ML Part

DATASET

The data used for this model is a custom-made dataset built using python pandas. The dataset used is indian_weather_dataset3.csv, which contains the following columns:

• Temperature (°C)

• Humidity (%)

The sole reason for using such a simple dataset is because of its high interpretability. While using a large real-world dataset can improve the performance of our machine learning model and can help in making better predictions, the crux of this project is the integration of machine learning to Android applications. We the Android club expects the audience to learn the process through this event and bring even more complex ideas to life.

MODEL DEVELOPMENT

This Python script demonstrates the process of building a machine learning model to predict weather conditions based on temperature and humidity data. The model uses a neural network built with TensorFlow and Keras, and is trained on a dataset of Indian weather conditions.

Features

• Data preprocessing with pandas and scikit-learn. • Neural network model creation with TensorFlow. • Model evaluation and predictions.

Dependencies

• Python 3.x • Pandas • NumPy • Matplotlib • TensorFlow • scikit-learn

Neural Network Architecture

The model is built using TensorFlow's Keras API, which allows for an intuitive stack of layers using Sequential. The architecture comprises: Input Layer: It takes the input with the shape corresponding to the number of features (2 in this case: temperature and humidity). Dense Layers: Two hidden layers with 64 and 32 neurons respectively, both using ReLU activation functions. ReLU is chosen for its efficiency and effectiveness in adding non-linearity to the model. Output Layer: The final layer has several neurons equal to the number of unique weather conditions, using a softmax activation function. Softmax makes the output sum up to one so the output can be interpreted as probabilities.

Compilation

The model is compiled with the Adam optimizer and sparse categorical crossentropy as the loss function. Adam is preferred for its adaptive learning rate capabilities, making it suitable for data with varying scales. Sparse categorical crossentropy is used as the loss function because it is efficient with categorical target variables that are represented as integers.

TFLITE CONVERSION

The decision to use TensorFlow Lite for IoT integration is driven by its efficiency and compatibility with edge devices. TensorFlow Lite models are tailored for deployment on resource-constrained platforms, offering optimal performance with minimal computational overhead. This makes it ideal for real-time applications like weather prediction on IoT devices. In simple language, tensorflow-lite models work efficiently in embedded devices like raspberry pi or android phones , without lagging and slowing the process.

We start by importing necessary libraries. • numpy for numerical operations. • load_model from keras.models to load our pre-trained weather prediction model stored in an HDF5 file. • os for operating system functionalities. • tensorflow library itself (tf) to utilize TensorFlow functionalities.

    converter = tf.lite.TFLiteConverter.from_keras_model(model)
    tflite_model = converter.convert()
    INFO:tensorflow: Assets written to: C:\Users\soodv\AppData\Local\Temp\tmp13c6f28x\assets

Here we use the TensorFlow Lite Converter (tf.lite.TFLiteConverter) to convert our loaded Keras model (model) into a TensorFlow Lite compatible format. This step is essential for optimizing the model for deployment on mobile or edge devices.This line actually performs the conversion of our Keras model (model) into a TensorFlow Lite model. Finally, we save the converted TensorFlow Lite model (tflite_model) into a file named Weather_predictor.tflite. The model is written in binary ('wb') mode into this file using the write() method of the file handler (f). This .tflite file can now be deployed and used efficiently on mobile devices or embedded systems. Now you can look for the file in the file explorer and integrate it with the Android studio which would be discussed later.

Android Part

Libraries Used

• TensorFlow Lite: For running machine learning models on Android.
• Retrofit: For making network requests and handling REST API responses
• Gson: For parsing JSON responses into Kotlin data classes
• All other minor dependencies are specified in the build.gradle file.

Flow diagram of the model

UI XML File

How your app looks is specified in the activity_main.xml here

Dependencies

For TensorFlow

    implementation("org.tensorflow:tensorflow-lite-support:0.1.0")
    implementation("org.tensorflow:tensorflow-lite-metadata:0.1.0")
    implementation("org.tensorflow:tensorflow-lite-gpu:2.3.0")

For Retrofit and Gson

    implementation 'com.squareup.retrofit2:retrofit:2.9.0'
    implementation 'com.squareup.retrofit2:converter-gson:2.9.0'
    implementation 'com.google.code.gson:gson:2.8.8'

Import TensorFlow Interpreter

    import org.tensorflow.lite.Interpreter

Weather-Predictor Model access

You can get the model from "asset" section or follow the link -> Tflite file

Import the ml file from the assets directory

    import com.example.demo.ml.WeatherPredictor

Import Retrofit

    import retrofit2.Call
    import retrofit2.Callback
    import retrofit2.Response
    import retrofit2.Retrofit
    import retrofit2.converter.gson.GsonConverterFactory

Code Snippets

SensorData Class

• The SensorData class defines the structure of sensor data fetched from Azure Blob Storage, including temperature, humidity, and IoT Hub details.

data class SensorData(
    val temperature: Double,
    val humidity: Double,
    val EventProcessedUtcTime: String,
    val PartitionId: Int,
    val EventEnqueuedUtcTime: String,
    val IoTHub: IoTHub
)

IoTHubData Class

• The IoTHub class contains IoT Hub-specific information like message and device IDs.

data class IoTHub(
    val MessageId: String?,
    val CorrelationId: String?,
    val ConnectionDeviceId: String,
    val ConnectionDeviceGenerationId: String,
    val EnqueuedTime: String
)

API Service Interface

• This ApiService interface defines a Retrofit API for fetching sensor data from a JSON file hosted on a server.
• It specifies a GET request to retrieve a list of SensorData objects from the specified JSON file.

import retrofit2.Call
import retrofit2.http.GET

interface ApiService {
    @GET("<YOUR_JSON_FILE_NAME>.json") //Json file name
    fun getSensorData(): Call<List<SensorData>>
}

MainActivity

Main onCreate Method

Here, We:

• Initialize our UI Components,
• Create variables for our cloud IOT data,
• We then provide this data to our TFLite file for processing,
• Throughout, we use Exception Handling to make sure we avoid errors.

Retrofit Setup

• The following code initializes Retrofit with a base URL and an access key, creates a service for making API calls, and defines a method for fetching sensor data.

        val baseUrl = "https://<YOUR_ACCOUNT_NAME>.blob.core.windows.net/<YOUR_BLOB_STORAGE_NAME>/"
        val accKey =
            "<YOUR_ACCOUNT_KEY>"

        val retrofit = Retrofit.Builder()
            .baseUrl("$baseUrl?${accKey}")
            .addConverterFactory(GsonConverterFactory.create())
            .build()
        val service = retrofit.create(ApiService::class.java)

Fetch Data fun

• Makes an asynchronous API call using Retrofit's enqueue method.
• Parses the response to extract the latest sensor data (temperature and humidity).
• Returns a FloatArray containing the temperature and humidity values.

    private fun fetchData(service: ApiService) {
        service.getSensorData().enqueue(object : 

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