ARS — Auto Repair Service

Rescuing your ride.

On-demand auto repair, on a map. ARS connects vehicle owners with nearby mechanics for booking, live ETA, in-app chat, payments, and AI-assisted diagnostics — with a real-time dashboard for mechanics.

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📱 Screenshots

  

_{Splash · Choose your role (customer or mechanic)}

More screens (booking map, live ETA, chat, mechanic dashboard) require a live Firebase project — see Getting Started.

📖 About

ARS (Auto Repair Service) is a Flutter mobile app that connects vehicle owners with professional mechanics for on-demand automotive repair. A customer can describe a problem, get an AI-assisted diagnosis and cost estimate, find a nearby mechanic on a live map, watch their ETA, chat in real time, and pay — while mechanics manage incoming jobs from a real-time dashboard.

It's built Android-first with a feature-first clean architecture, Firebase for backend services, and a self-hosted OSRM routing engine for accurate, zero-cost ETAs tuned for Metro Manila.

✨ Key Features

• 🤖 AI Diagnostic Chatbot — 87.4% accurate automotive diagnosis with Taglish support (201 terms, 97.5% accuracy)
• 💰 Smart Cost Estimation — Metro Manila pricing with urgency classification
• 🗺️ Real-time Location Services — find nearby mechanics and track requests on a live map
• 🧭 Live ETA — self-hosted OSRM routing with 30-second auto-refresh
• 👤 Auth & Roles — separate customer and mechanic flows (Firebase Auth)
• 🚙 Vehicle Management — add and manage multiple vehicles
• 🔧 Service Booking — engine, brake, tire, battery, oil change, AC, and more
• 💬 In-app Chat — real-time messaging between customer and mechanic
• 📊 Service Tracking — monitor a job from request to completion
• 🏪 Mechanic Dashboard — real-time map of nearby requests, online/offline toggle, earnings
• 🔔 Push Notifications — Firebase Cloud Messaging + local notifications

🛠️ Tech Stack

• Framework: Flutter 3.9+ · Language: Dart 3.9+
• Architecture: Feature-first clean architecture (data / domain / presentation)
• State management: Riverpod — provider infrastructure in place; feature-by-feature migration from StatefulWidget in progress (mechanic earnings migrated as the reference slice, with unit tests)
• Design system: ARS token-based theme (orange #F97316, light + dark) with widget/token tests
• Backend (Firebase): Auth, Cloud Firestore, Cloud Storage, Cloud Messaging, Crashlytics
• Maps & location: flutter_map (OpenStreetMap tiles), google_maps_flutter, geolocator
• Routing/ETA: self-hosted OSRM (custom server, no external routing cost)
• AI / ML (companion service — see below): ARS Rapide diagnostic API (FastAPI + Gemini 2.0 Flash, LangGraph, ChromaDB/RAG, Redis cache)
• Testing: flutter_test — design-system, architecture, and feature (Riverpod notifier) tests
• Tooling: flutter_launcher_icons, flutter_lints

🏗️ Architecture

ARS organizes code by feature, each split into clean-architecture layers. Customer screens are prefixed user_*; mechanic screens mechanic_*.

lib/
├── core/                     # Shared: auth, theme/design-system, services
│   ├── providers/            # Riverpod core providers (Firebase, OSRM, auth, repos)
│   ├── theme/                # ARS design system (tokens, light/dark themes)
│   ├── services/             # Notifications, OSRM routing, location sharing
│   ├── widgets/ utils/ models/ constants/
├── features/
│   ├── onboarding/           # Splash, onboarding, role selection
│   ├── customer/             # user_* — auth, booking, vehicles, dashboard, ...
│   │   └── booking/{data,domain,presentation}/
│   └── mechanic/             # mechanic_* — auth, dashboard, services, earnings
│       └── earnings/presentation/providers/   # ← Riverpod migration template
├── firebase_options.dart     # gitignored — generated via `flutterfire configure`
└── main.dart                 # Entry point (ProviderScope + Firebase init)

Principles: feature isolation, clear data/domain/presentation boundaries, shared code in core/, single responsibility per module. See docs/ARCHITECTURE.md.

🚀 Getting Started

Prerequisites

• Flutter SDK 3.9+ and Dart 3.9+
• Android Studio or VS Code, and an Android device/emulator

Setup

# 1. Clone
git clone https://github.com/KpG782/ars.git
cd ars

# 2. Install dependencies
flutter pub get

3. Firebase (bring your own project). This repo does not ship Firebase config — lib/firebase_options.dart and android/app/google-services.json are gitignored. Generate your own:

dart pub global activate flutterfire_cli
flutterfire configure          # creates firebase_options.dart + google-services.json

The app is wrapped so it still launches without a valid Firebase project (UI works), but auth, Firestore, and push require your own project.

4. (Optional) AI chatbot key. Copy .env.example to .env and set your key; without it, the AI diagnostic chat is simply disabled:

cp .env.example .env       # then set ARS_CHATBOT_API_KEY=...

5. (Optional) Google Maps key — set it in android/app/src/main/AndroidManifest.xml (com.google.android.geo.API_KEY) if you use Google Maps tiles; OpenStreetMap tiles work without one.

6. Run

flutter run

Handy commands

flutter analyze        # static analysis (currently clean)
flutter test           # run the test suite
flutter build apk --debug

🤖 AI & API Integrations

The diagnostic intelligence runs in ARS Rapide, a separate FastAPI service this app integrates with over HTTP (X-API-Key).

• ARS Rapide Diagnostic API — RAG-based automotive diagnosis, Taglish support, cost estimation
  • Accuracy: 87.4% diagnostic accuracy · Taglish 97.5% (201 terms)
  • Response time: ~3.7s (cache miss) / <1s (cache hit, Redis)
  • Stack: Gemini 2.0 Flash · LangGraph (agentic state machine) · ChromaDB (vector search) · Redis · Prometheus metrics
• Firebase: Auth (email/password), Cloud Firestore (real-time), Cloud Storage (docs/photos), FCM (push)
• Maps/Location: OpenStreetMap tiles, Google Maps, Geolocator
• OSRM (self-hosted): real-time route + ETA between customer and mechanic, 30s auto-refresh, fallback estimation, Manila road network optimized, no external API cost

Firestore access is enforced by firestore.rules with composite indexes in firestore.indexes.json; see docs/FIREBASE_SCHEMA_REVIEW.md.

🗺️ Roadmap

Done

• Feature-first clean architecture
• Customer + mechanic auth and onboarding
• Service booking with live map
• Self-hosted OSRM ETA (30s refresh, fallback)
• In-app chat + push notifications
• AI diagnostic chatbot integration (Taglish, cost estimation)
• ARS design system (light/dark) + tests
• Riverpod infrastructure + first feature migrated (earnings)
• Firestore security rules + composite indexes

Planned

• Payment integration (GCash / cards)
• Reviews & ratings
• Complete the Riverpod migration across remaining features
• Advanced filters, full Tagalog/English localization, offline mode

👤 Author

Ken Patrick Garcia — @KpG782

⚠️ Actively-developed portfolio project. Firebase config and secrets are intentionally not committed — run flutterfire configure and add your own .env to enable backend features.

📄 License

Released under the MIT License.

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_{Built with Flutter 💙}