

Backend

AI & LLMs (Free)

Speech Processing (Free)

📸 Screenshots

🏗️ UI Components

1. Home / Dashboard Screen (`lib/screens/home_screen.dart`)

User greeting with profile picture
"Good Morning" prompt
Main "Talk to AI assistant" card with Start Talking button
Voice and Image feature cards
Topics section with pill-shaped buttons
Information cards (Blood pressure, Sleep)
Bottom navigation bar with AI sparkle button

2. Voice Analysis Screen (`lib/screens/voice_analysis_screen.dart`)

"Listening..." indicator
Animated 3D orb visualizer with gradient colors
Live transcript display
Bottom control bar with timer, microphone button, and cancel button

3. Smart Chat Screen (`lib/screens/smart_chat_screen.dart`)

Chat interface with AI and user message bubbles
Sparkle icons for AI messages
Audio message bubbles with waveform visualization
Text input field with mic and add buttons
Pre-populated sample conversation

4. Shared Components

Gradient Background (lib/utils/gradient_background.dart) — Purple/pink gradient
Status Bar (lib/widgets/status_bar.dart) — Time, signal, WiFi, battery
Orb Visualizer (lib/widgets/orb_visualizer.dart) — Animated 3D sphere with swirling patterns

🎨 Design Features

Purple/pink gradient backgrounds matching app visuals
Rounded corners on all UI elements
Modern, clean aesthetic
Smooth animations on the orb visualizer
Consistent color scheme using #9B7EDE purple

🧠 Lira MVP Workflow

flowchart TD
    A[User speaks into Flutter app] --> B[Flutter captures audio]
    B --> C[Speech-to-Text (Whisper / Vosk / Coqui STT)]
    C --> D[Text sent to Python FastAPI backend]
    D --> E[Backend queries Free LLM (Mistral / LLaMA / OpenRouter)]
    E --> F[AI generates agentic response (grandma voice style)]
    F --> G[Text returned to Flutter app]
    G --> H[Text-to-Speech (Coqui TTS / flutter_tts)]
    H --> I[Flutter plays AI voice response]
    I --> A[User continues conversation]

Workflow explanation:

User speaks → Flutter captures audio
Audio → text via STT
Python backend receives text → queries free LLM
LLM generates empathetic, agentic response
Text-to-speech converts AI text → voice
Flutter plays voice back to user
Conversation continues naturally

🛠️ Free Backend Setup (Python + LLM)

Python with FastAPI for REST API endpoints
Free LLM options: OpenRouter, HuggingFace Inference (Mistral, LLaMA, Grok, Qwen)
Speech-to-Text: Whisper (local) or Vosk
Text-to-Speech: Coqui TTS or flutter_tts
Conversation memory: store last 3–5 messages in RAM (privacy-first)

Fully free, no subscription required, and privacy-friendly MVP

📂 Project Structure

Lira/
│── lib/
│   ├── screens/
│   │   ├── home_screen.dart
│   │   ├── voice_analysis_screen.dart
│   │   └── smart_chat_screen.dart
│   ├── widgets/
│   │   ├── status_bar.dart
│   │   └── orb_visualizer.dart
│   └── utils/
│       └── gradient_background.dart
│── assets/
│── backend/
│   ├── app/
│   │   ├── main.py (FastAPI factory)
│   │   ├── routers/ (chat, stt, tts routes)
│   │   ├── schemas.py (Pydantic models)
│   │   └── services/ (LLM provider abstractions)
│   └── requirements.txt
│── README.md

▶️ Getting Started

1. Clone the repository

git clone https://github.com/your-username/lira.git
cd lira

2. Install Flutter dependencies

flutter pub get

3. Run the app

flutter run

4. Backend setup

cd backend
pip install -r requirements.txt
cp .env.example .env
# Edit .env and add your LLM API key
uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

5. Configure environment

Edit backend/.env:

LLM_API_BASE_URL=https://openrouter.ai/api/v1
LLM_API_KEY=sk-...
LLM_MODEL=mistralai/mistral-7b-instruct

6. Configure Flutter backend URL

Edit lib/config/api_config.dart and set your backend URL:

Local: http://localhost:8000
Android Emulator: http://10.0.2.2:8000
Physical device: http://YOUR_COMPUTER_IP:8000

📖 For detailed setup instructions, see SETUP.md

📌 Roadmap for MVP → Full App

Multi-language support (Amharic, English)
Premium voices & AI personality options
Push notifications & reminders
Advanced conversation memory & reasoning
Integrations with Neuroviate for multicultural empathy
Polished UI animations and orb visualizer

🗣️ Voice + Agentic Integration Checklist

Audio capture (Flutter): use record or flutter_sound to stream PCM via WebSocket to the /stt endpoint. Buffer 1–2s chunks for responsiveness.
Speech-to-Text (Python): replace the stub with Whisper (faster-whisper) or Vosk. Emit partial transcripts to the client so voice_analysis_screen.dart can display live text.
Conversation hand-off: send the latest transcript plus last 5–10 turns to /chat. The backend keeps persona prompts + temperature settings server-side.
LLM provider config: switch models using LLM_MODEL env var without touching Flutter. Supports OpenRouter, HuggingFace or local inference once you point LLM_BASE_URL accordingly.
Text-to-Speech: call /tts with the assistant reply. Implement Coqui TTS (offline) or gTTS for a quick cloud option; Flutter plays via just_audio.
Memory + tools: use conversation payload to pass lightweight memory now; later extend backend to persist slots and emit tool_calls for reminders, journaling, etc.

🤝 Contributing

Pull requests welcome! Please open an issue for major changes.

📄 License

MIT License

Lira

Install / Use

README