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from random import choices
import numpy as np
import gradio as gr
from glob import glob
import tensorflow as tf
from tensorflow import keras
# Model & Pre-requisites
model_path = './FastFood.keras'
ffc = keras.models.load_model(model_path, compile=False)
class_names_path = './Fast Food-ClassNames.txt'
class_names = []
with open(class_names_path, mode='r') as f:
class_names = f.read().split(',')[:-1]
# Utility Functions
def predict_fast_food(image, labels=class_names, model=ffc):
image = tf.cast(image, tf.float32)
if image.shape[-2]!=224:
image = tf.image.resize(image, (224,224))
if np.max(image)==255:
image = image/255.
if len(image.shape) == 3:
image = tf.squeeze(image)[tf.newaxis, ...]
pred_proba = model.predict(image, verbose=0)[0]
label = tf.argmax(pred_proba, axis=-1)
pred_class = labels[int(label)]
return pred_class, pred_proba[label]
else:
pred_probas = model.predict(image, verbose=0)
labels = tf.argmax(pred_probas, axis=-1)
pred_classes = [class_names[label] for label in labels]
probas = tf.math.reduce_max(pred_probas, axis=-1)
return pred_classes, probas
def load_image(image_path):
image = tf.io.read_file(image_path)
image = tf.image.decode_jpeg(image, channels=3)
image = tf.image.resize(image, (224,224))
image = tf.image.convert_image_dtype(image, tf.float32)
image = image/255.
return image
# Load Example Images
subset_ds_path = './Fast FoodSubset'
# Select 5 images per class
example_image_paths = []
for class_ss_path in glob(subset_ds_path + '/*'):
image_paths = glob(class_ss_path + '/*')
selected_images = choices(image_paths, k=5)
example_image_paths.extend(selected_images)
example_images = [load_image(path).numpy() for path in example_image_paths]
# Define Interface
with gr.Blocks(theme='ocean') as app:
# Title or header (optional)
gr.Markdown("### 🍔 Fast Food Classifier Demo")
# Take Image Input
image_input = gr.Image(label='Image Input')
# Prediction Button
pred_btn = gr.Button('Predict')
# 2 Outputs
with gr.Row():
# Output of the Predicted Class
class_out = gr.Textbox(label='Predicted Class', placeholder='Hmm... Looking for something yummy.')
proba_out = gr.Textbox(label='Predicted Class Probability', placeholder='I believe on myself but numbers don\'t lie.')
# Add example images
gr.Examples(
examples=example_images,
inputs=image_input,
label="Try these example images"
)
def predict_fast_food_wrapper(image):
class_label, proba = predict_fast_food(image)
return class_label, f'{proba:.3%}'
# On Click Action
pred_btn.click(
fn=predict_fast_food_wrapper,
inputs=image_input,
outputs=[class_out, proba_out]
)
if __name__ == '__main__':
# Launch Application
app.launch() |