Upload app.py

app.py

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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()