feat: Add guest mode authentication and fix dashboard telemetry display

.gitignore

@@ -5,4 +5,6 @@ sketches/secrets.h
 
 .env
 .vscode
-.kiro
+.kiro
+
+stitch/

README.md

@@ -1,109 +1,237 @@
 # 🌱 TerraDetect – A Smart Agriculture Solution
 
 [![Live](https://img.shields.io/badge/Live-Demo-00C853?style=flat-square&logo=vercel&logoColor=white)](https://terradetect.onrender.com)
-[![Built with Flask](https://img.shields.io/badge/Built%20with-Flask-000?style=flat-square&logo=flask)](https://flask.palletsprojects.com/)
-[![Made with Python](https://img.shields.io/badge/Made%20with-Python-FFD43B?style=flat-square&logo=python&logoColor=blue)](https://python.org)
+[![Go Backend](https://img.shields.io/badge/Backend-Go-00ADD8?style=flat-square&logo=go)](https://go.dev)
+[![React Native](https://img.shields.io/badge/Mobile-React%20Native-61DAFB?style=flat-square&logo=react)](https://reactnative.dev)
 [![ESP32](https://img.shields.io/badge/Hardware-ESP32-3C873A?style=flat-square&logo=esphome)](https://www.espressif.com/en/products/socs/esp32)
 
 > 🔗 **Live Demo**: [https://terradetect.onrender.com](https://terradetect.onrender.com)
 
 ---
 
-TerraDetect is a smart farming assistant that combines real-time **soil monitoring**, **sensor-based data collection**, and **ML-powered crop & fertilizer recommendations**. It uses ESP32 microcontrollers to gather environmental data and a Flask web app to display and analyze this information.
+TerraDetect is a comprehensive smart farming platform that combines real-time **soil monitoring**, **IoT sensor integration**, and **ML-powered crop & fertilizer recommendations**. The system features a Go backend with ONNX inference, React Native mobile app with Expo, and ESP32 microcontrollers for environmental data collection.
 
 ---
 
 ## 🔧 Key Features
 
-### 🌐 Web Dashboard (Flask)
-- 📊 Visualize real-time & historical soil data
-- 🌾 Crop and fertilizer prediction using ML
-- 🌓 Dark mode UI with mobile responsiveness
-- 🔒 Secure API endpoints with JWT authentication
+### 📱 Mobile App (React Native + Expo)
+- � Modern Material Design 3 UI with dark mode support
+- 📊 Real-time sensor data visualization with telemetry cards
+- 🌾 ML-powered crop, fertilizer, and suitability predictions
+- 📈 Historical data tracking and analysis
+- � User authentication with JWT tokens
+- 🎭 **Guest Mode** - Try predictions without registration
+- � Offline support with request queuing
+- � Cross-platform (iOS & Android)
+
+### �️ Backend (Go + MongoDB)
+- ⚡ High-performance REST API with Gin framework
+- 🧠 ONNX Runtime for ML inference (crop & fertilizer models)
+- 🔐 JWT authentication with refresh tokens
+- 👥 User and device management
+- 📊 Sensor data storage and retrieval
+- 🎭 Guest token system for anonymous predictions
+- 🔒 Rate limiting and security middleware
+- 📡 CORS support for cross-origin requests
 
 ### 🔌 IoT Integration (ESP32)
-- 📡 WiFiManager for dynamic setup
-- 🔐 User-entered 6-character Device ID (stored in EEPROM)
+- 📡 WiFi connectivity with dynamic configuration
+- 🔐 Device ID authentication (format: AB1234)
 - 📈 HTTPS sensor data uploads with API key validation
-- 🌿 Sensor support:
+- 🌿 Multi-sensor support:
   - Soil pH
   - Moisture
-  - Temperature
+  - Temperature & Humidity
   - EC (Electrical Conductivity)
-  - NPK (via RS485 Modbus)
+  - NPK (Nitrogen, Phosphorus, Potassium)
+
+### 🤖 Machine Learning
+- 🌾 Crop recommendation (23 crop types)
+- 🧪 Fertilizer suggestion (7 fertilizer types)
+- 📊 Crop suitability analysis
+- ⚡ ONNX models for fast inference
+- 🎯 Confidence scores for predictions
 
 ---
 
-## 📁 Folder Structure
+## 📁 Project Structure
 
 ```
 TerraDetect/
-├── esp32/                # ESP32 firmware & sensor integration
-├── static/               # CSS, JS, images
-├── templates/            # Jinja2 HTML templates
-├── models/               # Crop & fertilizer ML models
-├── utils/                # Utility scripts (auth, helpers)
-├── app.py                # Flask backend
-└── requirements.txt      # Python dependencies
+├── backend/              # Go backend server
+│   ├── handlers/         # API route handlers
+│   ├── middleware/       # Auth, rate limiting, CORS
+│   ├── models/           # Data models (User, Device, Sensor)
+│   ├── inference/        # ONNX ML inference engine
+│   ├── db/               # MongoDB connection
+│   └── main.go           # Server entry point
+├── mobile/               # React Native mobile app
+│   ├── app/              # Expo Router pages
+│   │   ├── (app)/        # Authenticated screens
+│   │   └── (auth)/       # Auth screens (login, register, guest)
+│   ├── components/       # Reusable UI components
+│   ├── store/            # Zustand state management
+│   ├── lib/              # API client & utilities
+│   └── assets/           # Images, icons, fonts
+├── ml/                   # Machine learning models
+│   ├── crop-model.onnx   # Crop recommendation model
+│   ├── fertilizer-model.onnx  # Fertilizer suggestion model
+│   └── *.py              # Model training & conversion scripts
+├── sketches/             # ESP32 Arduino firmware
+│   └── esp32_terradetect.ino
+└── docs/                 # API documentation
 ```
 
 ---
 
 ## 🚀 Getting Started
 
-### 🧰 Requirements
-- Python 3.9+
-- Flask, scikit-learn, pandas
-- ESP32-WROOM-32 (30-pin or 38-pin)
-- Arduino IDE / PlatformIO for flashing ESP32
+### 🧰 Prerequisites
+- **Backend**: Go 1.21+, MongoDB 4.4+
+- **Mobile**: Node.js 18+, npm/yarn, Expo CLI
+- **IoT**: ESP32-WROOM-32, Arduino IDE or PlatformIO
+- **ML**: Python 3.9+ (for model training/conversion)
+
+### ⚙️ Backend Setup
+
+```bash
+# Navigate to backend directory
+cd backend
+
+# Install Go dependencies
+go mod download
+
+# Set up environment variables
+cp .env.example .env
+# Edit .env with your MongoDB URI and JWT secret
+
+# Run the server
+go run .
+```
+
+The backend will start on `http://localhost:8080`
 
-### ⚙️ Setup
+### 📱 Mobile App Setup
 
 ```bash
-# Clone the repo
-git clone https://github.com/gagan-ahlawat-0/TerraDetect---A-Smart-Agriculture-Solution.git
-cd TerraDetect---A-Smart-Agriculture-Solution
+# Navigate to mobile directory
+cd mobile
 
 # Install dependencies
-pip install -r requirements.txt
+npm install
+
+# Set up environment variables
+cp .env.example .env
+# Edit .env with your API URL
+
+# Start Expo development server
+npx expo start
 
-# Run the Flask app
-python app.py
+# Scan QR code with Expo Go app (iOS/Android)
+# Or press 'a' for Android emulator, 'i' for iOS simulator
 ```
 
-### 📡 Flashing ESP32
+### 📡 ESP32 Setup
 
-- Open `esp32/` code in Arduino IDE
-- Upload to ESP32 board
-- On first boot:
-  - Connect to ESP32 AP
-  - Enter WiFi credentials + Device ID
+1. Open `sketches/esp32_terradetect.ino` in Arduino IDE
+2. Install required libraries:
+   - WiFiManager
+   - HTTPClient
+   - ArduinoJson
+3. Copy `secrets.h.template` to `secrets.h` and configure:
+   - API endpoint URL
+   - Device ID
+   - API key
+4. Upload to ESP32 board
+5. On first boot, connect to ESP32 AP to configure WiFi
 
----
+### 🤖 ML Model Training (Optional)
+
+```bash
+# Navigate to ml directory
+cd ml
 
-## 🔐 Security
+# Train models (if needed)
+python train_crop_model.py
+python train_fertilizer_model.py
 
-- 🔑 HTTPS requests from ESP32 with API key
-- 🧠 Device identity stored in EEPROM
-- 🔒 JWT tokens for secure API access
+# Convert to ONNX format
+python export_onnx.py
+
+# Validate ONNX models
+python validate_onnx.py
+```
 
 ---
 
-## 📊 ML Prediction Modules
+## 🔐 API Endpoints
+
+### Authentication
+- `POST /api/v1/auth/register` - Register new user
+- `POST /api/v1/auth/login` - User login
+- `POST /api/v1/auth/guest` - Get guest token (30 min)
+- `POST /api/v1/auth/refresh` - Refresh access token
+- `POST /api/v1/auth/logout` - Logout user
+
+### Sensor Data (Authenticated)
+- `GET /api/v1/sensor/latest` - Get latest sensor reading
+- `GET /api/v1/sensor/history` - Get historical data (paginated)
+
+### Predictions (Authenticated)
+- `POST /api/v1/predict/crop` - Crop recommendation
+- `POST /api/v1/predict/fertilizer` - Fertilizer suggestion
+- `POST /api/v1/predict/suitability` - Crop suitability analysis
+
+### Guest Predictions (No Auth Required)
+- `POST /api/v1/guest/predict/crop` - Guest crop prediction
+- `POST /api/v1/guest/predict/fertilizer` - Guest fertilizer prediction
+- `POST /api/v1/guest/predict/suitability` - Guest suitability analysis
+
+### Device Management
+- `POST /api/v1/device/check` - Check device availability
+- `POST /api/esp32` - ESP32 sensor data upload (API key required)
 
-- **Crop Recommendation**  
-  Based on NPK, pH, and other sensor values
+See [docs/api.md](docs/api.md) for detailed API documentation.
 
-- **Fertilizer Suggestion**  
-  Trained on real-world datasets to identify soil deficiencies
+---
+
+## 🔒 Security Features
+
+- 🔑 JWT-based authentication with access & refresh tokens
+- 🎭 Guest tokens with limited permissions (30-minute expiry)
+- 🔐 API key validation for ESP32 devices
+- 🛡️ Rate limiting on all endpoints
+- 🚫 CORS protection with configurable origins
+- 🔒 Password hashing with bcrypt
+- 📊 User-specific data isolation
+- 🧠 Device identity validation (format: AB1234)
 
 ---
 
 ## 🧑‍🌾 Use Cases
 
-- Farmers optimizing fertilizer use
-- Students building Agri-IoT projects
-- Researchers monitoring field soil health
+- 🌾 **Farmers**: Real-time soil monitoring and crop recommendations
+- 🎓 **Students**: Learning platform for Agri-IoT and ML applications
+- 🔬 **Researchers**: Field data collection and analysis
+- 🏢 **Agribusinesses**: Precision agriculture and resource optimization
+- 🌱 **Home Gardeners**: Smart gardening with data-driven insights
+- 👥 **Consultants**: Try predictions as guest without registration
+
+---
+
+## 🎯 Key Improvements in v2.0
+
+- ✅ **Complete rewrite** from Flask to Go for better performance
+- ✅ **Native mobile app** with React Native + Expo
+- ✅ **Guest mode** for trying predictions without registration
+- ✅ **ONNX inference** for faster ML predictions
+- ✅ **Real-time telemetry** dashboard with live sensor data
+- ✅ **Offline support** with request queuing
+- ✅ **Material Design 3** UI with dark mode
+- ✅ **Enhanced security** with JWT refresh tokens
+- ✅ **Better error handling** and user feedback
+- ✅ **Comprehensive API** documentation
 
 ---
 
@@ -115,13 +243,16 @@ python app.py
 
 ## ⚙️ Tech Stack
 
-| Layer         | Tech Used                       |
-|---------------|----------------------------------|
-| Frontend      | HTML, CSS, JS, FontAwesome       |
-| Backend       | Python, Flask, REST APIs         |
-| ML            | Scikit-learn, Pandas, NumPy      |
-| IoT Hardware  | ESP32, RS485 NPK, analog sensors |
-| Auth & Security | JWT, API keys, EEPROM, HTTPS   |
+| Layer            | Technologies                                    |
+|------------------|-------------------------------------------------|
+| **Mobile App**   | React Native, Expo, TypeScript, NativeWind      |
+| **Backend**      | Go, Gin, MongoDB, ONNX Runtime                  |
+| **ML Models**    | Python, Scikit-learn, ONNX                      |
+| **IoT Hardware** | ESP32, RS485 NPK sensor, analog sensors         |
+| **Auth**         | JWT, bcrypt, guest tokens                       |
+| **State Mgmt**   | Zustand (mobile)                                |
+| **Styling**      | TailwindCSS (NativeWind), Material Design 3     |
+| **API**          | REST, JSON, CORS                                |
 
 ---
 

backend/handlers/auth.go

@@ -359,6 +359,27 @@ func (h *AuthHandler) Logout(c *gin.Context) {
 	c.JSON(http.StatusOK, gin.H{"message": "Logged out successfully."})
 }
 
+func (h *AuthHandler) GuestToken(c *gin.Context) {
+	// Generate a guest token valid for 30 minutes
+	guestToken, err := h.generateGuestToken(30 * time.Minute)
+	if err != nil {
+		c.JSON(http.StatusInternalServerError, gin.H{
+			"error": gin.H{
+				"code":    "INTERNAL_ERROR",
+				"message": "Failed to generate guest token.",
+			},
+		})
+		return
+	}
+
+	c.JSON(http.StatusOK, gin.H{
+		"access_token": guestToken,
+		"expires_in":   1800, // 30 minutes in seconds
+		"token_type":   "Bearer",
+		"user_type":    "guest",
+	})
+}
+
 func (h *AuthHandler) generateToken(username, deviceID, tokenType string, duration time.Duration) (string, error) {
 	claims := jwt.MapClaims{
 		"username":   username,
@@ -372,3 +393,17 @@ func (h *AuthHandler) generateToken(username, deviceID, tokenType string, durati
 
 	return token.SignedString([]byte(h.cfg.SecretKey))
 }
+
+func (h *AuthHandler) generateGuestToken(duration time.Duration) (string, error) {
+	claims := jwt.MapClaims{
+		"username":   "guest",
+		"device_id":  "",
+		"token_type": "guest",
+		"exp":        time.Now().Add(duration).Unix(),
+		"iat":        time.Now().Unix(),
+	}
+
+	token := jwt.NewWithClaims(jwt.SigningMethodHS256, claims)
+
+	return token.SignedString([]byte(h.cfg.SecretKey))
+}