Introduction

Developing next-generation apps requires a blend of cutting-edge technologies to provide intelligent and efficient user experiences. In this blog post, we'll explore a framework that combines the power of TensorFlow Lite (TFLite) for on-device machine learning with the invaluable input from human feedback. This approach not only enhances the capabilities of your applications but also ensures user-centric development.

As this is just an intro into this ever-growing field this blog will be focused on giving the head start into the world of on-device machine learning.

TensorFlow and Python for Model Development

Model Development

• Utilize the extensive capabilities of TensorFlow, a powerful open-source machine learning framework, for model development.

• Leverage Python, a versatile and widely used programming language, for building and training machine learning models.

Here, we will use the code to train a basic sentiment analysis model, detailed explanation link: Basic text classification | TensorFlow Core

import tensorflow as tf
import os
import shutil
import string
import re 

# Getting the dataset
url = "https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz"
dataset = tf.keras.utils.get_file("aclImdb_v1", url, untar=True, cache_dir='.', cache_subdir='')
dataset_dir = os.path.join(os.path.dirname(dataset), 'aclImdb')

# Removing the sup dir as we don't need that
train_dir = os.path.join(dataset_dir, 'train')
remove_dir = os.path.join(train_dir, 'unsup')
shutil.rmtree(remove_dir)

batch_size = 32
seed = 42

raw_train_ds = tf.keras.utils.text_dataset_from_directory(
    'aclImdb/train', 
    batch_size=batch_size, 
    validation_split=0.2, 
    subset='training', 
    seed=seed)
raw_val_ds = tf.keras.utils.text_dataset_from_directory(
    'aclImdb/train', 
    batch_size=batch_size, 
    validation_split=0.2, 
    subset='validation', 
    seed=seed)
raw_test_ds = tf.keras.utils.text_dataset_from_directory(
    'aclImdb/test', 
    batch_size=batch_size)

# To clean up the i/p data
def custom_standardization(input_data):
  lowercase = tf.strings.lower(input_data)
  stripped_html = tf.strings.regex_replace(lowercase, '<br />', ' ')
  return tf.strings.regex_replace(stripped_html,
                                  '[%s]' % re.escape(string.punctuation),
                                  '')

# Vectorizing the i/p text
max_features = 10000
sequence_length = 250

vectorize_layer = tf.keras.layers.TextVectorization(
    standardize=custom_standardization,
    max_tokens=max_features,
    output_mode='int',
    output_sequence_length=sequence_length)

# Make a text-only dataset (without labels), then call adapt
train_text = raw_train_ds.map(lambda x, y: x)
vectorize_layer.adapt(train_text)

def vectorize_text(text, label):
  text = tf.expand_dims(text, -1)
  return vectorize_layer(text), label

# retrieve a batch (of 32 reviews and labels) from the dataset
text_batch, label_batch = next(iter(raw_train_ds))
first_review, first_label = text_batch[0], label_batch[0]
print("Review", first_review)
print("Label", raw_train_ds.class_names[first_label])
print("Vectorized review", vectorize_text(first_review, first_label))

train_ds = raw_train_ds.map(vectorize_text)
val_ds = raw_val_ds.map(vectorize_text)
test_ds = raw_test_ds.map(vectorize_text)

# To stop I/P Buffering 
AUTOTUNE = tf.data.AUTOTUNE

train_ds = train_ds.cache().prefetch(buffer_size=AUTOTUNE)
val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)
test_ds = test_ds.cache().prefetch(buffer_size=AUTOTUNE)

# Creating the Model
embedding_dim = 16

model = tf.keras.Sequential([
  tf.keras.layers.Embedding(max_features, embedding_dim),
  tf.keras.layers.Dropout(0.2),
  tf.keras.layers.GlobalAveragePooling1D(),
  tf.keras.layers.Dropout(0.2),
  tf.keras.layers.Dense(1)])

model.summary()

model.compile(loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
              optimizer='adam',
              metrics=tf.metrics.BinaryAccuracy(threshold=0.0))

# Training the model
epochs = 50
history = model.fit(
  train_ds,
  validation_data=val_ds,
  epochs=epochs
)

# Evaluating the Model
loss, accuracy = model.evaluate(test_ds)

print("Loss: ", loss)
print("Accuracy: ", accuracy)

export_model = tf.keras.Sequential([
  vectorize_layer,
  model,
  tf.keras.layers.Activation('sigmoid')
])

export_model.compile(
    loss=tf.keras.losses.BinaryCrossentropy(from_logits=False), optimizer="adam", metrics=['accuracy']
)

# Test it with `raw_test_ds`, which yields raw strings
loss, accuracy = export_model.evaluate(raw_test_ds)
print(accuracy)


examples = [
  "The movie was great!",
  "The movie was okay.",
  "The movie was terrible..."
]

export_model.predict(examples)
export_model.save("model.keras", save_format="keras")

TensorFlow Lite

Overview

• TensorFlow Lite is a lightweight machine learning framework developed by Google, specifically designed for mobile and embedded devices.

• It enables developers to deploy models directly on user devices, making inference faster and more efficient.

Integrating TensorFlow model with our application

Creating TensorFlow Lite Model

For that we need to convert our model into a TensorFlow Lite Format. This can be done with the help of this code snippet:

import tensorflow as tf

# Load your pre-trained TensorFlow model
model = tf.keras.models.load_model("model.keras")

# Convert the model to TensorFlow Lite format
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()

# Save the converted model to a file
with open("model.tflite", "wb") as f:
    f.write(tflite_model)

This will create a model.tflite file that we will be using later in our application to inculcate Artificial Intelligence.

Application Integration

Android Integration:

For Android app integration, leverage the power of Kotlin or Java along with Android Studio:

• Load the TensorFlow Lite model in your Android app.

• Implement an efficient inference pipeline using Android's native features.

• Utilize the Android SDK for UI elements and seamless user interaction.

Detailed Explanation Link: TensorFlow Lite for Android

iOS Integration:

For iOS app integration, use Swift or Objective-C along with Xcode:

• Integrate the TensorFlow Lite model into your iOS app.

• Leverage Core ML for efficient on-device machine learning.

• Design a user-friendly interface using iOS SDK elements.

Detailed Explanation Link: iOS quickstart | TensorFlow Lite

Backend Integration with Python | Web Integration:

For backend logic and seamless communication with your app, Python plays a crucial role:

• Develop a Python-based backend to handle communication with the app.

• Implement data processing, storage, and retrieval using Python.

• Utilize Flask or Django for building robust API endpoints.

Detailed Explanation Link: TensorFlow Lite | Python

Human Feedback Integration

1. User Feedback Collection

Implement mechanisms to collect user feedback within your app using Python for backend integration. This could include:

• Ratings and reviews

• In-app surveys

• User analytics data

Utilize tools like Firebase Analytics, Google Analytics, or custom Python-based solutions to gather meaningful insights into user interactions.

2. Continuous Learning and Model Improvement

Leverage human feedback to iteratively improve your TFLite model, adapting to changing user needs and preferences.

Benefit of the Framework

1. On-Device Intelligence:

   • TensorFlow Lite enables on-device machine learning, reducing latency and dependency on network connectivity.

2. User-Centric Development:

   • Human feedback ensures that your app evolves based on real user experiences, leading to increased user satisfaction.

3. Privacy and Security:

   • On-device processing with TFLite enhances user privacy by reducing the need for data to be sent to external servers.

4. Efficient Resource Utilization:

   • TensorFlow Lite models are optimized for mobile devices, ensuring efficient use of device resources.

Real World Applications

Explore real-world applications of this framework in various domains such as:

• Image and object recognition

• Natural language processing

• Gesture recognition

• Personalized recommendations

• and many more.

Conclusion

By combining the capabilities of TensorFlow and Python for model development, TensorFlow Lite for on-device machine learning, and human feedback, your app development process becomes more adaptive and user-focused. This comprehensive framework empowers developers to create intelligent applications that continuously evolve to meet user expectations, setting the stage for the next generation of mobile experiences.